better error message

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

.gitignore

@@ -8,10 +8,6 @@ __pycache__/
 # C extensions
 *.so
 
-# tests and logs
-tests/fixtures
-logs/
-
 # Distribution / packaging
 .Python
 build/
@@ -120,7 +116,6 @@ dmypy.json
 .pyre/
 
 # vscode
-.vs
 .vscode
 
 # Pycharm

CONTRIBUTING.md

@@ -44,16 +44,9 @@ Did not find it? :( So we can act quickly on it, please follow these steps:
 To get the OS and software versions automatically, you can run the following command:
 
 ```bash
-transformers-cli env
+python transformers-cli env
 ```
 
-or from the root of the repository the following command:
-
-```bash
-python src/transformers/commands/transformers_cli.py env
-```
-
-
 ### Do you want to implement a new model?
 
 Awesome! Please provide the following information:

README.md

@@ -63,7 +63,7 @@ Choose the right framework for every part of a model's lifetime
 
 ## Installation
 
-This repo is tested on Python 3.6+, PyTorch 1.0.0+ (PyTorch 1.3.1+ for examples) and TensorFlow 2.0.
+This repo is tested on Python 3.6+, PyTorch 1.0.0+ and TensorFlow 2.0.
 
 You should install 🤗 Transformers in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
 
@@ -340,8 +340,8 @@ python ./examples/text-classification/run_glue.py \
     --do_eval \
     --data_dir $GLUE_DIR/$TASK_NAME \
     --max_seq_length 128 \
-    --per_device_eval_batch_size=8   \
-    --per_device_train_batch_size=8   \
+    --per_gpu_eval_batch_size=8   \
+    --per_gpu_train_batch_size=8   \
     --learning_rate 2e-5 \
     --num_train_epochs 3.0 \
     --output_dir /tmp/$TASK_NAME/
@@ -367,8 +367,8 @@ python ./examples/text-classification/run_glue.py \
     --data_dir=${GLUE_DIR}/STS-B  \
     --output_dir=./proc_data/sts-b-110   \
     --max_seq_length=128   \
-    --per_device_eval_batch_size=8   \
-    --per_device_train_batch_size=8   \
+    --per_gpu_eval_batch_size=8   \
+    --per_gpu_train_batch_size=8   \
     --gradient_accumulation_steps=1 \
     --max_steps=1200  \
     --model_name=xlnet-large-cased   \
@@ -391,8 +391,8 @@ python -m torch.distributed.launch --nproc_per_node 8 ./examples/text-classifica
     --do_eval   \
     --data_dir $GLUE_DIR/MRPC/   \
     --max_seq_length 128   \
-    --per_device_eval_batch_size=8   \
-    --per_device_train_batch_size=8   \
+    --per_gpu_eval_batch_size=8   \
+    --per_gpu_train_batch_size=8   \
     --learning_rate 2e-5   \
     --num_train_epochs 3.0  \
     --output_dir /tmp/mrpc_output/ \
@@ -428,8 +428,8 @@ python -m torch.distributed.launch --nproc_per_node=8 ./examples/question-answer
     --max_seq_length 384 \
     --doc_stride 128 \
     --output_dir ../models/wwm_uncased_finetuned_squad/ \
-    --per_device_eval_batch_size=3   \
-    --per_device_train_batch_size=3   \
+    --per_gpu_eval_batch_size=3   \
+    --per_gpu_train_batch_size=3   \
 ```
 
 Training with these hyper-parameters gave us the following results:

codecov.yml

@@ -1,6 +0,0 @@
-coverage:
-  status:
-    project:
-      default:
-        informational: true
-    patch: off

docs/README.md

@@ -7,14 +7,6 @@ you can install them with the following command, at the root of the code reposit
 pip install -e ".[docs]"
 ```
 
----
-**NOTE**
-
-You only need to generate the documentation to inspect it locally (if you're planning changes and want to 
-check how they look like before committing for instance). You don't have to commit the built documentation.
-
----
-
 ## Packages installed
 
 Here's an overview of all the packages installed. If you ran the previous command installing all packages from
@@ -76,18 +68,6 @@ It should build the static app that will be available under `/docs/_build/html`
 Accepted files are reStructuredText (.rst) and Markdown (.md). Create a file with its extension and put it
 in the source directory. You can then link it to the toc-tree by putting the filename without the extension.
 
-## Preview the documentation in a pull request
-
-Once you have made your pull request, you can check what the documentation will look like after it's merged by
-following these steps:
-
-- Look at the checks at the bottom of the conversation page of your PR (you may need to click on "show all checks" to
-  expand them).
-- Click on "details" next to the `ci/circleci: build_doc` check.
-- In the new window, click on the "Artifacts" tab.
-- Locate the file "docs/_build/html/index.html" (or any specific page you want to check) and click on it to get a 
-  preview.
-
 ## Writing Documentation - Specification
 
 The `huggingface/transformers` documentation follows the

docs/source/conf.py

@@ -26,7 +26,7 @@ author = u'huggingface'
 # The short X.Y version
 version = u''
 # The full version, including alpha/beta/rc tags
-release = u'2.11.0'
+release = u'2.10.0'
 
 
 # -- General configuration ---------------------------------------------------

docs/source/examples.md

@@ -1 +0,0 @@
-../../examples/README.md

docs/source/examples.md

@@ -0,0 +1,649 @@
+# Examples
+
+In this section a few examples are put together. All of these examples work for several models, making use of the very
+similar API between the different models.
+
+**Important**
+To run the latest versions of the examples, you have to install from source and install some specific requirements for the examples.
+Execute the following steps in a new virtual environment:
+
+```bash
+git clone https://github.com/huggingface/transformers
+cd transformers
+pip install .
+pip install -r ./examples/requirements.txt
+```
+
+| Section                    | Description                                                                                                                                                |
+|----------------------------|------------------------------------------------------------------------------------------------------------------------------------------
+| [TensorFlow 2.0 models on GLUE](#TensorFlow-2.0-Bert-models-on-GLUE) | Examples running BERT TensorFlow 2.0 model on the GLUE tasks. |
+| [Running on TPUs](#running-on-tpus) | Examples on running fine-tuning tasks on Google TPUs to accelerate workloads. |
+| [Language Model training](#language-model-training) | Fine-tuning (or training from scratch) the library models for language modeling on a text dataset. Causal language modeling for GPT/GPT-2, masked language modeling for BERT/RoBERTa. |
+| [Language Generation](#language-generation) | Conditional text generation using the auto-regressive models of the library: GPT, GPT-2, Transformer-XL and XLNet. |
+| [GLUE](#glue) | Examples running BERT/XLM/XLNet/RoBERTa on the 9 GLUE tasks. Examples feature distributed training as well as half-precision. |
+| [SQuAD](#squad) | Using BERT/RoBERTa/XLNet/XLM for question answering, examples with distributed training. |
+| [Multiple Choice](#multiple-choice) | Examples running BERT/XLNet/RoBERTa on the SWAG/RACE/ARC tasks. |
+| [Named Entity Recognition](https://github.com/huggingface/transformers/tree/master/examples/token-classification) | Using BERT for Named Entity Recognition (NER) on the CoNLL 2003 dataset, examples with distributed training. |
+| [XNLI](#xnli) | Examples running BERT/XLM on the XNLI benchmark. |
+| [Adversarial evaluation of model performances](#adversarial-evaluation-of-model-performances) | Testing a model with adversarial evaluation of natural language inference on the Heuristic Analysis for NLI Systems (HANS) dataset (McCoy et al., 2019.) |
+
+## TensorFlow 2.0 Bert models on GLUE
+
+Based on the script [`run_tf_glue.py`](https://github.com/huggingface/transformers/blob/master/examples/text-classification/run_tf_glue.py).
+
+Fine-tuning the library TensorFlow 2.0 Bert model for sequence classification on the  MRPC task of the GLUE benchmark: [General Language Understanding Evaluation](https://gluebenchmark.com/).
+
+This script has an option for mixed precision (Automatic Mixed Precision / AMP) to run models on Tensor Cores (NVIDIA Volta/Turing GPUs) and future hardware and an option for XLA, which uses the XLA compiler to reduce model runtime.
+Options are toggled using `USE_XLA` or `USE_AMP` variables in the script.
+These options and the below benchmark are provided by @tlkh.
+
+Quick benchmarks from the script (no other modifications):
+
+| GPU    | Mode | Time (2nd epoch) | Val Acc (3 runs) |
+| --------- | -------- | ----------------------- | ----------------------|
+| Titan V | FP32 | 41s | 0.8438/0.8281/0.8333 |
+| Titan V | AMP | 26s | 0.8281/0.8568/0.8411 |
+| V100    | FP32 | 35s | 0.8646/0.8359/0.8464 |
+| V100    | AMP | 22s | 0.8646/0.8385/0.8411 |
+| 1080 Ti | FP32 | 55s | - |
+
+Mixed precision (AMP) reduces the training time considerably for the same hardware and hyper-parameters (same batch size was used).
+
+## Running on TPUs
+
+You can accelerate your workloads on Google's TPUs. For information on how to setup your TPU environment refer to this
+[README](https://github.com/pytorch/xla/blob/master/README.md).
+
+The following are some examples of running the `*_tpu.py` finetuning scripts on TPUs. All steps for data preparation are
+identical to your normal GPU + Huggingface setup.
+
+### GLUE
+
+Before running anyone of these GLUE tasks you should download the
+[GLUE data](https://gluebenchmark.com/tasks) by running
+[this script](https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e)
+and unpack it to some directory `$GLUE_DIR`.
+
+For running your GLUE task on MNLI dataset you can run something like the following:
+
+```
+export XRT_TPU_CONFIG="tpu_worker;0;$TPU_IP_ADDRESS:8470"
+export GLUE_DIR=/path/to/glue
+export TASK_NAME=MNLI
+
+python run_glue_tpu.py \
+  --model_type bert \
+  --model_name_or_path bert-base-cased \
+  --task_name $TASK_NAME \
+  --do_train \
+  --do_eval \
+  --data_dir $GLUE_DIR/$TASK_NAME \
+  --max_seq_length 128 \
+  --train_batch_size 32 \
+  --learning_rate 3e-5 \
+  --num_train_epochs 3.0 \
+  --output_dir /tmp/$TASK_NAME \
+  --overwrite_output_dir \
+  --logging_steps 50 \
+  --save_steps 200 \
+  --num_cores=8 \
+  --only_log_master
+```
+
+
+## Language model training
+
+Based on the script [`run_language_modeling.py`](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py).
+
+Fine-tuning (or training from scratch) the library models for language modeling on a text dataset for GPT, GPT-2, BERT and RoBERTa (DistilBERT
+to be added soon). GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa
+are fine-tuned using a masked language modeling (MLM) loss.
+
+Before running the following example, you should get a file that contains text on which the language model will be
+trained or fine-tuned. A good example of such text is the [WikiText-2 dataset](https://blog.einstein.ai/the-wikitext-long-term-dependency-language-modeling-dataset/).
+
+We will refer to two different files: `$TRAIN_FILE`, which contains text for training, and `$TEST_FILE`, which contains
+text that will be used for evaluation.
+
+### GPT-2/GPT and causal language modeling
+
+The following example fine-tunes GPT-2 on WikiText-2. We're using the raw WikiText-2 (no tokens were replaced before
+the tokenization). The loss here is that of causal language modeling.
+
+```bash
+export TRAIN_FILE=/path/to/dataset/wiki.train.raw
+export TEST_FILE=/path/to/dataset/wiki.test.raw
+
+python run_language_modeling.py \
+    --output_dir=output \
+    --model_type=gpt2 \
+    --model_name_or_path=gpt2 \
+    --do_train \
+    --train_data_file=$TRAIN_FILE \
+    --do_eval \
+    --eval_data_file=$TEST_FILE
+```
+
+This takes about half an hour to train on a single K80 GPU and about one minute for the evaluation to run. It reaches
+a score of ~20 perplexity once fine-tuned on the dataset.
+
+### RoBERTa/BERT and masked language modeling
+
+The following example fine-tunes RoBERTa on WikiText-2. Here too, we're using the raw WikiText-2. The loss is different
+as BERT/RoBERTa have a bidirectional mechanism; we're therefore using the same loss that was used during their
+pre-training: masked language modeling.
+
+In accordance to the RoBERTa paper, we use dynamic masking rather than static masking. The model may, therefore, converge
+slightly slower (over-fitting takes more epochs).
+
+We use the `--mlm` flag so that the script may change its loss function.
+
+```bash
+export TRAIN_FILE=/path/to/dataset/wiki.train.raw
+export TEST_FILE=/path/to/dataset/wiki.test.raw
+
+python run_language_modeling.py \
+    --output_dir=output \
+    --model_type=roberta \
+    --model_name_or_path=roberta-base \
+    --do_train \
+    --train_data_file=$TRAIN_FILE \
+    --do_eval \
+    --eval_data_file=$TEST_FILE \
+    --mlm
+```
+
+## Language generation
+
+Based on the script [`run_generation.py`](https://github.com/huggingface/transformers/blob/master/examples/text-generation/run_generation.py).
+
+Conditional text generation using the auto-regressive models of the library: GPT, GPT-2, Transformer-XL, XLNet, CTRL.
+A similar script is used for our official demo [Write With Transfomer](https://transformer.huggingface.co), where you
+can try out the different models available in the library.
+
+Example usage:
+
+```bash
+python run_generation.py \
+    --model_type=gpt2 \
+    --model_name_or_path=gpt2
+```
+
+## GLUE
+
+Based on the script [`run_glue.py`](https://github.com/huggingface/transformers/blob/master/examples/text-classification/run_glue.py).
+
+Fine-tuning the library models for sequence classification on the GLUE benchmark: [General Language Understanding
+Evaluation](https://gluebenchmark.com/). This script can fine-tune the following models: BERT, XLM, XLNet and RoBERTa.
+
+GLUE is made up of a total of 9 different tasks. We get the following results on the dev set of the benchmark with an
+uncased  BERT base model (the checkpoint `bert-base-uncased`). All experiments ran single V100 GPUs with a total train
+batch sizes between 16 and 64. Some of these tasks have a small dataset and training can lead to high variance in the results
+between different runs. We report the median on 5 runs (with different seeds) for each of the metrics.
+
+| Task  | Metric                       | Result      |
+|-------|------------------------------|-------------|
+| CoLA  | Matthew's corr               | 49.23       |
+| SST-2 | Accuracy                     | 91.97       |
+| MRPC  | F1/Accuracy                  | 89.47/85.29 |
+| STS-B | Person/Spearman corr.        | 83.95/83.70 |
+| QQP   | Accuracy/F1                  | 88.40/84.31 |
+| MNLI  | Matched acc./Mismatched acc. | 80.61/81.08 |
+| QNLI  | Accuracy                     | 87.46       |
+| RTE   | Accuracy                     | 61.73       |
+| WNLI  | Accuracy                     | 45.07       |
+
+Some of these results are significantly different from the ones reported on the test set
+of GLUE benchmark on the website. For QQP and WNLI, please refer to [FAQ #12](https://gluebenchmark.com/faq) on the webite.
+
+Before running any one of these GLUE tasks you should download the
+[GLUE data](https://gluebenchmark.com/tasks) by running
+[this script](https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e)
+and unpack it to some directory `$GLUE_DIR`.
+
+```bash
+export GLUE_DIR=/path/to/glue
+export TASK_NAME=MRPC
+
+python run_glue.py \
+  --model_type bert \
+  --model_name_or_path bert-base-cased \
+  --task_name $TASK_NAME \
+  --do_train \
+  --do_eval \
+  --data_dir $GLUE_DIR/$TASK_NAME \
+  --max_seq_length 128 \
+  --per_gpu_train_batch_size 32 \
+  --learning_rate 2e-5 \
+  --num_train_epochs 3.0 \
+  --output_dir /tmp/$TASK_NAME/
+```
+
+where task name can be one of CoLA, SST-2, MRPC, STS-B, QQP, MNLI, QNLI, RTE, WNLI.
+
+The dev set results will be present within the text file `eval_results.txt` in the specified output_dir.
+In case of MNLI, since there are two separate dev sets (matched and mismatched), there will be a separate
+output folder called `/tmp/MNLI-MM/` in addition to `/tmp/MNLI/`.
+
+The code has not been tested with half-precision training with apex on any GLUE task apart from MRPC, MNLI,
+CoLA, SST-2. The following section provides details on how to run half-precision training with MRPC. With that being
+said, there shouldn’t be any issues in running half-precision training with the remaining GLUE tasks as well,
+since the data processor for each task inherits from the base class DataProcessor.
+
+### MRPC
+
+#### Fine-tuning example
+
+The following examples fine-tune BERT on the Microsoft Research Paraphrase Corpus (MRPC) corpus and runs in less
+than 10 minutes on a single K-80 and in 27 seconds (!) on single tesla V100 16GB with apex installed.
+
+Before running any one of these GLUE tasks you should download the
+[GLUE data](https://gluebenchmark.com/tasks) by running
+[this script](https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e)
+and unpack it to some directory `$GLUE_DIR`.
+
+```bash
+export GLUE_DIR=/path/to/glue
+
+python run_glue.py \
+  --model_name_or_path bert-base-cased \
+  --task_name MRPC \
+  --do_train \
+  --do_eval \
+  --data_dir $GLUE_DIR/MRPC/ \
+  --max_seq_length 128 \
+  --per_gpu_train_batch_size 32 \
+  --learning_rate 2e-5 \
+  --num_train_epochs 3.0 \
+  --output_dir /tmp/mrpc_output/
+```
+
+Our test ran on a few seeds with [the original implementation hyper-
+parameters](https://github.com/google-research/bert#sentence-and-sentence-pair-classification-tasks) gave evaluation
+results between 84% and 88%.
+
+#### Using Apex and mixed-precision
+
+Using Apex and 16 bit precision, the fine-tuning on MRPC only takes 27 seconds. First install
+[apex](https://github.com/NVIDIA/apex), then run the following example:
+
+```bash
+export GLUE_DIR=/path/to/glue
+
+python run_glue.py \
+  --model_name_or_path bert-base-cased \
+  --task_name MRPC \
+  --do_train \
+  --do_eval \
+  --data_dir $GLUE_DIR/MRPC/ \
+  --max_seq_length 128 \
+  --per_gpu_train_batch_size 32 \
+  --learning_rate 2e-5 \
+  --num_train_epochs 3.0 \
+  --output_dir /tmp/mrpc_output/ \
+  --fp16
+```
+
+#### Distributed training
+
+Here is an example using distributed training on 8 V100 GPUs. The model used is the BERT whole-word-masking and it
+reaches F1 > 92 on MRPC.
+
+```bash
+export GLUE_DIR=/path/to/glue
+
+python -m torch.distributed.launch \
+    --nproc_per_node 8 run_glue.py \
+    --model_name_or_path bert-base-cased \
+    --task_name MRPC \
+    --do_train \
+    --do_eval \
+    --data_dir $GLUE_DIR/MRPC/ \
+    --max_seq_length 128 \
+    --per_gpu_train_batch_size 8 \
+    --learning_rate 2e-5 \
+    --num_train_epochs 3.0 \
+    --output_dir /tmp/mrpc_output/
+```
+
+Training with these hyper-parameters gave us the following results:
+
+```bash
+acc = 0.8823529411764706
+acc_and_f1 = 0.901702786377709
+eval_loss = 0.3418912578906332
+f1 = 0.9210526315789473
+global_step = 174
+loss = 0.07231863956341798
+```
+
+### MNLI
+
+The following example uses the BERT-large, uncased, whole-word-masking model and fine-tunes it on the MNLI task.
+
+```bash
+export GLUE_DIR=/path/to/glue
+
+python -m torch.distributed.launch \
+    --nproc_per_node 8 run_glue.py \
+    --model_name_or_path bert-base-cased \
+    --task_name mnli \
+    --do_train \
+    --do_eval \
+    --data_dir $GLUE_DIR/MNLI/ \
+    --max_seq_length 128 \
+    --per_gpu_train_batch_size 8 \
+    --learning_rate 2e-5 \
+    --num_train_epochs 3.0 \
+    --output_dir output_dir \
+```
+
+The results  are the following:
+
+```bash
+***** Eval results *****
+  acc = 0.8679706601466992
+  eval_loss = 0.4911287787382479
+  global_step = 18408
+  loss = 0.04755385363816904
+
+***** Eval results *****
+  acc = 0.8747965825874695
+  eval_loss = 0.45516540421714036
+  global_step = 18408
+  loss = 0.04755385363816904
+```
+
+## Multiple Choice
+
+Based on the script [`run_multiple_choice.py`]().
+
+#### Fine-tuning on SWAG
+Download [swag](https://github.com/rowanz/swagaf/tree/master/data) data
+
+```bash
+#training on 4 tesla V100(16GB) GPUS
+export SWAG_DIR=/path/to/swag_data_dir
+python ./examples/multiple-choice/run_multiple_choice.py \
+--task_name swag \
+--model_name_or_path roberta-base \
+--do_train \
+--do_eval \
+--data_dir $SWAG_DIR \
+--learning_rate 5e-5 \
+--num_train_epochs 3 \
+--max_seq_length 80 \
+--output_dir models_bert/swag_base \
+--per_gpu_eval_batch_size=16 \
+--per_gpu_train_batch_size=16 \
+--gradient_accumulation_steps 2 \
+--overwrite_output
+```
+Training with the defined hyper-parameters yields the following results:
+```
+***** Eval results *****
+eval_acc = 0.8338998300509847
+eval_loss = 0.44457291918821606
+```
+
+## SQuAD
+
+Based on the script [`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py).
+
+#### Fine-tuning BERT on SQuAD1.0
+
+This example code fine-tunes BERT on the SQuAD1.0 dataset. It runs in 24 min (with BERT-base) or 68 min (with BERT-large)
+on a single tesla V100 16GB. The data for SQuAD can be downloaded with the following links and should be saved in a
+$SQUAD_DIR directory.
+
+* [train-v1.1.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json)
+* [dev-v1.1.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json)
+* [evaluate-v1.1.py](https://github.com/allenai/bi-att-flow/blob/master/squad/evaluate-v1.1.py)
+
+And for SQuAD2.0, you need to download:
+
+- [train-v2.0.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json)
+- [dev-v2.0.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json)
+- [evaluate-v2.0.py](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/)
+
+```bash
+export SQUAD_DIR=/path/to/SQUAD
+
+python run_squad.py \
+  --model_type bert \
+  --model_name_or_path bert-base-uncased \
+  --do_train \
+  --do_eval \
+  --train_file $SQUAD_DIR/train-v1.1.json \
+  --predict_file $SQUAD_DIR/dev-v1.1.json \
+  --per_gpu_train_batch_size 12 \
+  --learning_rate 3e-5 \
+  --num_train_epochs 2.0 \
+  --max_seq_length 384 \
+  --doc_stride 128 \
+  --output_dir /tmp/debug_squad/
+```
+
+Training with the previously defined hyper-parameters yields the following results:
+
+```bash
+f1 = 88.52
+exact_match = 81.22
+```
+
+#### Distributed training
+
+
+Here is an example using distributed training on 8 V100 GPUs and Bert Whole Word Masking uncased model to reach a F1 > 93 on SQuAD1.1:
+
+```bash
+python -m torch.distributed.launch --nproc_per_node=8 ./examples/question-answering/run_squad.py \
+    --model_type bert \
+    --model_name_or_path bert-large-uncased-whole-word-masking \
+    --do_train \
+    --do_eval \
+    --train_file $SQUAD_DIR/train-v1.1.json \
+    --predict_file $SQUAD_DIR/dev-v1.1.json \
+    --learning_rate 3e-5 \
+    --num_train_epochs 2 \
+    --max_seq_length 384 \
+    --doc_stride 128 \
+    --output_dir ./examples/models/wwm_uncased_finetuned_squad/ \
+    --per_gpu_eval_batch_size=3   \
+    --per_gpu_train_batch_size=3   \
+```
+
+Training with the previously defined hyper-parameters yields the following results:
+
+```bash
+f1 = 93.15
+exact_match = 86.91
+```
+
+This fine-tuned model is available as a checkpoint under the reference
+`bert-large-uncased-whole-word-masking-finetuned-squad`.
+
+#### Fine-tuning XLNet on SQuAD
+
+This example code fine-tunes XLNet on both SQuAD1.0 and SQuAD2.0 dataset. See above to download the data for SQuAD .
+
+##### Command for SQuAD1.0:
+
+```bash
+export SQUAD_DIR=/path/to/SQUAD
+
+python run_squad.py \
+    --model_type xlnet \
+    --model_name_or_path xlnet-large-cased \
+    --do_train \
+    --do_eval \
+    --train_file $SQUAD_DIR/train-v1.1.json \
+    --predict_file $SQUAD_DIR/dev-v1.1.json \
+    --learning_rate 3e-5 \
+    --num_train_epochs 2 \
+    --max_seq_length 384 \
+    --doc_stride 128 \
+    --output_dir ./wwm_cased_finetuned_squad/ \
+    --per_gpu_eval_batch_size=4  \
+    --per_gpu_train_batch_size=4   \
+    --save_steps 5000
+```
+
+##### Command for SQuAD2.0:
+
+```bash
+export SQUAD_DIR=/path/to/SQUAD
+
+python run_squad.py \
+    --model_type xlnet \
+    --model_name_or_path xlnet-large-cased \
+    --do_train \
+    --do_eval \
+    --version_2_with_negative \
+    --train_file $SQUAD_DIR/train-v2.0.json \
+    --predict_file $SQUAD_DIR/dev-v2.0.json \
+    --learning_rate 3e-5 \
+    --num_train_epochs 4 \
+    --max_seq_length 384 \
+    --doc_stride 128 \
+    --output_dir ./wwm_cased_finetuned_squad/ \
+    --per_gpu_eval_batch_size=2  \
+    --per_gpu_train_batch_size=2   \
+    --save_steps 5000
+```
+
+Larger batch size may improve the performance while costing more memory.
+
+##### Results for SQuAD1.0 with the previously defined hyper-parameters:
+
+```python
+{
+"exact": 85.45884578997162,
+"f1": 92.5974600601065,
+"total": 10570,
+"HasAns_exact": 85.45884578997162,
+"HasAns_f1": 92.59746006010651,
+"HasAns_total": 10570
+}
+```
+
+##### Results for SQuAD2.0 with the previously defined hyper-parameters:
+
+```python
+{
+"exact": 80.4177545691906,
+"f1": 84.07154997729623,
+"total": 11873,
+"HasAns_exact": 76.73751686909581,
+"HasAns_f1": 84.05558584352873,
+"HasAns_total": 5928,
+"NoAns_exact": 84.0874684608915,
+"NoAns_f1": 84.0874684608915,
+"NoAns_total": 5945
+}
+```
+
+
+
+
+## XNLI
+
+Based on the script [`run_xnli.py`](https://github.com/huggingface/transformers/blob/master/examples/text-classification/run_xnli.py).
+
+[XNLI](https://www.nyu.edu/projects/bowman/xnli/) is crowd-sourced dataset based on [MultiNLI](http://www.nyu.edu/projects/bowman/multinli/). It is an evaluation benchmark for cross-lingual text representations. Pairs of text are labeled with textual entailment annotations for 15 different languages (including both high-resource language such as English and low-resource languages such as Swahili).
+
+#### Fine-tuning on XNLI
+
+This example code fine-tunes mBERT (multi-lingual BERT) on the XNLI dataset. It runs in 106 mins
+on a single tesla V100 16GB. The data for XNLI can be downloaded with the following links and should be both saved (and un-zipped) in a
+`$XNLI_DIR` directory.
+
+* [XNLI 1.0](https://www.nyu.edu/projects/bowman/xnli/XNLI-1.0.zip)
+* [XNLI-MT 1.0](https://www.nyu.edu/projects/bowman/xnli/XNLI-MT-1.0.zip)
+
+```bash
+export XNLI_DIR=/path/to/XNLI
+
+python run_xnli.py \
+  --model_type bert \
+  --model_name_or_path bert-base-multilingual-cased \
+  --language de \
+  --train_language en \
+  --do_train \
+  --do_eval \
+  --data_dir $XNLI_DIR \
+  --per_gpu_train_batch_size 32 \
+  --learning_rate 5e-5 \
+  --num_train_epochs 2.0 \
+  --max_seq_length 128 \
+  --output_dir /tmp/debug_xnli/ \
+  --save_steps -1
+```
+
+Training with the previously defined hyper-parameters yields the following results on the **test** set:
+
+```bash
+acc = 0.7093812375249501
+```
+
+## MM-IMDb
+
+Based on the script [`run_mmimdb.py`](https://github.com/huggingface/transformers/blob/master/examples/contrib/mm-imdb/run_mmimdb.py).
+
+[MM-IMDb](http://lisi1.unal.edu.co/mmimdb/) is a Multimodal dataset with around 26,000 movies including images, plots and other metadata.
+
+### Training on MM-IMDb
+
+```
+python run_mmimdb.py \
+    --data_dir /path/to/mmimdb/dataset/ \
+    --model_type bert \
+    --model_name_or_path bert-base-uncased \
+    --output_dir /path/to/save/dir/ \
+    --do_train \
+    --do_eval \
+    --max_seq_len 512 \
+    --gradient_accumulation_steps 20 \
+    --num_image_embeds 3 \
+    --num_train_epochs 100 \
+    --patience 5
+```
+
+## Adversarial evaluation of model performances
+
+Here is an example on evaluating a model using adversarial evaluation of natural language inference with the Heuristic Analysis for NLI Systems (HANS) dataset [McCoy et al., 2019](https://arxiv.org/abs/1902.01007). The example was gracefully provided by [Nafise Sadat Moosavi](https://github.com/ns-moosavi).
+
+The HANS dataset can be downloaded from [this location](https://github.com/tommccoy1/hans).
+
+This is an example of using test_hans.py:
+
+```bash
+export HANS_DIR=path-to-hans
+export MODEL_TYPE=type-of-the-model-e.g.-bert-roberta-xlnet-etc
+export MODEL_PATH=path-to-the-model-directory-that-is-trained-on-NLI-e.g.-by-using-run_glue.py
+
+python examples/hans/test_hans.py \
+        --task_name hans \
+        --model_type $MODEL_TYPE \
+        --do_eval \
+        --data_dir $HANS_DIR \
+        --model_name_or_path $MODEL_PATH \
+        --max_seq_length 128 \
+        --output_dir $MODEL_PATH \
+```
+
+This will create the hans_predictions.txt file in MODEL_PATH, which can then be evaluated using hans/evaluate_heur_output.py from the HANS dataset.
+
+The results of the BERT-base model that is trained on MNLI using batch size 8 and the random seed 42 on the HANS dataset is as follows:
+
+```bash
+Heuristic entailed results:
+lexical_overlap: 0.9702
+subsequence: 0.9942
+constituent: 0.9962
+
+Heuristic non-entailed results:
+lexical_overlap: 0.199
+subsequence: 0.0396
+constituent: 0.118
+```

docs/source/index.rst

@@ -60,7 +60,6 @@ The library currently contains PyTorch and Tensorflow implementations, pre-train
     installation
     quickstart
     glossary
-    summary
     pretrained_models
     usage
     model_sharing

docs/source/main_classes/pipelines.rst

@@ -8,7 +8,7 @@ Recognition, Masked Language Modeling, Sentiment Analysis, Feature Extraction an
 There are two categories of pipeline abstractions to be aware about:
 
 - The :class:`~transformers.pipeline` which is the most powerful object encapsulating all other pipelines
-- The other task-specific pipelines, such as :class:`~transformers.TokenClassificationPipeline`
+- The other task-specific pipelines, such as :class:`~transformers.NerPipeline`
   or :class:`~transformers.QuestionAnsweringPipeline`
 
 The pipeline abstraction
@@ -30,15 +30,15 @@ Parent class: Pipeline
 .. autoclass:: transformers.Pipeline
     :members: predict, transform, save_pretrained
 
-TokenClassificationPipeline
-==========================================
-
-.. autoclass:: transformers.TokenClassificationPipeline
-
 NerPipeline
 ==========================================
 
-This class is an alias of the :class:`~transformers.TokenClassificationPipeline` defined above. Please refer to that pipeline for
+.. autoclass:: transformers.NerPipeline
+
+TokenClassificationPipeline
+==========================================
+
+This class is an alias of the :class:`~transformers.NerPipeline` defined above. Please refer to that pipeline for
 documentation and usage examples.
 
 FillMaskPipeline

docs/source/main_classes/tokenizer.rst

@@ -17,14 +17,12 @@ The base classes ``PreTrainedTokenizer`` and ``PreTrainedTokenizerFast`` impleme
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.PreTrainedTokenizer
-    :special-members: __call__
     :members:
 
 ``PreTrainedTokenizerFast``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.PreTrainedTokenizerFast
-    :special-members: __call__
     :members:
 
 ``BatchEncoding``

docs/source/model_doc/albert.rst

@@ -68,20 +68,6 @@ AlbertForSequenceClassification
     :members:
 
 
-AlbertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AlbertForMultipleChoice
-    :members:
-
-
-AlbertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.AlbertForTokenClassification
-    :members:
-
-
 AlbertForQuestionAnswering
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -108,24 +94,3 @@ TFAlbertForSequenceClassification
 
 .. autoclass:: transformers.TFAlbertForSequenceClassification
     :members:
-
-
-TFAlbertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFAlbertForMultipleChoice
-    :members:
-
-
-TFAlbertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFAlbertForTokenClassification
-    :members:
-
-
-TFAlbertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFAlbertForQuestionAnswering
-    :members:

docs/source/model_doc/bart.rst

@@ -4,9 +4,8 @@ Bart
 file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ and assign
 @sshleifer
 
-Overview
-~~~~~~~~~~~~~~~~~~~~~
-
+Paper
+~~~~~
 The Bart model was `proposed <https://arxiv.org/abs/1910.13461>`_ by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019.
 According to the abstract,
 
@@ -17,26 +16,14 @@ According to the abstract,
 The Authors' code can be found `here <https://github.com/pytorch/fairseq/tree/master/examples/bart>`_
 
 
-Implementation Notes:
-
+Implementation Notes
+~~~~~~~~~~~~~~~~~~~~
 - Bart doesn't use :obj:`token_type_ids` for sequence classification. Use BartTokenizer.encode to get the proper splitting.
 - The forward pass of ``BartModel`` will create decoder inputs (using the helper function ``transformers.modeling_bart._prepare_bart_decoder_inputs``)  if they are not passed. This is different than some other modeling APIs.
 - Model predictions are intended to be identical to the original implementation. This only works, however, if the string you pass to ``fairseq.encode`` starts with a space.
 - ``BartForConditionalGeneration.generate`` should be used for conditional generation tasks like summarization, see the example in that docstrings
-- Models that load the ``"facebook/bart-large-cnn"`` weights will not have a ``mask_token_id``, or be able to perform mask filling tasks.
+- Models that load the ``"bart-large-cnn"`` weights will not have a ``mask_token_id``, or be able to perform mask filling tasks.
 
-BartConfig
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BartConfig
-    :members:
-
-
-BartTokenizer
-~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BartTokenizer
-    :members:
 
 
 BartModel
@@ -48,20 +35,6 @@ BartModel
 .. autofunction:: transformers.modeling_bart._prepare_bart_decoder_inputs
 
 
-BartForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BartForSequenceClassification
-    :members: forward
-
-
-BartForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BartForQuestionAnswering
-    :members: forward
-
-
 BartForConditionalGeneration
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -69,3 +42,15 @@ BartForConditionalGeneration
     :members: generate, forward
 
 
+BartForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BartForSequenceClassification
+    :members: forward
+
+BartConfig
+~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BartConfig
+    :members:
+

docs/source/model_doc/camembert.rst

@@ -1,9 +1,6 @@
 CamemBERT
 ----------------------------------------------------
 
-Overview
-~~~~~~~~~~~~~~~~~~~~~
-
 The CamemBERT model was proposed in `CamemBERT: a Tasty French Language Model <https://arxiv.org/abs/1911.03894>`__
 by Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric Villemonte de la
 Clergerie, Djamé Seddah, and Benoît Sagot. It is based on Facebook's RoBERTa model released in 2019. It is a model
@@ -77,13 +74,6 @@ CamembertForTokenClassification
     :members:
 
 
-CamembertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.CamembertForQuestionAnswering
-    :members:
-
-
 TFCamembertModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -110,10 +100,3 @@ TFCamembertForTokenClassification
 
 .. autoclass:: transformers.TFCamembertForTokenClassification
     :members:
-
-
-TFCamembertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFCamembertForQuestionAnswering
-    :members:

docs/source/model_doc/ctrl.rst

@@ -1,9 +1,6 @@
 CTRL
 ----------------------------------------------------
 
-Overview
-~~~~~~~~~~~~~~~~~~~~~
-
 CTRL model was proposed in `CTRL: A Conditional Transformer Language Model for Controllable Generation <https://arxiv.org/abs/1909.05858>`_
 by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
 It's a causal (unidirectional) transformer pre-trained using language modeling on a very large

docs/source/model_doc/distilbert.rst

@@ -1,9 +1,6 @@
 DistilBERT
 ----------------------------------------------------
 
-Overview
-~~~~~~~~~~~~~~~~~~~~~
-
 The DistilBERT model was proposed in the blog post
 `Smaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT <https://medium.com/huggingface/distilbert-8cf3380435b5>`__,
 and the paper `DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter <https://arxiv.org/abs/1910.01108>`__.
@@ -75,20 +72,6 @@ DistilBertForSequenceClassification
     :members:
 
 
-DistilBertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DistilBertForMultipleChoice
-    :members:
-
-
-DistilBertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.DistilBertForTokenClassification
-    :members:
-
-
 DistilBertForQuestionAnswering
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -116,22 +99,6 @@ TFDistilBertForSequenceClassification
     :members:
 
 
-
-TFDistilBertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFDistilBertForMultipleChoice
-    :members:
-
-
-
-TFDistilBertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFDistilBertForTokenClassification
-    :members:
-
-
 TFDistilBertForQuestionAnswering
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/electra.rst

@@ -1,9 +1,6 @@
 ELECTRA
 ----------------------------------------------------
 
-Overview
-~~~~~~~~~~~~~~~~~~~~~
-
 The ELECTRA model was proposed in the paper.
 `ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators <https://openreview.net/pdf?id=r1xMH1BtvB>`__.
 ELECTRA is a new pre-training approach which trains two transformer models: the generator and the discriminator. The
@@ -92,13 +89,6 @@ ElectraForMaskedLM
     :members:
 
 
-ElectraForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ElectraForSequenceClassification
-    :members:
-
-
 ElectraForTokenClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -106,13 +96,6 @@ ElectraForTokenClassification
     :members:
 
 
-ElectraForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ElectraForQuestionAnswering
-    :members:
-
-
 TFElectraModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -139,10 +122,3 @@ TFElectraForTokenClassification
 
 .. autoclass:: transformers.TFElectraForTokenClassification
     :members:
-
-
-TFElectraForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFElectraForQuestionAnswering
-    :members:

docs/source/model_doc/flaubert.rst

@@ -1,9 +1,6 @@
 FlauBERT
 ----------------------------------------------------
 
-Overview
-~~~~~~~~~~~~~~~~~~~~~
-
 The FlauBERT model was proposed in the paper
 `FlauBERT: Unsupervised Language Model Pre-training for French <https://arxiv.org/abs/1912.05372>`__ by Hang Le et al.
 It's a transformer pre-trained using a masked language modeling (MLM) objective (BERT-like).
@@ -75,43 +72,3 @@ FlaubertForQuestionAnswering
     :members:
 
 
-TFFlaubertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFFlaubertModel
-    :members:
-
-
-TFFlaubertWithLMHeadModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFFlaubertWithLMHeadModel
-    :members:
-
-
-TFFlaubertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFFlaubertForSequenceClassification
-    :members:
-
-
-TFFlaubertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFFlaubertForMultipleChoice
-    :members:
-
-
-TFFlaubertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFFlaubertForTokenClassification
-    :members:
-
-
-TFFlaubertForQuestionAnsweringSimple
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFFlaubertForQuestionAnsweringSimple
-    :members:

docs/source/model_doc/longformer.rst

@@ -4,7 +4,7 @@ Longformer
 file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`_
 
 Overview
-~~~~~~~~~
+~~~~~
 The Longformer model was presented in `Longformer: The Long-Document Transformer <https://arxiv.org/pdf/2004.05150.pdf>`_ by Iz Beltagy, Matthew E. Peters, Arman Cohan.
 Here the abstract: 
 
@@ -13,7 +13,7 @@ Here the abstract:
 The Authors' code can be found `here <https://github.com/allenai/longformer>`_ .
 
 Longformer Self Attention
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~
 Longformer self attention employs self attention on both a "local" context and a "global" context.
 Most tokens only attend "locally" to each other meaning that each token attends to its :math:`\frac{1}{2} w` previous tokens and :math:`\frac{1}{2} w` succeding tokens with :math:`w` being the window length as defined in `config.attention_window`. Note that `config.attention_window` can be of type ``list`` to define a different :math:`w` for each layer. 
 A selecetd few tokens attend "globally" to all other tokens, as it is conventionally done for all tokens in *e.g.* `BertSelfAttention`.
@@ -21,7 +21,7 @@ A selecetd few tokens attend "globally" to all other tokens, as it is convention
 Note that "locally" and "globally" attending tokens are projected by different query, key and value matrices.
 Also note that every "locally" attending token not only attends to tokens within its window :math:`w`, but also to all "globally" attending tokens so that global attention is *symmetric*.
 
-The user can define which tokens attend "locally" and which tokens attend "globally" by setting the tensor `global_attention_mask` at run-time appropriately. `Longformer` employs the following logic for `global_attention_mask`: `0` - the token attends "locally", `1` - token attends "globally". For more information please also refer to :func:`~transformers.LongformerModel.forward` method.
+The user can define which tokens are masked, which tokens attend "locally" and which tokens attend "globally" by setting the `config.attention_mask` `torch.Tensor` appropriately. In contrast to other models `Longformer` accepts the following values in `config.attention_mask`: `0` - the token is masked and not attended at all (as is done in other models), `1` - the token attends "locally", `2` - token attends "globally". For more information please also refer to :func:`~transformers.LongformerModel.forward` method.
 
 Using Longformer self attention, the memory and time complexity of the query-key matmul operation, which usually represents the memory and time bottleneck, can be reduced from :math:`\mathcal{O}(n_s \times n_s)` to :math:`\mathcal{O}(n_s \times w)`, with :math:`n_s` being the sequence length and :math:`w` being the average window size. It is assumed that the number of "globally" attending tokens is insignificant as compared to the number of "locally" attending tokens.
 
@@ -55,13 +55,6 @@ LongformerTokenizer
     :members: 
 
 
-LongformerTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.LongformerTokenizerFast
-    :members: 
-
-
 LongformerModel
 ~~~~~~~~~~~~~~~~~~~~
 
@@ -76,27 +69,6 @@ LongformerForMaskedLM
     :members:
 
 
-LongformerForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.LongformerForSequenceClassification
-    :members:
-
-
-LongformerForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.LongformerForMultipleChoice
-    :members:
-
-
-LongformerForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.LongformerForTokenClassification
-    :members:
-
-
 LongformerForQuestionAnswering
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/marian.rst

@@ -6,11 +6,11 @@ file a `Github Issue <https://github.com/huggingface/transformers/issues/new?ass
 
 Implementation Notes
 ~~~~~~~~~~~~~~~~~~~~
-- Each model is about 298 MB on disk, there are 1,000+ models.
+- each model is about 298 MB on disk, there are 1,000+ models.
 - The list of supported language pairs can be found `here <https://huggingface.co/Helsinki-NLP>`__.
 - The 1,000+ models were originally trained by `Jörg Tiedemann <https://researchportal.helsinki.fi/en/persons/j%C3%B6rg-tiedemann>`__ using the `Marian <https://marian-nmt.github.io/>`_ C++ library, which supports fast training and translation.
 - All models are transformer encoder-decoders with 6 layers in each component. Each model's performance is documented in a model card.
-- The 80 opus models that require BPE preprocessing are not supported.
+- the 80 opus models that require BPE preprocessing are not supported.
 - The modeling code is the same as ``BartForConditionalGeneration`` with a few minor modifications:
     - static (sinusoid) positional embeddings (``MarianConfig.static_position_embeddings=True``)
     - a new final_logits_bias (``MarianConfig.add_bias_logits=True``)
@@ -86,19 +86,6 @@ Code to see available pretrained models:
     suffix = [x.split('/')[1] for x in model_ids]
     multi_models = [f'{org}/{s}' for s in suffix if s != s.lower()]
 
-MarianConfig
-~~~~~~~~~~~~~~~~~~~
-.. autoclass:: transformers.MarianConfig
-    :members:
-
-
-MarianTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MarianTokenizer
-    :members: prepare_translation_batch
-
-
 MarianMTModel
 ~~~~~~~~~~~~~
 
@@ -109,3 +96,10 @@ This class inherits all functionality from ``BartForConditionalGeneration``, see
 
 .. autoclass:: transformers.MarianMTModel
     :members:
+
+
+MarianTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.MarianTokenizer
+    :members: prepare_translation_batch

docs/source/model_doc/roberta.rst

@@ -1,9 +1,6 @@
 RoBERTa
 ----------------------------------------------------
 
-Overview
-~~~~~~~~~~~~~~~~~~~~~
-
 The RoBERTa model was proposed in `RoBERTa: A Robustly Optimized BERT Pretraining Approach <https://arxiv.org/abs/1907.11692>`_
 by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer,
 Veselin Stoyanov. It is based on Google's BERT model released in 2018.
@@ -77,27 +74,12 @@ RobertaForSequenceClassification
     :members:
 
 
-RobertaForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.RobertaForMultipleChoice
-    :members:
-
-
 RobertaForTokenClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.RobertaForTokenClassification
     :members:
 
-
-RobertaForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.RobertaForQuestionAnswering
-    :members:
-
-
 TFRobertaModel
 ~~~~~~~~~~~~~~~~~~~~
 
@@ -119,22 +101,8 @@ TFRobertaForSequenceClassification
     :members:
 
 
-TFRobertaForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFRobertaForMultipleChoice
-    :members:
-
-
 TFRobertaForTokenClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFRobertaForTokenClassification
     :members:
-
-
-TFRobertaForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFRobertaForQuestionAnswering
-    :members:

docs/source/model_doc/t5.rst

@@ -4,8 +4,7 @@ T5
 file a `Github Issue <https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`_
 
 Overview
-~~~~~~~~~~~~~~~~~~~~~
-
+~~~~~
 The T5 model was presented in `Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer <https://arxiv.org/pdf/1910.10683.pdf>`_ by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu in 
 Here the abstract: 
 
@@ -15,20 +14,10 @@ Our systematic study compares pre-training objectives, architectures, unlabeled
 By combining the insights from our exploration with scale and our new "Colossal Clean Crawled Corpus", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. 
 To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.*
 
-Tips:
-
-- T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised 
-  and supervised tasks and for which each task is converted into a text-to-text format.
-  T5 works well on a variety of tasks out-of-the-box by prepending a different prefix to the input corresponding to each task, e.g.: for translation: *translate English to German: ..., summarize: ...*.
-  For more information about which prefix to use, it is easiest to look into Appendix D of the `paper <https://arxiv.org/pdf/1910.10683.pdf>`_ .
-- For sequence to sequence generation, it is recommended to use ``T5ForConditionalGeneration.generate()``. The method takes care of feeding the encoded input via cross-attention layers to the decoder and auto-regressively generates the decoder output.
-- T5 uses relative scalar embeddings. Encoder input padding can be done on the left and on the right.
-
-The original code can be found `here <https://github.com/google-research/text-to-text-transfer-transformer>`_.
+The Authors' code can be found `here <https://github.com/google-research/text-to-text-transfer-transformer>`_ .
 
 Training
-~~~~~~~~~~~~~~~~~~~~~
-
+~~~~~~~~~~~~~~~~~~~~
 T5 is an encoder-decoder model and converts all NLP problems into a text-to-text format. It is trained using teacher forcing.
 This means that for training we always need an input sequence and a target sequence. 
 The input sequence is fed to the model using ``input_ids``. The target sequence is shifted to the right, *i.e.* prepended by a start-sequence token and fed to the decoder using the `decoder_input_ids`. In teacher-forcing style, the target sequence is then appended by the EOS token and corresponds to the ``lm_labels``. The PAD token is hereby used as the start-sequence token.
@@ -61,6 +50,17 @@ T5 can be trained / fine-tuned both in a supervised and unsupervised fashion.
   # the forward function automatically creates the correct decoder_input_ids
   model(input_ids=input_ids, lm_labels=lm_labels)
 
+Tips
+~~~~~~~~~~~~~~~~~~~~
+- T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised 
+  and supervised tasks and for which each task is converted into a text-to-text format.
+  T5 works well on a variety of tasks out-of-the-box by prepending a different prefix to the input corresponding to each task, e.g.: for translation: *translate English to German: ..., summarize: ...*.
+  For more information about which prefix to use, it is easiest to look into Appendix D of the `paper <https://arxiv.org/pdf/1910.10683.pdf>`_ .
+- For sequence to sequence generation, it is recommended to use ``T5ForConditionalGeneration.generate()``. The method takes care of feeding the encoded input via cross-attention layers to the decoder and auto-regressively generates the decoder output.
+- T5 uses relative scalar embeddings. Encoder input padding can be done on the left and on the right.
+
+The original code can be found `here <https://github.com/google-research/text-to-text-transfer-transformer>`_.
+
 
 T5Config
 ~~~~~~~~~~~~~~~~~~~~~
@@ -99,7 +99,7 @@ TFT5Model
 
 
 TFT5ForConditionalGeneration
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFT5ForConditionalGeneration
     :members:

docs/source/model_doc/xlm.rst

@@ -102,21 +102,6 @@ TFXLMForSequenceClassification
     :members:
 
 
-TFXLMForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFXLMForMultipleChoice
-    :members:
-
-
-TFXLMForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFXLMForTokenClassification
-    :members:
-
-
-
 TFXLMForQuestionAnsweringSimple
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/model_doc/xlmroberta.rst

@@ -1,9 +1,6 @@
 XLM-RoBERTa
 ------------------------------------------
 
-Overview
-~~~~~~~~~~~~~~~~~~~~~
-
 The XLM-RoBERTa model was proposed in `Unsupervised Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`__
 by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán,
 Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's RoBERTa model released in 2019.
@@ -84,13 +81,6 @@ XLMRobertaForTokenClassification
     :members:
 
 
-XLMRobertaForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLMRobertaForQuestionAnswering
-    :members:
-
-
 TFXLMRobertaModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -112,22 +102,8 @@ TFXLMRobertaForSequenceClassification
     :members:
 
 
-TFXLMRobertaForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFXLMRobertaForMultipleChoice
-    :members:
-
-
 TFXLMRobertaForTokenClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.TFXLMRobertaForTokenClassification
     :members:
-
-
-TFXLMRobertaForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFXLMRobertaForQuestionAnswering
-    :members:

docs/source/model_doc/xlnet.rst

@@ -71,13 +71,6 @@ XLNetForSequenceClassification
     :members:
 
 
-XLNetForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.XLNetForMultipleChoice
-    :members:
-
-
 XLNetForTokenClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -85,6 +78,13 @@ XLNetForTokenClassification
     :members:
 
 
+XLNetForMultipleChoice
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.XLNetForMultipleChoice
+    :members:
+
+
 XLNetForQuestionAnsweringSimple
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -120,20 +120,6 @@ TFXLNetForSequenceClassification
     :members:
 
 
-TFLNetForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFXLNetForMultipleChoice
-    :members:
-
-
-TFXLNetForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFXLNetForTokenClassification
-    :members:
-
-
 TFXLNetForQuestionAnsweringSimple
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/source/pretrained_models.rst

@@ -63,33 +63,33 @@ For a list that includes community-uploaded models, refer to `https://huggingfac
 |                   |                                                            | | Trained on uncased German text by DBMDZ                                                                                             |
 |                   |                                                            | (see `details on dbmdz repository <https://github.com/dbmdz/german-bert>`__).                                                         |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``cl-tohoku/bert-base-japanese``                           | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                   | ``bert-base-japanese``                                     | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | Trained on Japanese text. Text is tokenized with MeCab and WordPiece.                                                               |
 |                   |                                                            | | `MeCab <https://taku910.github.io/mecab/>`__ is required for tokenization.                                                          |
 |                   |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``cl-tohoku/bert-base-japanese-whole-word-masking``        | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                   | ``bert-base-japanese-whole-word-masking``                  | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | Trained on Japanese text using Whole-Word-Masking. Text is tokenized with MeCab and WordPiece.                                      |
 |                   |                                                            | | `MeCab <https://taku910.github.io/mecab/>`__ is required for tokenization.                                                          |
 |                   |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``cl-tohoku/bert-base-japanese-char``                      | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                   | ``bert-base-japanese-char``                                | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | Trained on Japanese text. Text is tokenized into characters.                                                                        |
 |                   |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``cl-tohoku/bert-base-japanese-char-whole-word-masking``   | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                   | ``bert-base-japanese-char-whole-word-masking``             | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | Trained on Japanese text using Whole-Word-Masking. Text is tokenized into characters.                                               |
 |                   |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``TurkuNLP/bert-base-finnish-cased-v1``                    | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                   | ``bert-base-finnish-cased-v1``                             | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | Trained on cased Finnish text.                                                                                                      |
 |                   |                                                            | (see `details on turkunlp.org <http://turkunlp.org/FinBERT/>`__).                                                                     |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``TurkuNLP/bert-base-finnish-uncased-v1``                  | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                   | ``bert-base-finnish-uncased-v1``                           | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | Trained on uncased Finnish text.                                                                                                    |
 |                   |                                                            | (see `details on turkunlp.org <http://turkunlp.org/FinBERT/>`__).                                                                     |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``wietsedv/bert-base-dutch-cased``                         | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
+|                   | ``bert-base-dutch-cased``                                  | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
 |                   |                                                            | | Trained on cased Dutch text.                                                                                                        |
 |                   |                                                            | (see `details on wietsedv repository <https://github.com/wietsedv/bertje/>`__).                                                       |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
@@ -259,34 +259,32 @@ For a list that includes community-uploaded models, refer to `https://huggingfac
 |                   | ``xlm-roberta-large``                                      | | ~355M parameters with 24-layers, 1027-hidden-state, 4096 feed-forward hidden-state, 16-heads,                                       |
 |                   |                                                            | | Trained on 2.5 TB of newly created clean CommonCrawl data in 100 languages                                                          |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| FlauBERT          | ``flaubert/flaubert_small_cased``                          | | 6-layer, 512-hidden, 8-heads, 54M parameters                                                                                        |
+| FlauBERT          | ``flaubert-small-cased``                                   | | 6-layer, 512-hidden, 8-heads, 54M parameters                                                                                        |
 |                   |                                                            | | FlauBERT small architecture                                                                                                         |
 |                   |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``flaubert/flaubert_base_uncased``                         | | 12-layer, 768-hidden, 12-heads, 137M parameters                                                                                     |
+|                   | ``flaubert-base-uncased``                                  | | 12-layer, 768-hidden, 12-heads, 137M parameters                                                                                     |
 |                   |                                                            | | FlauBERT base architecture with uncased vocabulary                                                                                  |
 |                   |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``flaubert/flaubert_base_cased``                           | | 12-layer, 768-hidden, 12-heads, 138M parameters                                                                                     |
+|                   | ``flaubert-base-cased``                                    | | 12-layer, 768-hidden, 12-heads, 138M parameters                                                                                     |
 |                   |                                                            | | FlauBERT base architecture with cased vocabulary                                                                                    |
 |                   |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``flaubert/flaubert_large_cased``                          | | 24-layer, 1024-hidden, 16-heads, 373M parameters                                                                                    |
+|                   | ``flaubert-large-cased``                                   | | 24-layer, 1024-hidden, 16-heads, 373M parameters                                                                                    |
 |                   |                                                            | | FlauBERT large architecture                                                                                                         |
 |                   |                                                            | (see `details <https://github.com/getalp/Flaubert>`__)                                                                                |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| Bart              | ``facebook/bart-large``                                    | | 24-layer, 1024-hidden, 16-heads, 406M parameters                                                                                    |
+| Bart              | ``bart-large``                                             | | 24-layer, 1024-hidden, 16-heads, 406M parameters                                                                                    |
 |                   |                                                            | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/bart>`_)                                                       |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``facebook/bart-base``                                     | | 12-layer, 768-hidden, 16-heads, 139M parameters                                                                                     |
-|                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``facebook/bart-large-mnli``                               | | Adds a 2 layer classification head with 1 million parameters                                                                        |
+|                   | ``bart-large-mnli``                                        | | Adds a 2 layer classification head with 1 million parameters                                                                        |
 |                   |                                                            | | bart-large base architecture with a classification head, finetuned on MNLI                                                          |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``facebook/bart-large-cnn``                                | | 12-layer, 1024-hidden, 16-heads, 406M parameters       (same as base)                                                               |
+|                   | ``bart-large-cnn``                                         | | 12-layer, 1024-hidden, 16-heads, 406M parameters       (same as base)                                                               |
 |                   |                                                            | | bart-large base architecture finetuned on cnn summarization task                                                                    |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``facebook/mbart-large-en-ro``                             | | 12-layer, 1024-hidden, 16-heads, 880M parameters                                                                                    |
+|                   | ``mbart-large-en-ro``                                      | | 12-layer, 1024-hidden, 16-heads, 880M parameters                                                                                    |
 |                   |                                                            | | bart-large architecture pretrained on cc25 multilingual data , finetuned on WMT english romanian translation.                       |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | DialoGPT          | ``DialoGPT-small``                                         | | 12-layer, 768-hidden, 12-heads, 124M parameters                                                                                     |
@@ -307,9 +305,9 @@ For a list that includes community-uploaded models, refer to `https://huggingfac
 | MarianMT          | ``Helsinki-NLP/opus-mt-{src}-{tgt}``                       | | 12-layer, 512-hidden, 8-heads, ~74M parameter Machine translation models. Parameter counts vary depending on vocab size.            |
 |                   |                                                            | | (see `model list <https://huggingface.co/Helsinki-NLP>`_)                                                                           |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| Longformer        | ``allenai/longformer-base-4096``                           | | 12-layer, 768-hidden, 12-heads, ~149M parameters                                                                                    |
+| Longformer        | ``longformer-base-4096``                                   | | 12-layer, 768-hidden, 12-heads, ~149M parameters                                                                                    |
 |                   |                                                            | | Starting from RoBERTa-base checkpoint, trained on documents of max length 4,096                                                     |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                   | ``allenai/longformer-large-4096``                          | | 24-layer, 1024-hidden, 16-heads, ~435M parameters                                                                                   |
+|                   | ``longformer-large-4096``                                  | | 24-layer, 1024-hidden, 16-heads, ~435M parameters                                                                                   |
 |                   |                                                            | | Starting from RoBERTa-large checkpoint, trained on documents of max length 4,096                                                    |
 +-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+

docs/source/summary.rst

@@ -1,618 +0,0 @@
-Summary of the models
-================================================
-
-This is a summary of the models available in the transformers library. It assumes you’re familiar with the original 
-`transformer model <https://arxiv.org/abs/1706.03762>`_. For a gentle introduction check the `annotated transformer 
-<http://nlp.seas.harvard.edu/2018/04/03/attention.html>`_. Here we focus on the high-level differences between the
-models. You can check them more in detail in their respective documentation. Also checkout the 
-:doc:`pretrained model page </pretrained_models>` to see the checkpoints available for each type of model and all `the 
-community models <https://huggingface.co/models>`_.
-
-Each one of the models in the library falls into one of the following categories:
-
-  * :ref:`autoregressive-models`
-  * :ref:`autoencoding-models`
-  * :ref:`seq-to-seq-models`
-  * :ref:`multimodal-models`
-
-Autoregressive models are pretrained on the classic language modeling task: guess the next token having read all the 
-previous ones. They correspond to the decoder of the original transformer model, and a mask is used on top of the full 
-sentence so that the attention heads can only see what was before in the next, and not what’s after. Although those 
-models can be fine-tuned and achieve great results on many tasks, the most natural application is text generation. 
-A typical example of such models is GPT.
-
-Autoencoding models are pretrained by corrupting the input tokens in some way and trying to reconstruct the original 
-sentence. They correspond to the encoder of the original transformer model in the sense that they get access to the 
-full inputs without any mask. Those models usually build a bidirectional representation of the whole sentence. They can 
-be fine-tuned and achieve great results on many tasks such as text generation, but their most natural application is 
-sentence classification or token classification. A typical example of such models is BERT.
-
-Note that the only difference between autoregressive models and autoencoding models is in the way the model is 
-pretrained. Therefore, the same architecture can be used for both autoregressive and autoencoding models. When a given
-model has been used for both pretraining, we have put it in the category corresponding to the article it was first
-introduced.
-
-Sequence-to-sequence models use both the encoder and the decoder of the original transformer, either for translation 
-tasks or by transforming other tasks to sequence-to-sequence problems. They can be fine-tuned to many tasks but their 
-most natural applications are translation, summarization and question answering. The original transformer model is an 
-example of such a model (only for translation), T5 is an example that can be fine-tuned on other tasks.
-
-Multimodal models mix text inputs with other kinds (like image) and are more specific to a given task.
-
-.. _autoregressive-models:
-
-Autoregressive models
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-As mentioned before, these models rely on the decoder part of the original transformer and use an attention mask so 
-that at each position, the model can only look at the tokens before in the attention heads.
-
-Original GPT
-----------------------------------------------
-
-.. raw:: html
-
-   <a href="https://huggingface.co/models?filter=openai-gpt">
-       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-openai--gpt-blueviolet">
-   </a>
-   <a href="/model_doc/gpt">
-       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-openai--gpt-blueviolet">
-   </a>
-
-`Improving Language Understanding by Generative Pre-Training <https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf>`_, 
-Alec Radford et al.
-
-The first autoregressive model based on the transformer architecture, pretrained on the Book Corpus dataset.
-
-The library provides versions of the model for language modeling and multitask language modeling/multiple choice 
-classification.
-
-GPT-2
-----------------------------------------------
-
-.. raw:: html
-
-   <a href="https://huggingface.co/models?filter=gpt2">
-       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-gpt2-blueviolet">
-   </a>
-   <a href="/model_doc/gpt2">
-       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-gpt2-blueviolet">
-   </a>
-
-`Language Models are Unsupervised Multitask Learners <https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf>`_, 
-Alec Radford et al.
-
-A bigger and better version of GPT, pretrained on WebText (web pages from outgoing links in Reddit with 3 karmas or 
-more).
-
-The library provides versions of the model for language modeling and multitask language modeling/multiple choice 
-classification.
-
-CTRL
-----------------------------------------------
-
-.. raw:: html
-
-   <a href="https://huggingface.co/models?filter=ctrl">
-       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-ctrl-blueviolet">
-   </a>
-   <a href="/model_doc/ctrl">
-       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-ctrl-blueviolet">
-   </a>
-
-`CTRL: A Conditional Transformer Language Model for Controllable Generation <https://arxiv.org/abs/1909.05858>`_, 
-Nitish Shirish Keskar et al.
-
-Same as the GPT model but adds the idea of control codes. Text is generated from a prompt (can be empty) and one (or 
-several) of those control codes which are then used to influence the text generation: generate with the style of 
-wikipedia article, a book or a movie review.
-
-The library provides a version of the model for language modeling only.
-
-Transformer-XL
-----------------------------------------------
-
-.. raw:: html
-
-   <a href="https://huggingface.co/models?filter=transfo-xl">
-       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-transfo--xl-blueviolet">
-   </a>
-   <a href="/model_doc/transformerxl">
-       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-transfo--xl-blueviolet">
-   </a>
-
-`Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`_, 
-Zihang Dai et al.
-
-Same as a regular GPT model, but introduces a recurrence mechanism for two consecutive segments (similar to a regular 
-RNNs with two consecutive inputs). In this context, a segment is a number of consecutive tokens (for instance 512) that 
-may span across multiple documents, and segments are fed in order to the model.
-
-Basically, the hidden states of the previous segment are concatenated to the current input to compute the attention 
-scores. This allows the model to pay attention to information that was in the previous segment as well as the current 
-one. By stacking multiple attention layers, the receptive field can be increased to multiple previous segments.
-
-This changes the positional embeddings to positional relative embeddings (as the regular positional embeddings would 
-give the same results in the current input and the current hidden state at a given position) and needs to make some 
-adjustments in the way attention scores are computed.
-
-The library provides a version of the model for language modeling only.
-
-.. _reformer:
-
-Reformer
-----------------------------------------------
-
-.. raw:: html
-
-   <a href="https://huggingface.co/models?filter=reformer">
-       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-reformer-blueviolet">
-   </a>
-   <a href="/model_doc/reformer">
-       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-reformer-blueviolet">
-   </a>
-
-`Reformer: The Efficient Transformer <https://arxiv.org/abs/2001.04451>`_,
-Nikita Kitaev et al .
-
-An autoregressive transformer model with lots of tricks to reduce memory footprint and compute time. Those tricks 
-include:
-
-  * Use :ref:`Axial position encoding <axial-pos-encoding>` (see below for more details). It’s a mechanism to avoid 
-    having a huge positional encoding matrix (when the sequence length is very big) by factorizing it in smaller 
-    matrices.
-  * Replace traditional attention by :ref:`LSH (local-sensitive hashing) attention <lsh-attention>` (see below for more 
-    details). It's a technique to avoid compute the full product query-key in the attention layers.
-  * Avoid storing the intermediate results of each layer by using reversible transformer layers to obtain them during 
-    the backward pass (subtracting the residuals from the input of the next layer gives them back) or recomputing them 
-    for results inside a given layer (less efficient than storing them but saves memory).
-  * Compute the feedforward operations by chunks and not on the whole batch.
-
-With those tricks, the model can be fed much larger sentences than traditional transformer autoregressive models.
-
-**Note:** This model could be very well be used in an autoencoding setting, there is no checkpoint for such a
-pretraining yet, though.
-
-The library provides a version of the model for language modeling only.
-
-XLNet
-----------------------------------------------
-
-.. raw:: html
-
-   <a href="https://huggingface.co/models?filter=xlnet">
-       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-xlnet-blueviolet">
-   </a>
-   <a href="/model_doc/xlnet">
-       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-xlnet-blueviolet">
-   </a>
-
-`XLNet: Generalized Autoregressive Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`_,
-Zhilin Yang et al.
-
-XLNet is not a traditional autoregressive model but uses a training strategy that builds on that. It permutes the 
-tokens in the sentence, then allows the model to use the last n tokens to predict the token n+1. Since this is all done 
-with a mask, the sentence is actually fed in the model in the right order, but instead of masking the first n tokens 
-for n+1, XLNet uses a mask that hides the previous tokens in some given permutation of 1,...,sequence length.
-
-XLNet also uses the same recurrence mechanism as TransformerXL to build long-term dependencies. 
-
-The library provides a version of the model for language modeling, token classification, sentence classification, 
-multiple choice classification and question answering.
-
-.. _autoencoding-models:
-
-Autoencoding models
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-As mentioned before, these models rely on the encoder part of the original transformer and use no mask so the model can
-look at all the tokens in the attention heads. For pretraining, inputs are a corrupted version of the sentence, usually 
-obtained by masking tokens, and targets are the original sentences.
-
-BERT
-----------------------------------------------
-
-.. raw:: html
-
-   <a href="https://huggingface.co/models?filter=bert">
-       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-bert-blueviolet">
-   </a>
-   <a href="/model_doc/bert">
-       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-bert-blueviolet">
-   </a>
-
-`BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`_,
-Jacob Devlin et al.
-
-Corrupts the inputs by using random masking, more precisely, during pretraining, a given percentage of tokens (usually 
-15%) are masked by
- 
-  * a special mask token with probability 0.8
-  * a random token different from the one masked with probability 0.1
-  * the same token with probability 0.1
-
-The model must predict the original sentence, but has a second objective: inputs are two sentences A and B (with a 
-separation token in between). With probability 50%, the sentences are consecutive in the corpus, in the remaining 50% 
-they are not related. The model has to predict if the sentences are consecutive or not.
-
-The library provides a version of the model for language modeling (traditional or masked), next sentence prediction, 
-token classification, sentence classification, multiple choice classification and question answering.
-
-ALBERT
-----------------------------------------------
-
-.. raw:: html
-
-   <a href="https://huggingface.co/models?filter=albert">
-       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-albert-blueviolet">
-   </a>
-   <a href="/model_doc/albert">
-       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-albert-blueviolet">
-   </a>
-
-`ALBERT: A Lite BERT for Self-supervised Learning of Language Representations <https://arxiv.org/abs/1909.11942>`_,
-Zhenzhong Lan et al.
-
-Same as BERT but with a few tweaks:
-
-  * Embedding size E is different from hidden size H justified because the embeddings are context independent (one 
-    embedding vector represents one token) whereas hidden states are context dependent (one hidden state represents a 
-    sequence of tokens) so it's more logical to have H >> E. Als, the embedding matrix is large since it's V x E (V 
-    being the vocab size). If E < H, it has less parameters.
-  * Layers are split in groups that share parameters (to save memory).
-  * Next sentence prediction is replaced by a sentence ordering prediction: in the inputs, we have two sentences A et B 
-    (that are consecutive) and we either feed A followed by B or B followed by A. The model must predict if they have 
-    been swapped or not.
-
-The library provides a version of the model for masked language modeling, token classification, sentence 
-classification, multiple choice classification and question answering.
-
-RoBERTa
-----------------------------------------------
-
-.. raw:: html
-
-   <a href="https://huggingface.co/models?filter=roberta">
-       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-roberta-blueviolet">
-   </a>
-   <a href="/model_doc/roberta">
-       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-roberta-blueviolet">
-   </a>
-
-`RoBERTa: A Robustly Optimized BERT Pretraining Approach <https://arxiv.org/abs/1907.11692>`_,
-Yinhan Liu et al.
-
-Same as BERT with better pretraining tricks:
-
-  * dynamic masking: tokens are masked differently at each epoch whereas BERT does it once and for all
-  * no NSP (next sentence prediction) loss and instead of putting just two sentences together, put a chunk of 
-    contiguous texts together to reach 512 tokens (so sentences in in an order than may span other several documents)
-  * train with larger batches
-  * use BPE with bytes as a subunit and not characters (because of unicode characters)
-
-The library provides a version of the model for masked language modeling, token classification, sentence 
-classification, multiple choice classification and question answering.
-
-DistilBERT
-----------------------------------------------
-
-.. raw:: html
-
-   <a href="https://huggingface.co/models?filter=distilbert">
-       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-distilbert-blueviolet">
-   </a>
-   <a href="/model_doc/distilbert">
-       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-distilbert-blueviolet">
-   </a>
-
-`DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter <https://arxiv.org/abs/1910.01108>`_,
-Victor Sanh et al.
-
-Same as BERT but smaller. Trained by distillation of the pretrained BERT model, meaning it's been trained to predict 
-the same probabilities as the larger model. The actual objective is a combination of:
-
-  * finding the same probabilities as the teacher model
-  * predicting the masked tokens correctly (but no next-sentence objective)
-  * a cosine similarity between the hidden states of the student and the teacher model
-
-The library provides a version of the model for masked language modeling, token classification, sentence classification 
-and question answering.
-
-XLM
-----------------------------------------------
-
-.. raw:: html
-
-   <a href="https://huggingface.co/models?filter=xlm">
-       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-xlm-blueviolet">
-   </a>
-   <a href="/model_doc/xlm">
-       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-xlm-blueviolet">
-   </a>
-
-`Cross-lingual Language Model Pretraining <https://arxiv.org/abs/1901.07291>`_, Guillaume Lample and Alexis Conneau
-
-A transformer model trained on several languages. There are three different type of training for this model and the 
-library provides checkpoints for all of them:
-
-  * Causal language modeling (CLM) which is the traditional autoregressive training (so this model could be in the 
-    previous section as well). One of the languages is selected for each training sample, and the model input is a 
-    sentence of 256 tokens that may span on several documents in one one those languages.
-  * Masked language modeling (MLM) which is like RoBERTa. One of the languages is selected for each training sample, 
-    and the model input is a sentence of 256 tokens that may span on several documents in one one those languages, with
-    dynamic masking of the tokens.
-  * A combination of MLM and translation language modeling (TLM). This consists of concatenating a sentence in two 
-    different languages, with random masking. To predict one of the masked token, the model can use both the 
-    surrounding context in language 1 as well as the context given by language 2.
-
-Checkpoints refer to which method was used for pretraining by having `clm`, `mlm` or `mlm-tlm` in their names. On top
-of positional embeddings, the model has language embeddings. When training using MLM/CLM, this gives the model an
-indication of the language used, and when training using MLM+TLM, an indication of which part of the input is in which
-language.
-
-The library provides a version of the model for language modeling, token classification, sentence classification and 
-question answering.
-
-XLM-RoBERTa
-----------------------------------------------
-
-.. raw:: html
-
-   <a href="https://huggingface.co/models?filter=xlm-roberta">
-       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-xlm--roberta-blueviolet">
-   </a>
-   <a href="/model_doc/xlmroberta">
-       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-xlm--roberta-blueviolet">
-   </a>
-
-`Unsupervised Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`_, Alexis Conneau et 
-al.
-
-Uses RoBERTa tricks on the XLM approach, but does not use the translation language modeling objective, only using 
-masked language modeling on sentences coming from one language. However, the model is trained on many more languages 
-(100) and doesn't use the language embeddings, so it's capable of detecting the input language by itself.
-
-The library provides a version of the model for masked language modeling, token classification, sentence 
-classification, multiple choice classification and question answering.
-
-FlauBERT
-----------------------------------------------
-
-.. raw:: html
-
-   <a href="https://huggingface.co/models?filter=flaubert">
-       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-flaubert-blueviolet">
-   </a>
-   <a href="/model_doc/flaubert">
-       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-flaubert-blueviolet">
-   </a>
-
-`FlauBERT: Unsupervised Language Model Pre-training for French <https://arxiv.org/abs/1912.05372>`_, Hang Le et al.
-
-Like RoBERTa, without the sentence ordering prediction (so just trained on the MLM objective).
-
-The library provides a version of the model for language modeling and sentence classification.
-
-ELECTRA
-----------------------------------------------
-
-.. raw:: html
-
-   <a href="https://huggingface.co/models?filter=electra">
-       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-electra-blueviolet">
-   </a>
-   <a href="/model_doc/electra">
-       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-electra-blueviolet">
-   </a>
-
-`ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators <https://arxiv.org/abs/2003.10555>`_, 
-Kevin Clark et al.
-
-ELECTRA is a transformer model pretrained with the use of another (small) masked language model. The inputs are 
-corrupted by that language model, which takes an input text that is randomly masked and outputs a text in which ELECTRA 
-has to predict which token is an original and which one has been replaced. Like for GAN training, the small language 
-model is trained for a few steps (but with the original texts as objective, not to fool the ELECTRA model like in a 
-traditional GAN setting) then the ELECTRA model is trained for a few steps.
-
-The library provides a version of the model for masked language modeling, token classification and sentence 
-classification.
-
-.. _longformer:
-
-Longformer
-----------------------------------------------
-
-.. raw:: html
-
-   <a href="https://huggingface.co/models?filter=longformer">
-       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-longformer-blueviolet">
-   </a>
-   <a href="/model_doc/longformer">
-       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-longformer-blueviolet">
-   </a>
-
-`Longformer: The Long-Document Transformer <https://arxiv.org/abs/2004.05150>`_, Iz Beltagy et al.
-
-A transformer model replacing the attention matrices by sparse matrices to go faster. Often, the local context (e.g., 
-what are the two tokens left and right?) is enough to take action for a given token. Some preselected input tokens are 
-still given global attention, but the attention matrix has way less parameters, resulting in a speed-up. See the 
-:ref:`local attention section <local-attention>` for more information.
-
-It is pretrained the same way a RoBERTa otherwise.
-
-**Note:** This model could be very well be used in an autoregressive setting, there is no checkpoint for such a
-pretraining yet, though.
-
-The library provides a version of the model for masked language modeling, token classification, sentence 
-classification, multiple choice classification and question answering.
-
-.. _seq-to-seq-models:
-
-Sequence-to-sequence models
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-As mentioned before, these models keep both the encoder and the decoder of the original transformer.
-
-BART
-----------------------------------------------
-
-.. raw:: html
-
-   <a href="https://huggingface.co/models?filter=bart">
-       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-bart-blueviolet">
-   </a>
-   <a href="/model_doc/bart">
-       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-bart-blueviolet">
-   </a>
-
-`BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension 
-<https://arxiv.org/abs/1910.13461>`_, Mike Lewis et al.
-
-Sequence-to-sequence model with an encoder and a decoder. Encoder is fed a corrupted version of the tokens, decoder is 
-fed the tokens (but has a mask to hide the future words like a regular transformers decoder). For the encoder, on the 
-pretraining tasks, a composition of the following transformations are applied:
-
-  * mask random tokens (like in BERT)
-  * delete random tokens
-  * mask a span of k tokens with a single mask token (a span of 0 tokens is an insertion of a mask token)
-  * permute sentences
-  * rotate the document to make it start by a specific token
-
-The library provides a version of this model for conditional generation and sequence classification.
-
-MarianMT
-----------------------------------------------
-
-.. raw:: html
-
-   <a href="https://huggingface.co/models?filter=marian">
-       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-marian-blueviolet">
-   </a>
-   <a href="/model_doc/marian">
-       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-marian-blueviolet">
-   </a>
-
-`Marian: Fast Neural Machine Translation in C++ <https://arxiv.org/abs/1804.00344>`_, Marcin Junczys-Dowmunt et al.
-
-A framework for translation models, using the same models as BART
-
-The library provides a version of this model for conditional generation.
-
-T5
-----------------------------------------------
-
-.. raw:: html
-
-   <a href="https://huggingface.co/models?filter=t5">
-       <img alt="Models" src="https://img.shields.io/badge/All_model_pages-t5-blueviolet">
-   </a>
-   <a href="/model_doc/t5">
-       <img alt="Doc" src="https://img.shields.io/badge/Model_documentation-t5-blueviolet">
-   </a>
-
-`Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer <https://arxiv.org/abs/1910.10683>`_, 
-Colin Raffel et al.
-
-Uses the traditional transformer model (except a slight change with the positional embeddings, which are learned at 
-each layer). To be able to operate on all NLP tasks, it transforms them in text-to-text problems by using certain 
-prefixes: “Summarize: …”, “question: …”, “translate English to German: …” and so forth.
-
-The pretraining includes both supervised and self-supervised training. Supervised training is conducted on downstream 
-tasks provided by the GLUE and SuperGLUE benchmarks (changing them to text-to-text tasks as explained above).
-
-Self-supervised training consists of corrupted pretrained, which means randomly removing 15% of the tokens and 
-replacing them by individual sentinel tokens (if several consecutive tokens are marked for removal, they are replaced 
-by one single sentinel token). The input of the encoder is the corrupted sentence, the input of the decoder the 
-original sentence and the target is then the dropped out tokens delimited by their sentinel tokens.
-
-For instance, if we have the sentence “My dog is very cute .”, and we decide to remove the token dog, is and cute, the 
-input becomes “My <x> very <y> .” and the target is “<x> dog is <y> . <z>”
-
-The library provides a version of this model for conditional generation.
-
-.. _multimodal-models:
-
-Multimodal models
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-There is one multimodal model in the library which has not been pretrained in the self-supervised fashion like the 
-others.
-
-MMBT
-----------------------------------------------
-
-`Supervised Multimodal Bitransformers for Classifying Images and Text <https://arxiv.org/abs/1909.02950>`_, Douwe Kiela 
-et al.
-
-A transformers model used in multimodal settings, combining a text and an image to make predictions. The transformer 
-model takes as inputs the embeddings of the tokenized text and a the final activations of a pretrained resnet on the 
-images (after the pooling layer) that goes through a linear layer (to go from number of features at the end of the 
-resnet to the hidden state dimension of the transformer).
-
-The different inputs are concatenated, and on top of the positional embeddings, a segment embedding is added to let the 
-model know which part of the input vector corresponds to the text or the image.
-
-The pretrained model only works for classification.
-
-..
-    More information in this :doc:`model documentation </model_doc/mmbt>`.
-    TODO: write this page
-
-More technical aspects
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Full vs sparse attention
-----------------------------------------------
-
-Most transformer models use full attention in the sense that the attention matrix is square. It can be a big 
-computational bottleneck when you have long texts. Longformer and reformer are models that try to be more efficient and 
-use a sparse version of the attention matrix to speed up training.
-
-.. _lsh-attention:
-
-**LSH attention**
-
-:ref:`Reformer <reformer>` uses LSH attention. In the softmax(QK^t), only the biggest elements (in the softmax 
-dimension) of the matrix QK^t are going to give useful contributions. So for each query q in Q, we can only consider 
-the keys k in K that are close to q. A hash function is used to determine if q and k are close. The attention mask is 
-modified to mask the current token (except at the first position) because it will give a query and key equal (so very 
-similar to each other). Since the hash can be a bit random, several hash functions are used in practice (determined by 
-a n_rounds parameter) then are averaged together.
-
-.. _local-attention:
-
-**Local attention**
-
-:ref:`Longformer <longformer>` uses local attention: often, the local context (e.g., what are the two tokens left and 
-right?) is enough to take action for a given token. Also, by stacking attention layers that have a small window, the 
-last layer will have a receptive field of more than just the tokens on the window, allowing them to build a 
-representation of the whole sentence.
-
-Some preselected input tokens are also given global attention: for those few tokens, the attention matrix can access 
-all tokens and this process is symmetric: all other tokens have access to those specific tokens (on top of the ones in 
-their local window). This is shown in Figure 2d of the paper, see below for a sample attention mask:
-
-.. image:: imgs/local_attention_mask.png
-   :scale: 50 %
-   :align: center
-
-Using those attention matrices with less parameters then allows the model to have inputs having a bigger sequence 
-length.
-
-Other tricks
-----------------------------------------------
-
-.. _axial-pos-encoding:
-
-**Axial positional encodings**
-
-:ref:`Reformer <reformer>` uses axial positional encodings: in traditional transformer models, the positional encoding 
-E is a matrix of size :math:`l` by :math:`d`, :math:`l` being the sequence length and :math:`d` the dimension of the 
-hidden state. If you have very long texts, this matrix can be huge and take way too much space on the GPU.
-
-To alleviate that, axial positional encodings consists in factorizing that big matrix E in two smaller matrices E1 and 
-E2, with dimensions :math:`l_{1} \times d_{1}` and :math:`l_{2} \times d_{2}`, such that :math:`l_{1} \times l_{2} = l`
-and :math:`d_{1} + d_{2} = d` (with the product for the lengths, this ends up being way smaller). The embedding for 
-time step :math:`j` in E is obtained by concatenating the embeddings for timestep :math:`j \% l1` in E1 and 
-:math:`j // l1` in E2.
-

examples/README.md

@@ -1,7 +1,6 @@
 ## Examples
 
 Version 2.9 of `transformers` introduces a new [`Trainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer.py) class for PyTorch, and its equivalent [`TFTrainer`](https://github.com/huggingface/transformers/blob/master/src/transformers/trainer_tf.py) for TF 2.
-Running the examples requires PyTorch 1.3.1+ or TensorFlow 2.0+.
 
 Here is the list of all our examples:
 - **grouped by task** (all official examples work for multiple models)
@@ -17,17 +16,17 @@ This is still a work-in-progress – in particular documentation is still sparse
 
 | Task | Example datasets | Trainer support | TFTrainer support | pytorch-lightning | Colab
 |---|---|:---:|:---:|:---:|:---:|
-| [**`language-modeling`**](https://github.com/huggingface/transformers/tree/master/examples/language-modeling)       | Raw text        | ✅ | -  | -  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb)
-| [**`text-classification`**](https://github.com/huggingface/transformers/tree/master/examples/text-classification)   | GLUE, XNLI      | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/trainer/01_text_classification.ipynb)
-| [**`token-classification`**](https://github.com/huggingface/transformers/tree/master/examples/token-classification) | CoNLL NER       | ✅ | ✅ | ✅ | -
-| [**`multiple-choice`**](https://github.com/huggingface/transformers/tree/master/examples/multiple-choice)           | SWAG, RACE, ARC | ✅ | ✅ | -  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ViktorAlm/notebooks/blob/master/MPC_GPU_Demo_for_TF_and_PT.ipynb)
-| [**`question-answering`**](https://github.com/huggingface/transformers/tree/master/examples/question-answering)     | SQuAD           | -  | ✅ | -  | -
-| [**`text-generation`**](https://github.com/huggingface/transformers/tree/master/examples/text-generation)           | -               | n/a | n/a | n/a | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb)
-| [**`distillation`**](https://github.com/huggingface/transformers/tree/master/examples/distillation)       | All               | -  | -  | -  | -
-| [**`summarization`**](https://github.com/huggingface/transformers/tree/master/examples/summarization)     | CNN/Daily Mail    | -  | -  | -  | -
-| [**`translation`**](https://github.com/huggingface/transformers/tree/master/examples/translation)         | WMT               | -  | -  | -  | -
-| [**`bertology`**](https://github.com/huggingface/transformers/tree/master/examples/bertology)             | -                 | -  | -  | -  | -
-| [**`adversarial`**](https://github.com/huggingface/transformers/tree/master/examples/adversarial)         | HANS              | -  | -  | -  | -
+| [**`language-modeling`**](./language-modeling)       | Raw text        | ✅ | -  | -  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb)
+| [**`text-classification`**](./text-classification)   | GLUE, XNLI      | ✅ | ✅ | ✅ | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/trainer/01_text_classification.ipynb)
+| [**`token-classification`**](./token-classification) | CoNLL NER       | ✅ | ✅ | ✅ | -
+| [**`multiple-choice`**](./multiple-choice)           | SWAG, RACE, ARC | ✅ | ✅ | -  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ViktorAlm/notebooks/blob/master/MPC_GPU_Demo_for_TF_and_PT.ipynb)
+| [**`question-answering`**](./question-answering)     | SQuAD           | -  | ✅ | -  | -
+| [**`text-generation`**](./text-generation)     | -           | -  | - | -  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/02_how_to_generate.ipynb)
+| [**`distillation`**](./distillation)       | All               | -  | -  | -  | -
+| [**`summarization`**](./summarization)     | CNN/Daily Mail    | -  | -  | -  | -
+| [**`translation`**](./translation)         | WMT               | -  | -  | -  | -
+| [**`bertology`**](./bertology)             | -                 | -  | -  | -  | -
+| [**`adversarial`**](./adversarial)         | HANS              | -  | -  | -  | -
 
 
 <br>
@@ -58,7 +57,7 @@ When using Tensorflow, TPUs are supported out of the box as a `tf.distribute.Str
 When using PyTorch, we support TPUs thanks to `pytorch/xla`. For more context and information on how to setup your TPU environment refer to Google's documentation and to the
 very detailed [pytorch/xla README](https://github.com/pytorch/xla/blob/master/README.md).
 
-In this repo, we provide a very simple launcher script named [xla_spawn.py](https://github.com/huggingface/transformers/tree/master/examples/xla_spawn.py) that lets you run our example scripts on multiple TPU cores without any boilerplate.
+In this repo, we provide a very simple launcher script named [xla_spawn.py](./xla_spawn.py) that lets you run our example scripts on multiple TPU cores without any boilerplate.
 Just pass a `--num_cores` flag to this script, then your regular training script with its arguments (this is similar to the `torch.distributed.launch` helper for torch.distributed).
 
 For example for `run_glue`:

examples/adversarial/README.md

@@ -11,7 +11,7 @@ export HANS_DIR=path-to-hans
 export MODEL_TYPE=type-of-the-model-e.g.-bert-roberta-xlnet-etc
 export MODEL_PATH=path-to-the-model-directory-that-is-trained-on-NLI-e.g.-by-using-run_glue.py
 
-python examples/adversarial/test_hans.py \
+python examples/hans/test_hans.py \
         --task_name hans \
         --model_type $MODEL_TYPE \
         --do_eval \

examples/adversarial/hans_processors.py

@@ -0,0 +1,221 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" GLUE processors and helpers """
+
+import logging
+import os
+
+from transformers.file_utils import is_tf_available
+from utils_hans import DataProcessor, InputExample, InputFeatures
+
+
+if is_tf_available():
+    import tensorflow as tf
+
+logger = logging.getLogger(__name__)
+
+
+def hans_convert_examples_to_features(
+    examples,
+    tokenizer,
+    max_length=512,
+    task=None,
+    label_list=None,
+    output_mode=None,
+    pad_on_left=False,
+    pad_token=0,
+    pad_token_segment_id=0,
+    mask_padding_with_zero=True,
+):
+    """
+    Loads a data file into a list of ``InputFeatures``
+
+    Args:
+        examples: List of ``InputExamples`` or ``tf.data.Dataset`` containing the examples.
+        tokenizer: Instance of a tokenizer that will tokenize the examples
+        max_length: Maximum example length
+        task: HANS
+        label_list: List of labels. Can be obtained from the processor using the ``processor.get_labels()`` method
+        output_mode: String indicating the output mode. Either ``regression`` or ``classification``
+        pad_on_left: If set to ``True``, the examples will be padded on the left rather than on the right (default)
+        pad_token: Padding token
+        pad_token_segment_id: The segment ID for the padding token (It is usually 0, but can vary such as for XLNet where it is 4)
+        mask_padding_with_zero: If set to ``True``, the attention mask will be filled by ``1`` for actual values
+            and by ``0`` for padded values. If set to ``False``, inverts it (``1`` for padded values, ``0`` for
+            actual values)
+
+    Returns:
+        If the ``examples`` input is a ``tf.data.Dataset``, will return a ``tf.data.Dataset``
+        containing the task-specific features. If the input is a list of ``InputExamples``, will return
+        a list of task-specific ``InputFeatures`` which can be fed to the model.
+
+    """
+    is_tf_dataset = False
+    if is_tf_available() and isinstance(examples, tf.data.Dataset):
+        is_tf_dataset = True
+
+    if task is not None:
+        processor = glue_processors[task]()
+        if label_list is None:
+            label_list = processor.get_labels()
+            logger.info("Using label list %s for task %s" % (label_list, task))
+        if output_mode is None:
+            output_mode = glue_output_modes[task]
+            logger.info("Using output mode %s for task %s" % (output_mode, task))
+
+    label_map = {label: i for i, label in enumerate(label_list)}
+
+    features = []
+    for (ex_index, example) in enumerate(examples):
+        if ex_index % 10000 == 0:
+            logger.info("Writing example %d" % (ex_index))
+        if is_tf_dataset:
+            example = processor.get_example_from_tensor_dict(example)
+            example = processor.tfds_map(example)
+
+        inputs = tokenizer.encode_plus(example.text_a, example.text_b, add_special_tokens=True, max_length=max_length,)
+        input_ids, token_type_ids = inputs["input_ids"], inputs["token_type_ids"]
+
+        # The mask has 1 for real tokens and 0 for padding tokens. Only real
+        # tokens are attended to.
+        attention_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
+
+        # Zero-pad up to the sequence length.
+        padding_length = max_length - len(input_ids)
+        if pad_on_left:
+            input_ids = ([pad_token] * padding_length) + input_ids
+            attention_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
+            token_type_ids = ([pad_token_segment_id] * padding_length) + token_type_ids
+        else:
+            input_ids = input_ids + ([pad_token] * padding_length)
+            attention_mask = attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
+            token_type_ids = token_type_ids + ([pad_token_segment_id] * padding_length)
+
+        assert len(input_ids) == max_length, "Error with input length {} vs {}".format(len(input_ids), max_length)
+        assert len(attention_mask) == max_length, "Error with input length {} vs {}".format(
+            len(attention_mask), max_length
+        )
+        assert len(token_type_ids) == max_length, "Error with input length {} vs {}".format(
+            len(token_type_ids), max_length
+        )
+
+        if output_mode == "classification":
+            label = label_map[example.label] if example.label in label_map else 0
+        elif output_mode == "regression":
+            label = float(example.label)
+        else:
+            raise KeyError(output_mode)
+        pairID = str(example.pairID)
+
+        if ex_index < 10:
+            logger.info("*** Example ***")
+            logger.info("text_a: %s" % (example.text_a))
+            logger.info("text_b: %s" % (example.text_b))
+            logger.info("guid: %s" % (example.guid))
+            logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
+            logger.info("attention_mask: %s" % " ".join([str(x) for x in attention_mask]))
+            logger.info("token_type_ids: %s" % " ".join([str(x) for x in token_type_ids]))
+            logger.info("label: %s (id = %d)" % (example.label, label))
+
+        features.append(
+            InputFeatures(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                token_type_ids=token_type_ids,
+                label=label,
+                pairID=pairID,
+            )
+        )
+
+    if is_tf_available() and is_tf_dataset:
+
+        def gen():
+            for ex in features:
+                yield (
+                    {
+                        "input_ids": ex.input_ids,
+                        "attention_mask": ex.attention_mask,
+                        "token_type_ids": ex.token_type_ids,
+                    },
+                    ex.label,
+                )
+
+        return tf.data.Dataset.from_generator(
+            gen,
+            ({"input_ids": tf.int32, "attention_mask": tf.int32, "token_type_ids": tf.int32}, tf.int64),
+            (
+                {
+                    "input_ids": tf.TensorShape([None]),
+                    "attention_mask": tf.TensorShape([None]),
+                    "token_type_ids": tf.TensorShape([None]),
+                },
+                tf.TensorShape([]),
+            ),
+        )
+
+    return features
+
+
+class HansProcessor(DataProcessor):
+    """Processor for the HANS data set."""
+
+    def get_example_from_tensor_dict(self, tensor_dict):
+        """See base class."""
+        return InputExample(
+            tensor_dict["idx"].numpy(),
+            tensor_dict["premise"].numpy().decode("utf-8"),
+            tensor_dict["hypothesis"].numpy().decode("utf-8"),
+            str(tensor_dict["label"].numpy()),
+        )
+
+    def get_train_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(self._read_tsv(os.path.join(data_dir, "heuristics_train_set.txt")), "train")
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(self._read_tsv(os.path.join(data_dir, "heuristics_evaluation_set.txt")), "dev")
+
+    def get_labels(self):
+        """See base class."""
+        return ["contradiction", "entailment", "neutral"]
+
+    def _create_examples(self, lines, set_type):
+        """Creates examples for the training and dev sets."""
+        examples = []
+        for (i, line) in enumerate(lines):
+            if i == 0:
+                continue
+            guid = "%s-%s" % (set_type, line[0])
+            text_a = line[5]
+            text_b = line[6]
+            pairID = line[7][2:] if line[7].startswith("ex") else line[7]
+            label = line[-1]
+            examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label, pairID=pairID))
+        return examples
+
+
+glue_tasks_num_labels = {
+    "hans": 3,
+}
+
+glue_processors = {
+    "hans": HansProcessor,
+}
+
+glue_output_modes = {
+    "hans": "classification",
+}

examples/adversarial/test_hans.py

@@ -25,10 +25,13 @@ import random
 
 import numpy as np
 import torch
-from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
+from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
 from torch.utils.data.distributed import DistributedSampler
 from tqdm import tqdm, trange
 
+from hans_processors import glue_output_modes as output_modes
+from hans_processors import glue_processors as processors
+from hans_processors import hans_convert_examples_to_features as convert_examples_to_features
 from transformers import (
     WEIGHTS_NAME,
     AdamW,
@@ -50,10 +53,8 @@ from transformers import (
     XLNetConfig,
     XLNetForSequenceClassification,
     XLNetTokenizer,
-    default_data_collator,
     get_linear_schedule_with_warmup,
 )
-from utils_hans import HansDataset, hans_output_modes, hans_processors
 
 
 try:
@@ -64,6 +65,13 @@ except ImportError:
 
 logger = logging.getLogger(__name__)
 
+ALL_MODELS = sum(
+    (
+        tuple(conf.pretrained_config_archive_map.keys())
+        for conf in (BertConfig, XLNetConfig, XLMConfig, RobertaConfig, DistilBertConfig)
+    ),
+    (),
+)
 
 MODEL_CLASSES = {
     "bert": (BertConfig, BertForSequenceClassification, BertTokenizer),
@@ -90,9 +98,7 @@ def train(args, train_dataset, model, tokenizer):
 
     args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
     train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
-    train_dataloader = DataLoader(
-        train_dataset, sampler=train_sampler, batch_size=args.train_batch_size, collate_fn=default_data_collator,
-    )
+    train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)
 
     if args.max_steps > 0:
         t_total = args.max_steps
@@ -154,7 +160,12 @@ def train(args, train_dataset, model, tokenizer):
         epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
         for step, batch in enumerate(epoch_iterator):
             model.train()
-            inputs = {k: t.to(args.device) for k, t in batch.items() if k != "pairID"}
+            batch = tuple(t.to(args.device) for t in batch)
+            inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
+            if args.model_type != "distilbert":
+                inputs["token_type_ids"] = (
+                    batch[2] if args.model_type in ["bert", "xlnet"] else None
+                )  # XLM, DistilBERT and RoBERTa don't use segment_ids
             outputs = model(**inputs)
             loss = outputs[0]  # model outputs are always tuple in transformers (see doc)
 
@@ -226,21 +237,14 @@ def train(args, train_dataset, model, tokenizer):
     return global_step, tr_loss / global_step
 
 
-def evaluate(args, model, tokenizer, label_list, prefix=""):
+def evaluate(args, model, tokenizer, prefix=""):
     # Loop to handle MNLI double evaluation (matched, mis-matched)
     eval_task_names = ("mnli", "mnli-mm") if args.task_name == "mnli" else (args.task_name,)
     eval_outputs_dirs = (args.output_dir, args.output_dir + "-MM") if args.task_name == "mnli" else (args.output_dir,)
 
     results = {}
     for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs):
-        eval_dataset = HansDataset(
-            args.data_dir,
-            tokenizer,
-            args.task_name,
-            args.max_seq_length,
-            overwrite_cache=args.overwrite_cache,
-            evaluate=True,
-        )
+        eval_dataset, label_list = load_and_cache_examples(args, eval_task, tokenizer, evaluate=True)
 
         if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
             os.makedirs(eval_output_dir)
@@ -248,9 +252,7 @@ def evaluate(args, model, tokenizer, label_list, prefix=""):
         args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
         # Note that DistributedSampler samples randomly
         eval_sampler = SequentialSampler(eval_dataset)
-        eval_dataloader = DataLoader(
-            eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, collate_fn=default_data_collator,
-        )
+        eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
 
         # multi-gpu eval
         if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
@@ -266,9 +268,14 @@ def evaluate(args, model, tokenizer, label_list, prefix=""):
         out_label_ids = None
         for batch in tqdm(eval_dataloader, desc="Evaluating"):
             model.eval()
-            inputs = {k: t.to(args.device) for k, t in batch.items() if k != "pairID"}
-            pair_ids = batch.pop("pairID", None)
+            batch = tuple(t.to(args.device) for t in batch)
+
             with torch.no_grad():
+                inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
+                if args.model_type != "distilbert":
+                    inputs["token_type_ids"] = (
+                        batch[2] if args.model_type in ["bert", "xlnet"] else None
+                    )  # XLM, DistilBERT and RoBERTa don't use segment_ids
                 outputs = model(**inputs)
                 tmp_eval_loss, logits = outputs[:2]
 
@@ -277,11 +284,11 @@ def evaluate(args, model, tokenizer, label_list, prefix=""):
             if preds is None:
                 preds = logits.detach().cpu().numpy()
                 out_label_ids = inputs["labels"].detach().cpu().numpy()
-                pair_ids = pair_ids.detach().cpu().numpy()
+                pair_ids = batch[4].detach().cpu().numpy()
             else:
                 preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
                 out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0)
-                pair_ids = np.append(pair_ids, pair_ids.detach().cpu().numpy(), axis=0)
+                pair_ids = np.append(pair_ids, batch[4].detach().cpu().numpy(), axis=0)
 
         eval_loss = eval_loss / nb_eval_steps
         if args.output_mode == "classification":
@@ -298,6 +305,67 @@ def evaluate(args, model, tokenizer, label_list, prefix=""):
     return results
 
 
+def load_and_cache_examples(args, task, tokenizer, evaluate=False):
+    if args.local_rank not in [-1, 0] and not evaluate:
+        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
+
+    processor = processors[task]()
+    output_mode = output_modes[task]
+    # Load data features from cache or dataset file
+    cached_features_file = os.path.join(
+        args.data_dir,
+        "cached_{}_{}_{}_{}".format(
+            "dev" if evaluate else "train",
+            list(filter(None, args.model_name_or_path.split("/"))).pop(),
+            str(args.max_seq_length),
+            str(task),
+        ),
+    )
+
+    label_list = processor.get_labels()
+
+    if os.path.exists(cached_features_file) and not args.overwrite_cache:
+        logger.info("Loading features from cached file %s", cached_features_file)
+        features = torch.load(cached_features_file)
+    else:
+        logger.info("Creating features from dataset file at %s", args.data_dir)
+        if task in ["mnli", "mnli-mm"] and args.model_type in ["roberta"]:
+            # HACK(label indices are swapped in RoBERTa pretrained model)
+            label_list[1], label_list[2] = label_list[2], label_list[1]
+        examples = (
+            processor.get_dev_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir)
+        )
+        features = convert_examples_to_features(
+            examples,
+            tokenizer,
+            label_list=label_list,
+            max_length=args.max_seq_length,
+            output_mode=output_mode,
+            pad_on_left=bool(args.model_type in ["xlnet"]),  # pad on the left for xlnet
+            pad_token=tokenizer.pad_token_id,
+            pad_token_segment_id=tokenizer.pad_token_type_id,
+        )
+        if args.local_rank in [-1, 0]:
+            logger.info("Saving features into cached file %s", cached_features_file)
+            torch.save(features, cached_features_file)
+
+    if args.local_rank == 0 and not evaluate:
+        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
+
+    # Convert to Tensors and build dataset
+    all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
+    all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
+    all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long)
+    if output_mode == "classification":
+        all_labels = torch.tensor([f.label for f in features], dtype=torch.long)
+    elif output_mode == "regression":
+        all_labels = torch.tensor([f.label for f in features], dtype=torch.float)
+    all_pair_ids = torch.tensor([int(f.pairID) for f in features], dtype=torch.long)
+
+    dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels, all_pair_ids)
+    return dataset, label_list
+
+
 def main():
     parser = argparse.ArgumentParser()
 
@@ -321,14 +389,14 @@ def main():
         default=None,
         type=str,
         required=True,
-        help="Path to pretrained model or model identifier from huggingface.co/models",
+        help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS),
     )
     parser.add_argument(
         "--task_name",
         default=None,
         type=str,
         required=True,
-        help="The name of the task to train selected in the list: " + ", ".join(hans_processors.keys()),
+        help="The name of the task to train selected in the list: " + ", ".join(processors.keys()),
     )
     parser.add_argument(
         "--output_dir",
@@ -480,10 +548,10 @@ def main():
 
     # Prepare GLUE task
     args.task_name = args.task_name.lower()
-    if args.task_name not in hans_processors:
+    if args.task_name not in processors:
         raise ValueError("Task not found: %s" % (args.task_name))
-    processor = hans_processors[args.task_name]()
-    args.output_mode = hans_output_modes[args.task_name]
+    processor = processors[args.task_name]()
+    args.output_mode = output_modes[args.task_name]
     label_list = processor.get_labels()
     num_labels = len(label_list)
 
@@ -520,9 +588,7 @@ def main():
 
     # Training
     if args.do_train:
-        train_dataset = HansDataset(
-            args.data_dir, tokenizer, args.task_name, args.max_seq_length, overwrite_cache=args.overwrite_cache
-        )
+        train_dataset, _ = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=False)
         global_step, tr_loss = train(args, train_dataset, model, tokenizer)
         logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
 
@@ -566,7 +632,7 @@ def main():
 
             model = model_class.from_pretrained(checkpoint)
             model.to(args.device)
-            result = evaluate(args, model, tokenizer, label_list, prefix=prefix)
+            result = evaluate(args, model, tokenizer, prefix=prefix)
             result = dict((k + "_{}".format(global_step), v) for k, v in result.items())
             results.update(result)
 

examples/adversarial/utils_hans.py

@@ -14,339 +14,108 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import logging
-import os
-from dataclasses import dataclass
-from typing import List, Optional, Union
-
-import tqdm
-from filelock import FileLock
-
-from transformers import (
-    DataProcessor,
-    PreTrainedTokenizer,
-    RobertaTokenizer,
-    RobertaTokenizerFast,
-    XLMRobertaTokenizer,
-    is_tf_available,
-    is_torch_available,
-)
+import copy
+import csv
+import json
 
 
-logger = logging.getLogger(__name__)
-
-
-@dataclass(frozen=True)
-class InputExample:
+class InputExample(object):
     """
     A single training/test example for simple sequence classification.
 
     Args:
         guid: Unique id for the example.
         text_a: string. The untokenized text of the first sequence. For single
-            sequence tasks, only this sequence must be specified.
+        sequence tasks, only this sequence must be specified.
         text_b: (Optional) string. The untokenized text of the second sequence.
-            Only must be specified for sequence pair tasks.
+        Only must be specified for sequence pair tasks.
         label: (Optional) string. The label of the example. This should be
-            specified for train and dev examples, but not for test examples.
-        pairID: (Optional) string. Unique identifier for the pair of sentences.
+        specified for train and dev examples, but not for test examples.
     """
 
-    guid: str
-    text_a: str
-    text_b: Optional[str] = None
-    label: Optional[str] = None
-    pairID: Optional[str] = None
+    def __init__(self, guid, text_a, text_b=None, label=None, pairID=None):
+        self.guid = guid
+        self.text_a = text_a
+        self.text_b = text_b
+        self.label = label
+        self.pairID = pairID
+
+    def __repr__(self):
+        return str(self.to_json_string())
+
+    def to_dict(self):
+        """Serializes this instance to a Python dictionary."""
+        output = copy.deepcopy(self.__dict__)
+        return output
+
+    def to_json_string(self):
+        """Serializes this instance to a JSON string."""
+        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
 
 
-@dataclass(frozen=True)
-class InputFeatures:
+class InputFeatures(object):
     """
     A single set of features of data.
-    Property names are the same names as the corresponding inputs to a model.
 
     Args:
         input_ids: Indices of input sequence tokens in the vocabulary.
         attention_mask: Mask to avoid performing attention on padding token indices.
             Mask values selected in ``[0, 1]``:
             Usually  ``1`` for tokens that are NOT MASKED, ``0`` for MASKED (padded) tokens.
-        token_type_ids: (Optional) Segment token indices to indicate first and second
-            portions of the inputs. Only some models use them.
-        label: (Optional) Label corresponding to the input. Int for classification problems,
-            float for regression problems.
-        pairID: (Optional) Unique identifier for the pair of sentences.
+        token_type_ids: Segment token indices to indicate first and second portions of the inputs.
+        label: Label corresponding to the input
     """
 
-    input_ids: List[int]
-    attention_mask: Optional[List[int]] = None
-    token_type_ids: Optional[List[int]] = None
-    label: Optional[Union[int, float]] = None
-    pairID: Optional[int] = None
+    def __init__(self, input_ids, attention_mask, token_type_ids, label, pairID=None):
+        self.input_ids = input_ids
+        self.attention_mask = attention_mask
+        self.token_type_ids = token_type_ids
+        self.label = label
+        self.pairID = pairID
+
+    def __repr__(self):
+        return str(self.to_json_string())
+
+    def to_dict(self):
+        """Serializes this instance to a Python dictionary."""
+        output = copy.deepcopy(self.__dict__)
+        return output
+
+    def to_json_string(self):
+        """Serializes this instance to a JSON string."""
+        return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
 
 
-if is_torch_available():
-    import torch
-    from torch.utils.data.dataset import Dataset
-
-    class HansDataset(Dataset):
-        """
-        This will be superseded by a framework-agnostic approach
-        soon.
-        """
-
-        features: List[InputFeatures]
-
-        def __init__(
-            self,
-            data_dir: str,
-            tokenizer: PreTrainedTokenizer,
-            task: str,
-            max_seq_length: Optional[int] = None,
-            overwrite_cache=False,
-            evaluate: bool = False,
-        ):
-            processor = hans_processors[task]()
-            output_mode = hans_output_modes[task]
-
-            cached_features_file = os.path.join(
-                data_dir,
-                "cached_{}_{}_{}_{}".format(
-                    "dev" if evaluate else "train", tokenizer.__class__.__name__, str(max_seq_length), task,
-                ),
-            )
-
-            # Make sure only the first process in distributed training processes the dataset,
-            # and the others will use the cache.
-            lock_path = cached_features_file + ".lock"
-            with FileLock(lock_path):
-
-                if os.path.exists(cached_features_file) and not overwrite_cache:
-                    logger.info(f"Loading features from cached file {cached_features_file}")
-                    self.features = torch.load(cached_features_file)
-                else:
-                    logger.info(f"Creating features from dataset file at {data_dir}")
-                    label_list = processor.get_labels()
-
-                    if task in ["mnli", "mnli-mm"] and tokenizer.__class__ in (
-                        RobertaTokenizer,
-                        RobertaTokenizerFast,
-                        XLMRobertaTokenizer,
-                    ):
-                        # HACK(label indices are swapped in RoBERTa pretrained model)
-                        label_list[1], label_list[2] = label_list[2], label_list[1]
-                    examples = (
-                        processor.get_dev_examples(data_dir) if evaluate else processor.get_train_examples(data_dir)
-                    )
-
-                    logger.info("Training examples: %s", len(examples))
-                    # TODO clean up all this to leverage built-in features of tokenizers
-                    self.features = hans_convert_examples_to_features(
-                        examples, label_list, max_seq_length, tokenizer, output_mode
-                    )
-                    logger.info("Saving features into cached file %s", cached_features_file)
-                    torch.save(self.features, cached_features_file)
-
-        def __len__(self):
-            return len(self.features)
-
-        def __getitem__(self, i) -> InputFeatures:
-            return self.features[i]
-
-
-if is_tf_available():
-    import tensorflow as tf
-
-    class TFHansDataset:
-        """
-        This will be superseded by a framework-agnostic approach
-        soon.
-        """
-
-        features: List[InputFeatures]
-
-        def __init__(
-            self,
-            data_dir: str,
-            tokenizer: PreTrainedTokenizer,
-            task: str,
-            max_seq_length: Optional[int] = 128,
-            overwrite_cache=False,
-            evaluate: bool = False,
-        ):
-            processor = hans_processors[task]()
-            output_mode = hans_output_modes[task]
-            label_list = processor.get_labels()
-
-            if task in ["mnli", "mnli-mm"] and tokenizer.__class__ in (
-                RobertaTokenizer,
-                RobertaTokenizerFast,
-                XLMRobertaTokenizer,
-            ):
-                # HACK(label indices are swapped in RoBERTa pretrained model)
-                label_list[1], label_list[2] = label_list[2], label_list[1]
-
-            examples = processor.get_dev_examples(data_dir) if evaluate else processor.get_train_examples(data_dir)
-            self.features = hans_convert_examples_to_features(
-                examples, label_list, max_seq_length, tokenizer, output_mode
-            )
-
-            def gen():
-                for (ex_index, ex) in tqdm.tqdm(enumerate(self.features), desc="convert examples to features"):
-                    if ex_index % 10000 == 0:
-                        logger.info("Writing example %d of %d" % (ex_index, len(examples)))
-
-                    yield (
-                        {
-                            "example_id": 0,
-                            "input_ids": ex.input_ids,
-                            "attention_mask": ex.attention_mask,
-                            "token_type_ids": ex.token_type_ids,
-                        },
-                        ex.label,
-                    )
-
-            self.dataset = tf.data.Dataset.from_generator(
-                gen,
-                (
-                    {
-                        "example_id": tf.int32,
-                        "input_ids": tf.int32,
-                        "attention_mask": tf.int32,
-                        "token_type_ids": tf.int32,
-                    },
-                    tf.int64,
-                ),
-                (
-                    {
-                        "example_id": tf.TensorShape([]),
-                        "input_ids": tf.TensorShape([None, None]),
-                        "attention_mask": tf.TensorShape([None, None]),
-                        "token_type_ids": tf.TensorShape([None, None]),
-                    },
-                    tf.TensorShape([]),
-                ),
-            )
-
-        def get_dataset(self):
-            return self.dataset
-
-        def __len__(self):
-            return len(self.features)
-
-        def __getitem__(self, i) -> InputFeatures:
-            return self.features[i]
-
-
-class HansProcessor(DataProcessor):
-    """Processor for the HANS data set."""
+class DataProcessor(object):
+    """Base class for data converters for sequence classification data sets."""
 
     def get_example_from_tensor_dict(self, tensor_dict):
-        """See base class."""
-        return InputExample(
-            tensor_dict["idx"].numpy(),
-            tensor_dict["premise"].numpy().decode("utf-8"),
-            tensor_dict["hypothesis"].numpy().decode("utf-8"),
-            str(tensor_dict["label"].numpy()),
-        )
+        """Gets an example from a dict with tensorflow tensors
+
+        Args:
+            tensor_dict: Keys and values should match the corresponding Glue
+                tensorflow_dataset examples.
+        """
+        raise NotImplementedError()
 
     def get_train_examples(self, data_dir):
-        """See base class."""
-        return self._create_examples(self._read_tsv(os.path.join(data_dir, "heuristics_train_set.txt")), "train")
+        """Gets a collection of `InputExample`s for the train set."""
+        raise NotImplementedError()
 
     def get_dev_examples(self, data_dir):
-        """See base class."""
-        return self._create_examples(self._read_tsv(os.path.join(data_dir, "heuristics_evaluation_set.txt")), "dev")
+        """Gets a collection of `InputExample`s for the dev set."""
+        raise NotImplementedError()
 
     def get_labels(self):
-        """See base class."""
-        return ["contradiction", "entailment", "neutral"]
+        """Gets the list of labels for this data set."""
+        raise NotImplementedError()
 
-    def _create_examples(self, lines, set_type):
-        """Creates examples for the training and dev sets."""
-        examples = []
-        for (i, line) in enumerate(lines):
-            if i == 0:
-                continue
-            guid = "%s-%s" % (set_type, line[0])
-            text_a = line[5]
-            text_b = line[6]
-            pairID = line[7][2:] if line[7].startswith("ex") else line[7]
-            label = line[-1]
-            examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label, pairID=pairID))
-        return examples
-
-
-def hans_convert_examples_to_features(
-    examples: List[InputExample],
-    label_list: List[str],
-    max_length: int,
-    tokenizer: PreTrainedTokenizer,
-    output_mode: str,
-):
-    """
-    Loads a data file into a list of ``InputFeatures``
-
-    Args:
-        examples: List of ``InputExamples`` containing the examples.
-        tokenizer: Instance of a tokenizer that will tokenize the examples.
-        max_length: Maximum example length.
-        label_list: List of labels. Can be obtained from the processor using the ``processor.get_labels()`` method.
-        output_mode: String indicating the output mode. Either ``regression`` or ``classification``.
-
-    Returns:
-        A list of task-specific ``InputFeatures`` which can be fed to the model.
-
-    """
-
-    label_map = {label: i for i, label in enumerate(label_list)}
-
-    features = []
-    for (ex_index, example) in tqdm.tqdm(enumerate(examples), desc="convert examples to features"):
-        if ex_index % 10000 == 0:
-            logger.info("Writing example %d" % (ex_index))
-
-        inputs = tokenizer.encode_plus(
-            example.text_a,
-            example.text_b,
-            add_special_tokens=True,
-            max_length=max_length,
-            pad_to_max_length=True,
-            return_overflowing_tokens=True,
-        )
-        if "num_truncated_tokens" in inputs and inputs["num_truncated_tokens"] > 0:
-            logger.info(
-                "Attention! you are cropping tokens (swag task is ok). "
-                "If you are training ARC and RACE and you are poping question + options,"
-                "you need to try to use a bigger max seq length!"
-            )
-
-        if output_mode == "classification":
-            label = label_map[example.label] if example.label in label_map else 0
-        elif output_mode == "regression":
-            label = float(example.label)
-        else:
-            raise KeyError(output_mode)
-
-        pairID = int(example.pairID)
-
-        features.append(InputFeatures(**inputs, label=label, pairID=pairID))
-
-    for i, example in enumerate(examples[:5]):
-        logger.info("*** Example ***")
-        logger.info(f"guid: {example}")
-        logger.info(f"features: {features[i]}")
-
-    return features
-
-
-hans_tasks_num_labels = {
-    "hans": 3,
-}
-
-hans_processors = {
-    "hans": HansProcessor,
-}
-
-hans_output_modes = {
-    "hans": "classification",
-}
+    @classmethod
+    def _read_tsv(cls, input_file, quotechar=None):
+        """Reads a tab separated value file."""
+        with open(input_file, "r", encoding="utf-8-sig") as f:
+            reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
+            lines = []
+            for line in reader:
+                lines.append(line)
+            return lines

examples/benchmarking/plot_csv_file.py

@@ -3,9 +3,9 @@ from collections import defaultdict
 from dataclasses import dataclass, field
 from typing import Optional
 
-import matplotlib.pyplot as plt
 import numpy as np
 
+import matplotlib.pyplot as plt
 from transformers import HfArgumentParser
 
 

examples/bertology/run_bertology.py

@@ -34,8 +34,8 @@ from transformers import (
     AutoConfig,
     AutoModelForSequenceClassification,
     AutoTokenizer,
+    DefaultDataCollator,
     GlueDataset,
-    default_data_collator,
     glue_compute_metrics,
     glue_output_modes,
     glue_processors,
@@ -424,7 +424,7 @@ def main():
         eval_dataset = Subset(eval_dataset, list(range(min(args.data_subset, len(eval_dataset)))))
     eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)
     eval_dataloader = DataLoader(
-        eval_dataset, sampler=eval_sampler, batch_size=args.batch_size, collate_fn=default_data_collator
+        eval_dataset, sampler=eval_sampler, batch_size=args.batch_size, collate_fn=DefaultDataCollator().collate_batch
     )
 
     # Compute head entropy and importance score

examples/contrib/mm-imdb/run_mmimdb.py

@@ -34,11 +34,26 @@ from tqdm import tqdm, trange
 from transformers import (
     WEIGHTS_NAME,
     AdamW,
-    AutoConfig,
-    AutoModel,
-    AutoTokenizer,
+    AlbertConfig,
+    AlbertModel,
+    AlbertTokenizer,
+    BertConfig,
+    BertModel,
+    BertTokenizer,
+    DistilBertConfig,
+    DistilBertModel,
+    DistilBertTokenizer,
     MMBTConfig,
     MMBTForClassification,
+    RobertaConfig,
+    RobertaModel,
+    RobertaTokenizer,
+    XLMConfig,
+    XLMModel,
+    XLMTokenizer,
+    XLNetConfig,
+    XLNetModel,
+    XLNetTokenizer,
     get_linear_schedule_with_warmup,
 )
 from utils_mmimdb import ImageEncoder, JsonlDataset, collate_fn, get_image_transforms, get_mmimdb_labels
@@ -52,6 +67,23 @@ except ImportError:
 
 logger = logging.getLogger(__name__)
 
+ALL_MODELS = sum(
+    (
+        tuple(conf.pretrained_config_archive_map.keys())
+        for conf in (BertConfig, XLNetConfig, XLMConfig, RobertaConfig, DistilBertConfig)
+    ),
+    (),
+)
+
+MODEL_CLASSES = {
+    "bert": (BertConfig, BertModel, BertTokenizer),
+    "xlnet": (XLNetConfig, XLNetModel, XLNetTokenizer),
+    "xlm": (XLMConfig, XLMModel, XLMTokenizer),
+    "roberta": (RobertaConfig, RobertaModel, RobertaTokenizer),
+    "distilbert": (DistilBertConfig, DistilBertModel, DistilBertTokenizer),
+    "albert": (AlbertConfig, AlbertModel, AlbertTokenizer),
+}
+
 
 def set_seed(args):
     random.seed(args.seed)
@@ -319,12 +351,19 @@ def main():
         required=True,
         help="The input data dir. Should contain the .jsonl files for MMIMDB.",
     )
+    parser.add_argument(
+        "--model_type",
+        default=None,
+        type=str,
+        required=True,
+        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
+    )
     parser.add_argument(
         "--model_name_or_path",
         default=None,
         type=str,
         required=True,
-        help="Path to pretrained model or model identifier from huggingface.co/models",
+        help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS),
     )
     parser.add_argument(
         "--output_dir",
@@ -346,7 +385,7 @@ def main():
     )
     parser.add_argument(
         "--cache_dir",
-        default=None,
+        default="",
         type=str,
         help="Where do you want to store the pre-trained models downloaded from s3",
     )
@@ -487,14 +526,18 @@ def main():
     # Setup model
     labels = get_mmimdb_labels()
     num_labels = len(labels)
-    transformer_config = AutoConfig.from_pretrained(args.config_name if args.config_name else args.model_name_or_path)
-    tokenizer = AutoTokenizer.from_pretrained(
+    args.model_type = args.model_type.lower()
+    config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
+    transformer_config = config_class.from_pretrained(
+        args.config_name if args.config_name else args.model_name_or_path
+    )
+    tokenizer = tokenizer_class.from_pretrained(
         args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
         do_lower_case=args.do_lower_case,
-        cache_dir=args.cache_dir,
+        cache_dir=args.cache_dir if args.cache_dir else None,
     )
-    transformer = AutoModel.from_pretrained(
-        args.model_name_or_path, config=transformer_config, cache_dir=args.cache_dir
+    transformer = model_class.from_pretrained(
+        args.model_name_or_path, config=transformer_config, cache_dir=args.cache_dir if args.cache_dir else None
     )
     img_encoder = ImageEncoder(args)
     config = MMBTConfig(transformer_config, num_labels=num_labels)
@@ -540,12 +583,13 @@ def main():
         # Load a trained model and vocabulary that you have fine-tuned
         model = MMBTForClassification(config, transformer, img_encoder)
         model.load_state_dict(torch.load(os.path.join(args.output_dir, WEIGHTS_NAME)))
-        tokenizer = AutoTokenizer.from_pretrained(args.output_dir)
+        tokenizer = tokenizer_class.from_pretrained(args.output_dir)
         model.to(args.device)
 
     # Evaluation
     results = {}
     if args.do_eval and args.local_rank in [-1, 0]:
+        tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
         checkpoints = [args.output_dir]
         if args.eval_all_checkpoints:
             checkpoints = list(

examples/contrib/run_swag.py

@@ -31,8 +31,14 @@ from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, Tenso
 from torch.utils.data.distributed import DistributedSampler
 from tqdm import tqdm, trange
 
-from transformers import WEIGHTS_NAME, AdamW, AutoConfig, AutoTokenizer, get_linear_schedule_with_warmup
-from transformers.modeling_auto import AutoModelForMultipleChoice
+from transformers import (
+    WEIGHTS_NAME,
+    AdamW,
+    BertConfig,
+    BertForMultipleChoice,
+    BertTokenizer,
+    get_linear_schedule_with_warmup,
+)
 
 
 try:
@@ -43,6 +49,12 @@ except ImportError:
 
 logger = logging.getLogger(__name__)
 
+ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in [BertConfig]), ())
+
+MODEL_CLASSES = {
+    "bert": (BertConfig, BertForMultipleChoice, BertTokenizer),
+}
+
 
 class SwagExample(object):
     """A single training/test example for the SWAG dataset."""
@@ -480,12 +492,19 @@ def main():
         required=True,
         help="SWAG csv for predictions. E.g., val.csv or test.csv",
     )
+    parser.add_argument(
+        "--model_type",
+        default=None,
+        type=str,
+        required=True,
+        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
+    )
     parser.add_argument(
         "--model_name_or_path",
         default=None,
         type=str,
         required=True,
-        help="Path to pretrained model or model identifier from huggingface.co/models",
+        help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS),
     )
     parser.add_argument(
         "--output_dir",
@@ -517,6 +536,9 @@ def main():
     parser.add_argument(
         "--evaluate_during_training", action="store_true", help="Rul evaluation during training at each logging step."
     )
+    parser.add_argument(
+        "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model."
+    )
 
     parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.")
     parser.add_argument(
@@ -630,9 +652,13 @@ def main():
     if args.local_rank not in [-1, 0]:
         torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
 
-    config = AutoConfig.from_pretrained(args.config_name if args.config_name else args.model_name_or_path)
-    tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,)
-    model = AutoModelForMultipleChoice.from_pretrained(
+    args.model_type = args.model_type.lower()
+    config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
+    config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path)
+    tokenizer = tokenizer_class.from_pretrained(
+        args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case
+    )
+    model = model_class.from_pretrained(
         args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config
     )
 
@@ -668,8 +694,8 @@ def main():
         torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
 
         # Load a trained model and vocabulary that you have fine-tuned
-        model = AutoModelForMultipleChoice.from_pretrained(args.output_dir)
-        tokenizer = AutoTokenizer.from_pretrained(args.output_dir)
+        model = model_class.from_pretrained(args.output_dir)
+        tokenizer = tokenizer_class.from_pretrained(args.output_dir)
         model.to(args.device)
 
     # Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory
@@ -692,8 +718,8 @@ def main():
         for checkpoint in checkpoints:
             # Reload the model
             global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
-            model = AutoModelForMultipleChoice.from_pretrained(checkpoint)
-            tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+            model = model_class.from_pretrained(checkpoint)
+            tokenizer = tokenizer_class.from_pretrained(checkpoint)
             model.to(args.device)
 
             # Evaluate

examples/distillation/run_squad_w_distillation.py

@@ -67,6 +67,9 @@ except ImportError:
 
 logger = logging.getLogger(__name__)
 
+ALL_MODELS = sum(
+    (tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, XLNetConfig, XLMConfig)), ()
+)
 
 MODEL_CLASSES = {
     "bert": (BertConfig, BertForQuestionAnswering, BertTokenizer),
@@ -502,7 +505,7 @@ def main():
         default=None,
         type=str,
         required=True,
-        help="Path to pretrained model or model identifier from huggingface.co/models",
+        help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS),
     )
     parser.add_argument(
         "--output_dir",

examples/movement-pruning/README.md

@@ -1,183 +0,0 @@
-# Movement Pruning: Adaptive Sparsity by Fine-Tuning
-
-*Magnitude pruning is a widely used strategy for reducing model size in pure supervised learning; however, it is less effective in the transfer learning regime that has become standard for state-of-the-art natural language processing applications. We propose the use of *movement pruning*, a simple, deterministic first-order weight pruning method that is more adaptive to pretrained model fine-tuning. Experiments show that when pruning large pretrained language models, movement pruning shows significant improvements in high-sparsity regimes. When combined with distillation, the approach achieves minimal accuracy loss with down to only 3% of the model parameters:*
-
-| Fine-pruning+Distillation<br>(Teacher=BERT-base fine-tuned) | BERT base<br>fine-tuned | Remaining<br>Weights (%) | Magnitude Pruning      | L0 Regularization      | Movement Pruning       | Soft Movement Pruning          |
-| :---:                                                       | :---:                   | :---:                    | :---:                  | :---:                  | :---:                  | :---:                          |
-| SQuAD - Dev<br>EM/F1                                        | 80.4/88.1               | 10%<br>3%                | 70.2/80.1<br>45.5/59.6 | 72.4/81.9<br>64.3/75.8 | 75.6/84.3<br>67.5/78.0 | **76.6/84.9**<br>**72.7/82.3** |
-| MNLI - Dev<br>acc/MM acc                                    | 84.5/84.9               | 10%<br>3%                | 78.3/79.3<br>69.4/70.6 | 78.7/79.7<br>76.0/76.2 | 80.1/80.4<br>76.5/77.4 | **81.2/81.8**<br>**79.5/80.1** |
-| QQP - Dev<br>acc/F1                                         | 91.4/88.4               | 10%<br>3%                | 79.8/65.0<br>72.4/57.8 | 88.1/82.8<br>87.0/81.9 | 89.7/86.2<br>86.1/81.5 | **90.2/86.8**<br>**89.1/85.5** |
-
-This page contains information on how to fine-prune pre-trained models such as `BERT` to obtain extremely sparse models with movement pruning. In contrast to magnitude pruning which selects weights that are far from 0, movement pruning retains weights that are moving away from 0.
-
-For more information, we invite you to check out [our paper](https://arxiv.org/abs/2005.07683).
-You can also have a look at this fun *Explain Like I'm Five* introductory [slide deck](https://www.slideshare.net/VictorSanh/movement-pruning-explain-like-im-five-234205241).
-
-<div align="center">
-<img src="https://www.seekpng.com/png/detail/166-1669328_how-to-make-emmental-cheese-at-home-icooker.png" width="400">
-</div>
-
-## Extreme sparsity and efficient storage
-
-One promise of extreme pruning is to obtain extremely small models that can be easily sent (and stored) on edge devices. By setting weights to 0., we reduce the amount of information we need to store, and thus decreasing the memory size. We are able to obtain extremely sparse fine-pruned models with movement pruning: ~95% of the dense performance with ~5% of total remaining weights in the BERT encoder.
-
-In [this notebook](https://github.com/huggingface/transformers/blob/master/examples/movement-pruning/Saving_PruneBERT.ipynb), we showcase how we can leverage standard tools that exist out-of-the-box to efficiently store an extremely sparse question answering model (only 6% of total remaining weights in the encoder). We are able to reduce the memory size of the encoder **from the 340MB (the orignal dense BERT) to 11MB**, without any additional training of the model (every operation is performed *post fine-pruning*). It is sufficiently small to store it on a [91' floppy disk](https://en.wikipedia.org/wiki/Floptical) 📎!
-
-While movement pruning does not directly optimize for memory footprint (but rather the number of non-null weights), we hypothetize that further memory compression ratios can be achieved with specific quantization aware trainings (see for instance [Q8BERT](https://arxiv.org/abs/1910.06188), [And the Bit Goes Down](https://arxiv.org/abs/1907.05686) or [Quant-Noise](https://arxiv.org/abs/2004.07320)).
-
-## Fine-pruned models
-
-As examples, we release two English PruneBERT checkpoints (models fine-pruned from a pre-trained `BERT` checkpoint), one on SQuAD and the other on MNLI.
-
-- **`prunebert-base-uncased-6-finepruned-w-distil-squad`**<br/>
-Pre-trained `BERT-base-uncased` fine-pruned with soft movement pruning on SQuAD v1.1. We use an additional distillation signal from `BERT-base-uncased` finetuned on SQuAD. The encoder counts 6% of total non-null weights and reaches 83.8 F1 score. The model can be accessed with: `pruned_bert = BertForQuestionAnswering.from_pretrained("huggingface/prunebert-base-uncased-6-finepruned-w-distil-squad")`
-- **`prunebert-base-uncased-6-finepruned-w-distil-mnli`**<br/>
-Pre-trained `BERT-base-uncased` fine-pruned with soft movement pruning on MNLI. We use an additional distillation signal from `BERT-base-uncased` finetuned on MNLI. The encoder counts 6% of total non-null weights and reaches 80.7 (matched) accuracy. The model can be accessed with: `pruned_bert = BertForSequenceClassification.from_pretrained("huggingface/prunebert-base-uncased-6-finepruned-w-distil-mnli")`
-
-## How to fine-prune?
-
-### Setup
-
-The code relies on the 🤗 Transformers library. In addition to the dependencies listed in the [`examples`](https://github.com/huggingface/transformers/tree/master/examples) folder, you should install a few additional dependencies listed in the `requirements.txt` file: `pip install -r requirements.txt`.
-
-Note that we built our experiments on top of a stabilized version of the library (commit https://github.com/huggingface/transformers/commit/352d5472b0c1dec0f420d606d16747d851b4bda8): we do not guarantee that everything is still compatible with the latest version of the master branch.
-
-### Fine-pruning with movement pruning
-
-Below, we detail how to reproduce the results reported in the paper. We use SQuAD as a running example. Commands (and scripts) can be easily adapted for other tasks.
-
-The following command fine-prunes a pre-trained `BERT-base` on SQuAD using movement pruning towards 15% of remaining weights (85% sparsity). Note that we freeze all the embeddings modules (from their pre-trained value) and only prune the Fully Connected layers in the encoder (12 layers of Transformer Block).
-
-```bash
-SERIALIZATION_DIR=<OUTPUT_DIR>
-SQUAD_DATA=<SQUAD_DATA>
-
-python examples/movement-pruning/masked_run_squad.py \
-    --output_dir $SERIALIZATION_DIR \
-    --data_dir $SQUAD_DATA \
-    --train_file train-v1.1.json \
-    --predict_file dev-v1.1.json \
-    --do_train --do_eval --do_lower_case \
-    --model_type masked_bert \
-    --model_name_or_path bert-base-uncased \
-    --per_gpu_train_batch_size 16 \
-    --warmup_steps 5400 \
-    --num_train_epochs 10 \
-    --learning_rate 3e-5 --mask_scores_learning_rate 1e-2 \
-    --initial_threshold 1 --final_threshold 0.15 \
-    --initial_warmup 1 --final_warmup 2 \
-    --pruning_method topK --mask_init constant --mask_scale 0.
-```
-
-### Fine-pruning with other methods
-
-We can also explore other fine-pruning methods by changing the `pruning_method` parameter:
-
-Soft movement pruning
-```bash
-python examples/movement-pruning/masked_run_squad.py \
-    --output_dir $SERIALIZATION_DIR \
-    --data_dir $SQUAD_DATA \
-    --train_file train-v1.1.json \
-    --predict_file dev-v1.1.json \
-    --do_train --do_eval --do_lower_case \
-    --model_type masked_bert \
-    --model_name_or_path bert-base-uncased \
-    --per_gpu_train_batch_size 16 \
-    --warmup_steps 5400 \
-    --num_train_epochs 10 \
-    --learning_rate 3e-5 --mask_scores_learning_rate 1e-2 \
-    --initial_threshold 0 --final_threshold 0.1 \
-    --initial_warmup 1 --final_warmup 2 \
-    --pruning_method sigmoied_threshold --mask_init constant --mask_scale 0. \
-    --regularization l1 --final_lambda 400.
-```
-
-L0 regularization
-```bash
-python examples/movement-pruning/masked_run_squad.py \
-    --output_dir $SERIALIZATION_DIR \
-    --data_dir $SQUAD_DATA \
-    --train_file train-v1.1.json \
-    --predict_file dev-v1.1.json \
-    --do_train --do_eval --do_lower_case \
-    --model_type masked_bert \
-    --model_name_or_path bert-base-uncased \
-    --per_gpu_train_batch_size 16 \
-    --warmup_steps 5400 \
-    --num_train_epochs 10 \
-    --learning_rate 3e-5 --mask_scores_learning_rate 1e-1 \
-    --initial_threshold 1. --final_threshold 1. \
-    --initial_warmup 1 --final_warmup 1 \
-    --pruning_method l0 --mask_init constant --mask_scale 2.197 \
-    --regularization l0 --final_lambda 125.
-```
-
-Iterative Magnitude Pruning
-```bash
-python examples/movement-pruning/masked_run_squad.py \
-    --output_dir ./dbg \
-    --data_dir examples/distillation/data/squad_data \
-    --train_file train-v1.1.json \
-    --predict_file dev-v1.1.json \
-    --do_train --do_eval --do_lower_case \
-    --model_type masked_bert \
-    --model_name_or_path bert-base-uncased \
-    --per_gpu_train_batch_size 16 \
-    --warmup_steps 5400 \
-    --num_train_epochs 10 \
-    --learning_rate 3e-5 \
-    --initial_threshold 1 --final_threshold 0.15 \
-    --initial_warmup 1 --final_warmup 2 \
-    --pruning_method magnitude
-```
-
-### After fine-pruning
-
-**Counting parameters**
-
-Regularization based pruning methods (soft movement pruning and L0 regularization) rely on the penalty to induce sparsity. The multiplicative coefficient controls the sparsity level.
-To obtain the effective sparsity level in the encoder, we simply count the number of activated (non-null) weights:
-
-```bash
-python examples/movement-pruning/count_parameters.py \
-    --pruning_method sigmoied_threshold \
-    --threshold 0.1 \
-    --serialization_dir $SERIALIZATION_DIR
-```
-
-**Pruning once for all**
-
-Once the model has been fine-pruned, the pruned weights can be set to 0. once for all (reducing the amount of information to store). In our running experiments, we can convert a `MaskedBertForQuestionAnswering` (a BERT model augmented to enable on-the-fly pruning capabilities) to a standard `BertForQuestionAnswering`:
-
-```bash
-python examples/movement-pruning/bertarize.py \
-    --pruning_method sigmoied_threshold \
-    --threshold 0.1 \
-    --model_name_or_path $SERIALIZATION_DIR
-```
-
-## Hyper-parameters
-
-For reproducibility purposes, we share the detailed results presented in the paper. These [tables](https://docs.google.com/spreadsheets/d/17JgRq_OFFTniUrz6BZWW_87DjFkKXpI1kYDSsseT_7g/edit?usp=sharing) exhaustively describe the individual hyper-parameters used for each data point.
-
-## Inference speed
-
-Early experiments show that even though models fine-pruned with (soft) movement pruning are extremely sparse, they do not benefit from significant improvement in terms of inference speed when using the standard PyTorch inference.
-We are currently benchmarking and exploring inference setups specifically for sparse architectures.
-In particular, hardware manufacturers are announcing devices that will speedup inference for sparse networks considerably.
-
-## Citation
-
-If you find this resource useful, please consider citing the following paper:
-
-```
-@article{sanh2020movement,
-    title={Movement Pruning: Adaptive Sparsity by Fine-Tuning},
-    author={Victor Sanh and Thomas Wolf and Alexander M. Rush},
-    year={2020},
-    eprint={2005.07683},
-    archivePrefix={arXiv},
-    primaryClass={cs.CL}
-}
-```

examples/movement-pruning/Saving_PruneBERT.ipynb

@@ -1,634 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Saving PruneBERT\n",
-    "\n",
-    "\n",
-    "This notebook aims at showcasing how we can leverage standard tools to save (and load) an extremely sparse model fine-pruned with [movement pruning](https://arxiv.org/abs/2005.07683) (or any other unstructured pruning mehtod).\n",
-    "\n",
-    "In this example, we used BERT (base-uncased, but the procedure described here is not specific to BERT and can be applied to a large variety of models.\n",
-    "\n",
-    "We first obtain an extremely sparse model by fine-pruning with movement pruning on SQuAD v1.1. We then used the following combination of standard tools:\n",
-    "- We reduce the precision of the model with Int8 dynamic quantization using [PyTorch implementation](https://pytorch.org/tutorials/intermediate/dynamic_quantization_bert_tutorial.html). We only quantized the Fully Connected Layers.\n",
-    "- Sparse quantized matrices are converted into the [Compressed Sparse Row format](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html).\n",
-    "- We use HDF5 with `gzip` compression to store the weights.\n",
-    "\n",
-    "We experiment with a question answering model with only 6% of total remaining weights in the encoder (previously obtained with movement pruning). **We are able to reduce the memory size of the encoder from 340MB (original dense BERT) to 11MB**, which fits on a [91' floppy disk](https://en.wikipedia.org/wiki/Floptical)!\n",
-    "\n",
-    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/0/00/Floptical_disk_21MB.jpg/440px-Floptical_disk_21MB.jpg\" width=\"200\">\n",
-    "\n",
-    "*Note: this notebook is compatible with `torch>=1.5.0` If you are using, `torch==1.4.0`, please refer to [this previous version of the notebook](https://github.com/huggingface/transformers/commit/b11386e158e86e62d4041eabd86d044cd1695737).*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Includes\n",
-    "\n",
-    "import h5py\n",
-    "import os\n",
-    "import json\n",
-    "from collections import OrderedDict\n",
-    "\n",
-    "from scipy import sparse\n",
-    "import numpy as np\n",
-    "\n",
-    "import torch\n",
-    "from torch import nn\n",
-    "\n",
-    "from transformers import *\n",
-    "\n",
-    "os.chdir('../../')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Saving"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Dynamic quantization induces little or no loss of performance while significantly reducing the memory footprint."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Load fine-pruned model and quantize the model\n",
-    "\n",
-    "model = BertForQuestionAnswering.from_pretrained(\"huggingface/prunebert-base-uncased-6-finepruned-w-distil-squad\")\n",
-    "model.to('cpu')\n",
-    "\n",
-    "quantized_model = torch.quantization.quantize_dynamic(\n",
-    "                    model=model,\n",
-    "                    qconfig_spec = {\n",
-    "                        torch.nn.Linear : torch.quantization.default_dynamic_qconfig,\n",
-    "                    },\n",
-    "                    dtype=torch.qint8,\n",
-    "                )\n",
-    "# print(quantized_model)\n",
-    "\n",
-    "qtz_st = quantized_model.state_dict()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Saving the original (encoder + classifier) in the standard torch.save format\n",
-    "\n",
-    "dense_st = {name: param for name, param in model.state_dict().items() \n",
-    "                            if \"embedding\" not in name and \"pooler\" not in name}\n",
-    "torch.save(dense_st, 'dbg/dense_squad.pt',)\n",
-    "dense_mb_size = os.path.getsize(\"dbg/dense_squad.pt\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Decompose quantization for bert.encoder.layer.0.attention.self.query._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.0.attention.self.key._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.0.attention.self.value._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.0.attention.output.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.0.intermediate.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.0.output.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.1.attention.self.query._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.1.attention.self.key._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.1.attention.self.value._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.1.attention.output.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.1.intermediate.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.1.output.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.2.attention.self.query._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.2.attention.self.key._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.2.attention.self.value._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.2.attention.output.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.2.intermediate.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.2.output.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.3.attention.self.query._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.3.attention.self.key._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.3.attention.self.value._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.3.attention.output.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.3.intermediate.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.3.output.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.4.attention.self.query._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.4.attention.self.key._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.4.attention.self.value._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.4.attention.output.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.4.intermediate.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.4.output.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.5.attention.self.query._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.5.attention.self.key._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.5.attention.self.value._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.5.attention.output.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.5.intermediate.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.5.output.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.6.attention.self.query._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.6.attention.self.key._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.6.attention.self.value._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.6.attention.output.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.6.intermediate.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.6.output.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.7.attention.self.query._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.7.attention.self.key._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.7.attention.self.value._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.7.attention.output.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.7.intermediate.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.7.output.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.8.attention.self.query._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.8.attention.self.key._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.8.attention.self.value._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.8.attention.output.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.8.intermediate.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.8.output.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.9.attention.self.query._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.9.attention.self.key._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.9.attention.self.value._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.9.attention.output.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.9.intermediate.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.9.output.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.10.attention.self.query._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.10.attention.self.key._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.10.attention.self.value._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.10.attention.output.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.10.intermediate.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.10.output.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.11.attention.self.query._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.11.attention.self.key._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.11.attention.self.value._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.11.attention.output.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.11.intermediate.dense._packed_params.weight\n",
-      "Decompose quantization for bert.encoder.layer.11.output.dense._packed_params.weight\n",
-      "Decompose quantization for bert.pooler.dense._packed_params.weight\n",
-      "Decompose quantization for qa_outputs._packed_params.weight\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Elementary representation: we decompose the quantized tensors into (scale, zero_point, int_repr).\n",
-    "# See https://pytorch.org/docs/stable/quantization.html\n",
-    "\n",
-    "# We further leverage the fact that int_repr is sparse matrix to optimize the storage: we decompose int_repr into\n",
-    "# its CSR representation (data, indptr, indices).\n",
-    "\n",
-    "elementary_qtz_st = {}\n",
-    "for name, param in qtz_st.items():\n",
-    "    if \"dtype\" not in name and param.is_quantized:\n",
-    "        print(\"Decompose quantization for\", name)\n",
-    "        # We need to extract the scale, the zero_point and the int_repr for the quantized tensor and modules\n",
-    "        scale = param.q_scale()                                # torch.tensor(1,) - float32\n",
-    "        zero_point = param.q_zero_point()                      # torch.tensor(1,) - int32\n",
-    "        elementary_qtz_st[f\"{name}.scale\"] = scale\n",
-    "        elementary_qtz_st[f\"{name}.zero_point\"] = zero_point\n",
-    "\n",
-    "        # We assume the int_repr is sparse and compute its CSR representation\n",
-    "        # Only the FCs in the encoder are actually sparse\n",
-    "        int_repr = param.int_repr()                         # torch.tensor(nb_rows, nb_columns) - int8\n",
-    "        int_repr_cs = sparse.csr_matrix(int_repr)           # scipy.sparse.csr.csr_matrix\n",
-    "\n",
-    "        elementary_qtz_st[f\"{name}.int_repr.data\"] = int_repr_cs.data                  # np.array int8\n",
-    "        elementary_qtz_st[f\"{name}.int_repr.indptr\"] = int_repr_cs.indptr              # np.array int32\n",
-    "        assert max(int_repr_cs.indices) < 65535 # If not, we shall fall back to int32\n",
-    "        elementary_qtz_st[f\"{name}.int_repr.indices\"] = np.uint16(int_repr_cs.indices) # np.array uint16\n",
-    "        elementary_qtz_st[f\"{name}.int_repr.shape\"] = int_repr_cs.shape                # tuple(int, int)\n",
-    "    else:\n",
-    "        elementary_qtz_st[name] = param\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create mapping from torch.dtype to string description (we could also used an int8 instead of string)\n",
-    "str_2_dtype = {\"qint8\": torch.qint8}\n",
-    "dtype_2_str = {torch.qint8: \"qint8\"}\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Encoder Size (MB) - Sparse & Quantized - `torch.save`: 21.29\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Saving the pruned (encoder + classifier) in the standard torch.save format\n",
-    "\n",
-    "dense_optimized_st = {name: param for name, param in elementary_qtz_st.items() \n",
-    "                                    if \"embedding\" not in name and \"pooler\" not in name}\n",
-    "torch.save(dense_optimized_st, 'dbg/dense_squad_optimized.pt',)\n",
-    "print(\"Encoder Size (MB) - Sparse & Quantized - `torch.save`:\",\n",
-    "      round(os.path.getsize(\"dbg/dense_squad_optimized.pt\")/1e6, 2))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Skip bert.embeddings.word_embeddings.weight\n",
-      "Skip bert.embeddings.position_embeddings.weight\n",
-      "Skip bert.embeddings.token_type_embeddings.weight\n",
-      "Skip bert.embeddings.LayerNorm.weight\n",
-      "Skip bert.embeddings.LayerNorm.bias\n",
-      "Skip bert.pooler.dense.scale\n",
-      "Skip bert.pooler.dense.zero_point\n",
-      "Skip bert.pooler.dense._packed_params.weight.scale\n",
-      "Skip bert.pooler.dense._packed_params.weight.zero_point\n",
-      "Skip bert.pooler.dense._packed_params.weight.int_repr.data\n",
-      "Skip bert.pooler.dense._packed_params.weight.int_repr.indptr\n",
-      "Skip bert.pooler.dense._packed_params.weight.int_repr.indices\n",
-      "Skip bert.pooler.dense._packed_params.weight.int_repr.shape\n",
-      "Skip bert.pooler.dense._packed_params.bias\n",
-      "Skip bert.pooler.dense._packed_params.dtype\n",
-      "\n",
-      "Encoder Size (MB) - Dense:              340.26\n",
-      "Encoder Size (MB) - Sparse & Quantized: 11.28\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Save the decomposed state_dict with an HDF5 file\n",
-    "# Saving only the encoder + QA Head\n",
-    "\n",
-    "with h5py.File('dbg/squad_sparse.h5','w') as hf:\n",
-    "    for name, param in elementary_qtz_st.items():\n",
-    "        if \"embedding\" in name:\n",
-    "            print(f\"Skip {name}\")\n",
-    "            continue\n",
-    "\n",
-    "        if \"pooler\" in name:\n",
-    "            print(f\"Skip {name}\")\n",
-    "            continue\n",
-    "\n",
-    "        if type(param) == torch.Tensor:\n",
-    "            if param.numel() == 1:\n",
-    "                # module scale\n",
-    "                # module zero_point\n",
-    "                hf.attrs[name] = param\n",
-    "                continue\n",
-    "\n",
-    "            if param.requires_grad:\n",
-    "                # LayerNorm\n",
-    "                param = param.detach().numpy()\n",
-    "            hf.create_dataset(name, data=param, compression=\"gzip\", compression_opts=9)\n",
-    "\n",
-    "        elif type(param) == float or type(param) == int or type(param) == tuple:\n",
-    "            # float - tensor _packed_params.weight.scale\n",
-    "            # int   - tensor _packed_params.weight.zero_point\n",
-    "            # tuple - tensor _packed_params.weight.shape\n",
-    "            hf.attrs[name] = param\n",
-    "\n",
-    "        elif type(param) == torch.dtype:\n",
-    "            # dtype - tensor _packed_params.dtype\n",
-    "            hf.attrs[name] = dtype_2_str[param]\n",
-    "            \n",
-    "        else:\n",
-    "            hf.create_dataset(name, data=param, compression=\"gzip\", compression_opts=9)\n",
-    "\n",
-    "\n",
-    "with open('dbg/metadata.json', 'w') as f:\n",
-    "    f.write(json.dumps(qtz_st._metadata))  \n",
-    "\n",
-    "size = os.path.getsize(\"dbg/squad_sparse.h5\") + os.path.getsize(\"dbg/metadata.json\")\n",
-    "print(\"\")\n",
-    "print(\"Encoder Size (MB) - Dense:             \", round(dense_mb_size/1e6, 2))\n",
-    "print(\"Encoder Size (MB) - Sparse & Quantized:\", round(size/1e6, 2))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Size (MB): 99.41\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Save the decomposed state_dict to HDF5 storage\n",
-    "# Save everything in the architecutre (embedding + encoder + QA Head)\n",
-    "\n",
-    "with h5py.File('dbg/squad_sparse_with_embs.h5','w') as hf:\n",
-    "    for name, param in elementary_qtz_st.items():\n",
-    "#         if \"embedding\" in name:\n",
-    "#             print(f\"Skip {name}\")\n",
-    "#             continue\n",
-    "\n",
-    "#         if \"pooler\" in name:\n",
-    "#             print(f\"Skip {name}\")\n",
-    "#             continue\n",
-    "\n",
-    "        if type(param) == torch.Tensor:\n",
-    "            if param.numel() == 1:\n",
-    "                # module scale\n",
-    "                # module zero_point\n",
-    "                hf.attrs[name] = param\n",
-    "                continue\n",
-    "\n",
-    "            if param.requires_grad:\n",
-    "                # LayerNorm\n",
-    "                param = param.detach().numpy()\n",
-    "            hf.create_dataset(name, data=param, compression=\"gzip\", compression_opts=9)\n",
-    "\n",
-    "        elif type(param) == float or type(param) == int or type(param) == tuple:\n",
-    "            # float - tensor _packed_params.weight.scale\n",
-    "            # int   - tensor _packed_params.weight.zero_point\n",
-    "            # tuple - tensor _packed_params.weight.shape\n",
-    "            hf.attrs[name] = param\n",
-    "\n",
-    "        elif type(param) == torch.dtype:\n",
-    "            # dtype - tensor _packed_params.dtype\n",
-    "            hf.attrs[name] = dtype_2_str[param]\n",
-    "            \n",
-    "        else:\n",
-    "            hf.create_dataset(name, data=param, compression=\"gzip\", compression_opts=9)\n",
-    "\n",
-    "\n",
-    "\n",
-    "with open('dbg/metadata.json', 'w') as f:\n",
-    "    f.write(json.dumps(qtz_st._metadata))   \n",
-    "\n",
-    "size = os.path.getsize(\"dbg/squad_sparse_with_embs.h5\") + os.path.getsize(\"dbg/metadata.json\")\n",
-    "print('\\nSize (MB):', round(size/1e6, 2))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Loading"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Reconstruct the elementary state dict\n",
-    "\n",
-    "reconstructed_elementary_qtz_st = {}\n",
-    "\n",
-    "hf = h5py.File('dbg/squad_sparse_with_embs.h5','r')\n",
-    "\n",
-    "for attr_name, attr_param in hf.attrs.items():\n",
-    "    if 'shape' in attr_name:\n",
-    "        attr_param = tuple(attr_param)\n",
-    "    elif \".scale\" in attr_name:\n",
-    "        if \"_packed_params\" in attr_name:\n",
-    "            attr_param = float(attr_param)\n",
-    "        else:\n",
-    "            attr_param = torch.tensor(attr_param)\n",
-    "    elif \".zero_point\" in attr_name:\n",
-    "        if \"_packed_params\" in attr_name:\n",
-    "            attr_param = int(attr_param)\n",
-    "        else:\n",
-    "            attr_param = torch.tensor(attr_param)\n",
-    "    elif \".dtype\" in attr_name:\n",
-    "        attr_param = str_2_dtype[attr_param]\n",
-    "    reconstructed_elementary_qtz_st[attr_name] = attr_param\n",
-    "    # print(f\"Unpack {attr_name}\")\n",
-    "    \n",
-    "# Get the tensors/arrays\n",
-    "for data_name, data_param in hf.items():\n",
-    "    if \"LayerNorm\" in data_name or \"_packed_params.bias\" in data_name:\n",
-    "        reconstructed_elementary_qtz_st[data_name] = torch.from_numpy(np.array(data_param))\n",
-    "    elif \"embedding\" in data_name:\n",
-    "        reconstructed_elementary_qtz_st[data_name] = torch.from_numpy(np.array(data_param))\n",
-    "    else: # _packed_params.weight.int_repr.data, _packed_params.weight.int_repr.indices and _packed_params.weight.int_repr.indptr\n",
-    "        data_param = np.array(data_param)\n",
-    "        if \"indices\" in data_name:\n",
-    "            data_param = np.array(data_param, dtype=np.int32)\n",
-    "        reconstructed_elementary_qtz_st[data_name] = data_param\n",
-    "    # print(f\"Unpack {data_name}\")\n",
-    "    \n",
-    "\n",
-    "hf.close()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Sanity checks\n",
-    "\n",
-    "for name, param in reconstructed_elementary_qtz_st.items():\n",
-    "    assert name in elementary_qtz_st\n",
-    "for name, param in elementary_qtz_st.items():\n",
-    "    assert name in reconstructed_elementary_qtz_st, name\n",
-    "\n",
-    "for name, param in reconstructed_elementary_qtz_st.items():\n",
-    "    assert type(param) == type(elementary_qtz_st[name]), name\n",
-    "    if type(param) == torch.Tensor:\n",
-    "        assert torch.all(torch.eq(param, elementary_qtz_st[name])), name\n",
-    "    elif type(param) == np.ndarray:\n",
-    "        assert (param == elementary_qtz_st[name]).all(), name\n",
-    "    else:\n",
-    "        assert param == elementary_qtz_st[name], name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Re-assemble the sparse int_repr from the CSR format\n",
-    "\n",
-    "reconstructed_qtz_st = {}\n",
-    "\n",
-    "for name, param in reconstructed_elementary_qtz_st.items():\n",
-    "    if \"weight.int_repr.indptr\" in name:\n",
-    "        prefix_ = name[:-16]\n",
-    "        data    = reconstructed_elementary_qtz_st[f\"{prefix_}.int_repr.data\"]\n",
-    "        indptr  = reconstructed_elementary_qtz_st[f\"{prefix_}.int_repr.indptr\"]\n",
-    "        indices = reconstructed_elementary_qtz_st[f\"{prefix_}.int_repr.indices\"]\n",
-    "        shape   = reconstructed_elementary_qtz_st[f\"{prefix_}.int_repr.shape\"]\n",
-    "\n",
-    "        int_repr = sparse.csr_matrix(arg1=(data, indices, indptr),\n",
-    "                                     shape=shape)\n",
-    "        int_repr = torch.tensor(int_repr.todense())\n",
-    "\n",
-    "        scale = reconstructed_elementary_qtz_st[f\"{prefix_}.scale\"]\n",
-    "        zero_point = reconstructed_elementary_qtz_st[f\"{prefix_}.zero_point\"]\n",
-    "        weight = torch._make_per_tensor_quantized_tensor(int_repr,\n",
-    "                                                         scale,\n",
-    "                                                         zero_point)\n",
-    "\n",
-    "        reconstructed_qtz_st[f\"{prefix_}\"] = weight\n",
-    "    elif \"int_repr.data\" in name or \"int_repr.shape\" in name or \"int_repr.indices\" in name or \\\n",
-    "         \"weight.scale\" in name or \"weight.zero_point\" in name:\n",
-    "        continue\n",
-    "    else:\n",
-    "        reconstructed_qtz_st[name] = param\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Sanity checks\n",
-    "\n",
-    "for name, param in reconstructed_qtz_st.items():\n",
-    "    assert name in qtz_st\n",
-    "for name, param in qtz_st.items():\n",
-    "    assert name in reconstructed_qtz_st, name\n",
-    "\n",
-    "for name, param in reconstructed_qtz_st.items():\n",
-    "    assert type(param) == type(qtz_st[name]), name\n",
-    "    if type(param) == torch.Tensor:\n",
-    "        assert torch.all(torch.eq(param, qtz_st[name])), name\n",
-    "    elif type(param) == np.ndarray:\n",
-    "        assert (param == qtz_st[name]).all(), name\n",
-    "    else:\n",
-    "        assert param == qtz_st[name], name"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Sanity checks"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<All keys matched successfully>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Load the re-constructed state dict into a model\n",
-    "\n",
-    "dummy_model = BertForQuestionAnswering.from_pretrained('bert-base-uncased')\n",
-    "dummy_model.to('cpu')\n",
-    "\n",
-    "reconstructed_qtz_model = torch.quantization.quantize_dynamic(\n",
-    "                            model=dummy_model,\n",
-    "                            qconfig_spec = None,\n",
-    "                            dtype=torch.qint8,\n",
-    "                          )\n",
-    "\n",
-    "reconstructed_qtz_st = OrderedDict(reconstructed_qtz_st)\n",
-    "with open('dbg/metadata.json', 'r') as read_file:\n",
-    "    metadata = json.loads(read_file.read())\n",
-    "reconstructed_qtz_st._metadata = metadata\n",
-    "\n",
-    "reconstructed_qtz_model.load_state_dict(reconstructed_qtz_st)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Sanity check passed\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Sanity checks on the infernce\n",
-    "\n",
-    "N = 32\n",
-    "\n",
-    "for _ in range(25):\n",
-    "    inputs = torch.randint(low=0, high=30000, size=(N, 128))\n",
-    "    mask = torch.ones(size=(N, 128))\n",
-    "\n",
-    "    y_reconstructed = reconstructed_qtz_model(input_ids=inputs, attention_mask=mask)[0]\n",
-    "    y               = quantized_model(input_ids=inputs, attention_mask=mask)[0]\n",
-    "    \n",
-    "    assert torch.all(torch.eq(y, y_reconstructed))\n",
-    "print(\"Sanity check passed\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

examples/movement-pruning/bertarize.py

@@ -1,132 +0,0 @@
-# Copyright 2020-present, the HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Once a model has been fine-pruned, the weights that are masked during the forward pass can be pruned once for all.
-For instance, once the a model from the :class:`~emmental.MaskedBertForSequenceClassification` is trained, it can be saved (and then loaded)
-as a standard :class:`~transformers.BertForSequenceClassification`.
-"""
-
-import argparse
-import os
-import shutil
-
-import torch
-
-from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
-
-
-def main(args):
-    pruning_method = args.pruning_method
-    threshold = args.threshold
-
-    model_name_or_path = args.model_name_or_path.rstrip("/")
-    target_model_path = args.target_model_path
-
-    print(f"Load fine-pruned model from {model_name_or_path}")
-    model = torch.load(os.path.join(model_name_or_path, "pytorch_model.bin"))
-    pruned_model = {}
-
-    for name, tensor in model.items():
-        if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
-            pruned_model[name] = tensor
-            print(f"Copied layer {name}")
-        elif "classifier" in name or "qa_output" in name:
-            pruned_model[name] = tensor
-            print(f"Copied layer {name}")
-        elif "bias" in name:
-            pruned_model[name] = tensor
-            print(f"Copied layer {name}")
-        else:
-            if pruning_method == "magnitude":
-                mask = MagnitudeBinarizer.apply(inputs=tensor, threshold=threshold)
-                pruned_model[name] = tensor * mask
-                print(f"Pruned layer {name}")
-            elif pruning_method == "topK":
-                if "mask_scores" in name:
-                    continue
-                prefix_ = name[:-6]
-                scores = model[f"{prefix_}mask_scores"]
-                mask = TopKBinarizer.apply(scores, threshold)
-                pruned_model[name] = tensor * mask
-                print(f"Pruned layer {name}")
-            elif pruning_method == "sigmoied_threshold":
-                if "mask_scores" in name:
-                    continue
-                prefix_ = name[:-6]
-                scores = model[f"{prefix_}mask_scores"]
-                mask = ThresholdBinarizer.apply(scores, threshold, True)
-                pruned_model[name] = tensor * mask
-                print(f"Pruned layer {name}")
-            elif pruning_method == "l0":
-                if "mask_scores" in name:
-                    continue
-                prefix_ = name[:-6]
-                scores = model[f"{prefix_}mask_scores"]
-                l, r = -0.1, 1.1
-                s = torch.sigmoid(scores)
-                s_bar = s * (r - l) + l
-                mask = s_bar.clamp(min=0.0, max=1.0)
-                pruned_model[name] = tensor * mask
-                print(f"Pruned layer {name}")
-            else:
-                raise ValueError("Unknown pruning method")
-
-    if target_model_path is None:
-        target_model_path = os.path.join(
-            os.path.dirname(model_name_or_path), f"bertarized_{os.path.basename(model_name_or_path)}"
-        )
-
-    if not os.path.isdir(target_model_path):
-        shutil.copytree(model_name_or_path, target_model_path)
-        print(f"\nCreated folder {target_model_path}")
-
-    torch.save(pruned_model, os.path.join(target_model_path, "pytorch_model.bin"))
-    print("\nPruned model saved! See you later!")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "--pruning_method",
-        choices=["l0", "magnitude", "topK", "sigmoied_threshold"],
-        type=str,
-        required=True,
-        help="Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning, sigmoied_threshold = Soft movement pruning)",
-    )
-    parser.add_argument(
-        "--threshold",
-        type=float,
-        required=False,
-        help="For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model."
-        "For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."
-        "Not needed for `l0`",
-    )
-    parser.add_argument(
-        "--model_name_or_path",
-        type=str,
-        required=True,
-        help="Folder containing the model that was previously fine-pruned",
-    )
-    parser.add_argument(
-        "--target_model_path",
-        default=None,
-        type=str,
-        required=False,
-        help="Folder containing the model that was previously fine-pruned",
-    )
-
-    args = parser.parse_args()
-
-    main(args)

examples/movement-pruning/counts_parameters.py

@@ -1,92 +0,0 @@
-# Copyright 2020-present, the HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Count remaining (non-zero) weights in the encoder (i.e. the transformer layers).
-Sparsity and remaining weights levels are equivalent: sparsity % = 100 - remaining weights %.
-"""
-import argparse
-import os
-
-import torch
-
-from emmental.modules import ThresholdBinarizer, TopKBinarizer
-
-
-def main(args):
-    serialization_dir = args.serialization_dir
-    pruning_method = args.pruning_method
-    threshold = args.threshold
-
-    st = torch.load(os.path.join(serialization_dir, "pytorch_model.bin"), map_location="cpu")
-
-    remaining_count = 0  # Number of remaining (not pruned) params in the encoder
-    encoder_count = 0  # Number of params in the encoder
-
-    print("name".ljust(60, " "), "Remaining Weights %", "Remaning Weight")
-    for name, param in st.items():
-        if "encoder" not in name:
-            continue
-
-        if "mask_scores" in name:
-            if pruning_method == "topK":
-                mask_ones = TopKBinarizer.apply(param, threshold).sum().item()
-            elif pruning_method == "sigmoied_threshold":
-                mask_ones = ThresholdBinarizer.apply(param, threshold, True).sum().item()
-            elif pruning_method == "l0":
-                l, r = -0.1, 1.1
-                s = torch.sigmoid(param)
-                s_bar = s * (r - l) + l
-                mask = s_bar.clamp(min=0.0, max=1.0)
-                mask_ones = (mask > 0.0).sum().item()
-            else:
-                raise ValueError("Unknown pruning method")
-            remaining_count += mask_ones
-            print(name.ljust(60, " "), str(round(100 * mask_ones / param.numel(), 3)).ljust(20, " "), str(mask_ones))
-        else:
-            encoder_count += param.numel()
-            if "bias" in name or "LayerNorm" in name:
-                remaining_count += param.numel()
-
-    print("")
-    print("Remaining Weights (global) %: ", 100 * remaining_count / encoder_count)
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "--pruning_method",
-        choices=["l0", "topK", "sigmoied_threshold"],
-        type=str,
-        required=True,
-        help="Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement pruning)",
-    )
-    parser.add_argument(
-        "--threshold",
-        type=float,
-        required=False,
-        help="For `topK`, it is the level of remaining weights (in %) in the fine-pruned model."
-        "For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."
-        "Not needed for `l0`",
-    )
-    parser.add_argument(
-        "--serialization_dir",
-        type=str,
-        required=True,
-        help="Folder containing the model that was previously fine-pruned",
-    )
-
-    args = parser.parse_args()
-
-    main(args)

examples/movement-pruning/emmental/__init__.py

@@ -1,10 +0,0 @@
-# flake8: noqa
-from .configuration_bert_masked import MaskedBertConfig
-from .modeling_bert_masked import (
-    MaskedBertForMultipleChoice,
-    MaskedBertForQuestionAnswering,
-    MaskedBertForSequenceClassification,
-    MaskedBertForTokenClassification,
-    MaskedBertModel,
-)
-from .modules import *

examples/movement-pruning/emmental/configuration_bert_masked.py

@@ -1,71 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Masked BERT model configuration. It replicates the class `~transformers.BertConfig`
-and adapts it to the specificities of MaskedBert (`pruning_method`, `mask_init` and `mask_scale`."""
-
-
-import logging
-
-from transformers.configuration_utils import PretrainedConfig
-
-
-logger = logging.getLogger(__name__)
-
-
-class MaskedBertConfig(PretrainedConfig):
-    """
-    A class replicating the `~transformers.BertConfig` with additional parameters for pruning/masking configuration.
-    """
-
-    model_type = "masked_bert"
-
-    def __init__(
-        self,
-        vocab_size=30522,
-        hidden_size=768,
-        num_hidden_layers=12,
-        num_attention_heads=12,
-        intermediate_size=3072,
-        hidden_act="gelu",
-        hidden_dropout_prob=0.1,
-        attention_probs_dropout_prob=0.1,
-        max_position_embeddings=512,
-        type_vocab_size=2,
-        initializer_range=0.02,
-        layer_norm_eps=1e-12,
-        pad_token_id=0,
-        pruning_method="topK",
-        mask_init="constant",
-        mask_scale=0.0,
-        **kwargs
-    ):
-        super().__init__(pad_token_id=pad_token_id, **kwargs)
-
-        self.vocab_size = vocab_size
-        self.hidden_size = hidden_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.hidden_act = hidden_act
-        self.intermediate_size = intermediate_size
-        self.hidden_dropout_prob = hidden_dropout_prob
-        self.attention_probs_dropout_prob = attention_probs_dropout_prob
-        self.max_position_embeddings = max_position_embeddings
-        self.type_vocab_size = type_vocab_size
-        self.initializer_range = initializer_range
-        self.layer_norm_eps = layer_norm_eps
-        self.pruning_method = pruning_method
-        self.mask_init = mask_init
-        self.mask_scale = mask_scale

examples/movement-pruning/emmental/modules/__init__.py

@@ -1,3 +0,0 @@
-# flake8: noqa
-from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
-from .masked_nn import MaskedLinear

examples/movement-pruning/emmental/modules/binarizer.py

@@ -1,144 +0,0 @@
-# coding=utf-8
-# Copyright 2020-present, AllenAI Authors, University of Illinois Urbana-Champaign,
-# Intel Nervana Systems and the HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Binarizers take a (real value) matrice as input and produce a binary (values in {0,1}) mask of the same shape.
-"""
-
-import torch
-from torch import autograd
-
-
-class ThresholdBinarizer(autograd.Function):
-    """
-    Thresholdd binarizer.
-    Computes a binary mask M from a real value matrix S such that `M_{i,j} = 1` if and only if `S_{i,j} > \tau`
-    where `\tau` is a real value threshold.
-
-    Implementation is inspired from:
-        https://github.com/arunmallya/piggyback
-        Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights
-        Arun Mallya, Dillon Davis, Svetlana Lazebnik
-    """
-
-    @staticmethod
-    def forward(ctx, inputs: torch.tensor, threshold: float, sigmoid: bool):
-        """
-        Args:
-            inputs (`torch.FloatTensor`)
-                The input matrix from which the binarizer computes the binary mask.
-            threshold (`float`)
-                The threshold value (in R).
-            sigmoid (`bool`)
-                If set to ``True``, we apply the sigmoid function to the `inputs` matrix before comparing to `threshold`.
-                In this case, `threshold` should be a value between 0 and 1.
-        Returns:
-            mask (`torch.FloatTensor`)
-                Binary matrix of the same size as `inputs` acting as a mask (1 - the associated weight is
-                retained, 0 - the associated weight is pruned).
-        """
-        nb_elems = inputs.numel()
-        nb_min = int(0.005 * nb_elems) + 1
-        if sigmoid:
-            mask = (torch.sigmoid(inputs) > threshold).type(inputs.type())
-        else:
-            mask = (inputs > threshold).type(inputs.type())
-        if mask.sum() < nb_min:
-            # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
-            k_threshold = inputs.flatten().kthvalue(max(nb_elems - nb_min, 1)).values
-            mask = (inputs > k_threshold).type(inputs.type())
-        return mask
-
-    @staticmethod
-    def backward(ctx, gradOutput):
-        return gradOutput, None, None
-
-
-class TopKBinarizer(autograd.Function):
-    """
-    Top-k Binarizer.
-    Computes a binary mask M from a real value matrix S such that `M_{i,j} = 1` if and only if `S_{i,j}`
-    is among the k% highest values of S.
-
-    Implementation is inspired from:
-        https://github.com/allenai/hidden-networks
-        What's hidden in a randomly weighted neural network?
-        Vivek Ramanujan*, Mitchell Wortsman*, Aniruddha Kembhavi, Ali Farhadi, Mohammad Rastegari
-    """
-
-    @staticmethod
-    def forward(ctx, inputs: torch.tensor, threshold: float):
-        """
-        Args:
-            inputs (`torch.FloatTensor`)
-                The input matrix from which the binarizer computes the binary mask.
-            threshold (`float`)
-                The percentage of weights to keep (the rest is pruned).
-                `threshold` is a float between 0 and 1.
-        Returns:
-            mask (`torch.FloatTensor`)
-                Binary matrix of the same size as `inputs` acting as a mask (1 - the associated weight is
-                retained, 0 - the associated weight is pruned).
-        """
-        # Get the subnetwork by sorting the inputs and using the top threshold %
-        mask = inputs.clone()
-        _, idx = inputs.flatten().sort(descending=True)
-        j = int(threshold * inputs.numel())
-
-        # flat_out and mask access the same memory.
-        flat_out = mask.flatten()
-        flat_out[idx[j:]] = 0
-        flat_out[idx[:j]] = 1
-        return mask
-
-    @staticmethod
-    def backward(ctx, gradOutput):
-        return gradOutput, None
-
-
-class MagnitudeBinarizer(object):
-    """
-    Magnitude Binarizer.
-    Computes a binary mask M from a real value matrix S such that `M_{i,j} = 1` if and only if `S_{i,j}`
-    is among the k% highest values of |S| (absolute value).
-
-    Implementation is inspired from https://github.com/NervanaSystems/distiller/blob/2291fdcc2ea642a98d4e20629acb5a9e2e04b4e6/distiller/pruning/automated_gradual_pruner.py#L24
-    """
-
-    @staticmethod
-    def apply(inputs: torch.tensor, threshold: float):
-        """
-        Args:
-            inputs (`torch.FloatTensor`)
-                The input matrix from which the binarizer computes the binary mask.
-                This input marix is typically the weight matrix.
-            threshold (`float`)
-                The percentage of weights to keep (the rest is pruned).
-                `threshold` is a float between 0 and 1.
-        Returns:
-            mask (`torch.FloatTensor`)
-                Binary matrix of the same size as `inputs` acting as a mask (1 - the associated weight is
-                retained, 0 - the associated weight is pruned).
-        """
-        # Get the subnetwork by sorting the inputs and using the top threshold %
-        mask = inputs.clone()
-        _, idx = inputs.abs().flatten().sort(descending=True)
-        j = int(threshold * inputs.numel())
-
-        # flat_out and mask access the same memory.
-        flat_out = mask.flatten()
-        flat_out[idx[j:]] = 0
-        flat_out[idx[:j]] = 1
-        return mask

examples/movement-pruning/emmental/modules/masked_nn.py

@@ -1,107 +0,0 @@
-# coding=utf-8
-# Copyright 2020-present, the HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Masked Linear module: A fully connected layer that computes an adaptive binary mask on the fly.
-The mask (binary or not) is computed at each forward pass and multiplied against
-the weight matrix to prune a portion of the weights.
-The pruned weight matrix is then multiplied against the inputs (and if necessary, the bias is added).
-"""
-
-import math
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.nn import init
-
-from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
-
-
-class MaskedLinear(nn.Linear):
-    """
-    Fully Connected layer with on the fly adaptive mask.
-    If needed, a score matrix is created to store the importance of each associated weight.
-    """
-
-    def __init__(
-        self,
-        in_features: int,
-        out_features: int,
-        bias: bool = True,
-        mask_init: str = "constant",
-        mask_scale: float = 0.0,
-        pruning_method: str = "topK",
-    ):
-        """
-        Args:
-            in_features (`int`)
-                Size of each input sample
-            out_features (`int`)
-                Size of each output sample
-            bias (`bool`)
-                If set to ``False``, the layer will not learn an additive bias.
-                Default: ``True``
-            mask_init (`str`)
-                The initialization method for the score matrix if a score matrix is needed.
-                Choices: ["constant", "uniform", "kaiming"]
-                Default: ``constant``
-            mask_scale (`float`)
-                The initialization parameter for the chosen initialization method `mask_init`.
-                Default: ``0.``
-            pruning_method (`str`)
-                Method to compute the mask.
-                Choices: ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
-                Default: ``topK``
-        """
-        super(MaskedLinear, self).__init__(in_features=in_features, out_features=out_features, bias=bias)
-        assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
-        self.pruning_method = pruning_method
-
-        if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
-            self.mask_scale = mask_scale
-            self.mask_init = mask_init
-            self.mask_scores = nn.Parameter(torch.Tensor(self.weight.size()))
-            self.init_mask()
-
-    def init_mask(self):
-        if self.mask_init == "constant":
-            init.constant_(self.mask_scores, val=self.mask_scale)
-        elif self.mask_init == "uniform":
-            init.uniform_(self.mask_scores, a=-self.mask_scale, b=self.mask_scale)
-        elif self.mask_init == "kaiming":
-            init.kaiming_uniform_(self.mask_scores, a=math.sqrt(5))
-
-    def forward(self, input: torch.tensor, threshold: float):
-        # Get the mask
-        if self.pruning_method == "topK":
-            mask = TopKBinarizer.apply(self.mask_scores, threshold)
-        elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
-            sig = "sigmoied" in self.pruning_method
-            mask = ThresholdBinarizer.apply(self.mask_scores, threshold, sig)
-        elif self.pruning_method == "magnitude":
-            mask = MagnitudeBinarizer.apply(self.weight, threshold)
-        elif self.pruning_method == "l0":
-            l, r, b = -0.1, 1.1, 2 / 3
-            if self.training:
-                u = torch.zeros_like(self.mask_scores).uniform_().clamp(0.0001, 0.9999)
-                s = torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b)
-            else:
-                s = torch.sigmoid(self.mask_scores)
-            s_bar = s * (r - l) + l
-            mask = s_bar.clamp(min=0.0, max=1.0)
-        # Mask weights with computed mask
-        weight_thresholded = mask * self.weight
-        # Compute output (linear layer) with masked weights
-        return F.linear(input, weight_thresholded, self.bias)

examples/movement-pruning/masked_run_glue.py

@@ -1,924 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Fine-pruning Masked BERT on sequence classification on GLUE."""
-
-import argparse
-import glob
-import json
-import logging
-import os
-import random
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
-from torch.utils.data.distributed import DistributedSampler
-from tqdm import tqdm, trange
-
-from emmental import MaskedBertConfig, MaskedBertForSequenceClassification
-from transformers import (
-    WEIGHTS_NAME,
-    AdamW,
-    BertConfig,
-    BertForSequenceClassification,
-    BertTokenizer,
-    get_linear_schedule_with_warmup,
-)
-from transformers import glue_compute_metrics as compute_metrics
-from transformers import glue_convert_examples_to_features as convert_examples_to_features
-from transformers import glue_output_modes as output_modes
-from transformers import glue_processors as processors
-
-
-try:
-    from torch.utils.tensorboard import SummaryWriter
-except ImportError:
-    from tensorboardX import SummaryWriter
-
-
-logger = logging.getLogger(__name__)
-
-MODEL_CLASSES = {
-    "bert": (BertConfig, BertForSequenceClassification, BertTokenizer),
-    "masked_bert": (MaskedBertConfig, MaskedBertForSequenceClassification, BertTokenizer),
-}
-
-
-def set_seed(args):
-    random.seed(args.seed)
-    np.random.seed(args.seed)
-    torch.manual_seed(args.seed)
-    if args.n_gpu > 0:
-        torch.cuda.manual_seed_all(args.seed)
-
-
-def schedule_threshold(
-    step: int,
-    total_step: int,
-    warmup_steps: int,
-    initial_threshold: float,
-    final_threshold: float,
-    initial_warmup: int,
-    final_warmup: int,
-    final_lambda: float,
-):
-    if step <= initial_warmup * warmup_steps:
-        threshold = initial_threshold
-    elif step > (total_step - final_warmup * warmup_steps):
-        threshold = final_threshold
-    else:
-        spars_warmup_steps = initial_warmup * warmup_steps
-        spars_schedu_steps = (final_warmup + initial_warmup) * warmup_steps
-        mul_coeff = 1 - (step - spars_warmup_steps) / (total_step - spars_schedu_steps)
-        threshold = final_threshold + (initial_threshold - final_threshold) * (mul_coeff ** 3)
-    regu_lambda = final_lambda * threshold / final_threshold
-    return threshold, regu_lambda
-
-
-def regularization(model: nn.Module, mode: str):
-    regu, counter = 0, 0
-    for name, param in model.named_parameters():
-        if "mask_scores" in name:
-            if mode == "l1":
-                regu += torch.norm(torch.sigmoid(param), p=1) / param.numel()
-            elif mode == "l0":
-                regu += torch.sigmoid(param - 2 / 3 * np.log(0.1 / 1.1)).sum() / param.numel()
-            else:
-                ValueError("Don't know this mode.")
-            counter += 1
-    return regu / counter
-
-
-def train(args, train_dataset, model, tokenizer, teacher=None):
-    """ Train the model """
-    if args.local_rank in [-1, 0]:
-        tb_writer = SummaryWriter(log_dir=args.output_dir)
-
-    args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
-    train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
-    train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)
-
-    if args.max_steps > 0:
-        t_total = args.max_steps
-        args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
-    else:
-        t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
-
-    # Prepare optimizer and schedule (linear warmup and decay)
-    no_decay = ["bias", "LayerNorm.weight"]
-    optimizer_grouped_parameters = [
-        {
-            "params": [p for n, p in model.named_parameters() if "mask_score" in n and p.requires_grad],
-            "lr": args.mask_scores_learning_rate,
-        },
-        {
-            "params": [
-                p
-                for n, p in model.named_parameters()
-                if "mask_score" not in n and p.requires_grad and not any(nd in n for nd in no_decay)
-            ],
-            "lr": args.learning_rate,
-            "weight_decay": args.weight_decay,
-        },
-        {
-            "params": [
-                p
-                for n, p in model.named_parameters()
-                if "mask_score" not in n and p.requires_grad and any(nd in n for nd in no_decay)
-            ],
-            "lr": args.learning_rate,
-            "weight_decay": 0.0,
-        },
-    ]
-
-    optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
-    scheduler = get_linear_schedule_with_warmup(
-        optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
-    )
-
-    # Check if saved optimizer or scheduler states exist
-    if os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt")) and os.path.isfile(
-        os.path.join(args.model_name_or_path, "scheduler.pt")
-    ):
-        # Load in optimizer and scheduler states
-        optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt")))
-        scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "scheduler.pt")))
-
-    if args.fp16:
-        try:
-            from apex import amp
-        except ImportError:
-            raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
-        model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
-
-    # multi-gpu training (should be after apex fp16 initialization)
-    if args.n_gpu > 1:
-        model = torch.nn.DataParallel(model)
-
-    # Distributed training (should be after apex fp16 initialization)
-    if args.local_rank != -1:
-        model = torch.nn.parallel.DistributedDataParallel(
-            model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True,
-        )
-
-    # Train!
-    logger.info("***** Running training *****")
-    logger.info("  Num examples = %d", len(train_dataset))
-    logger.info("  Num Epochs = %d", args.num_train_epochs)
-    logger.info("  Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
-    logger.info(
-        "  Total train batch size (w. parallel, distributed & accumulation) = %d",
-        args.train_batch_size
-        * args.gradient_accumulation_steps
-        * (torch.distributed.get_world_size() if args.local_rank != -1 else 1),
-    )
-    logger.info("  Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
-    logger.info("  Total optimization steps = %d", t_total)
-    # Distillation
-    if teacher is not None:
-        logger.info("  Training with distillation")
-
-    global_step = 0
-    # Global TopK
-    if args.global_topk:
-        threshold_mem = None
-    epochs_trained = 0
-    steps_trained_in_current_epoch = 0
-    # Check if continuing training from a checkpoint
-    if os.path.exists(args.model_name_or_path):
-        # set global_step to global_step of last saved checkpoint from model path
-        try:
-            global_step = int(args.model_name_or_path.split("-")[-1].split("/")[0])
-        except ValueError:
-            global_step = 0
-        epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps)
-        steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps)
-
-        logger.info("  Continuing training from checkpoint, will skip to saved global_step")
-        logger.info("  Continuing training from epoch %d", epochs_trained)
-        logger.info("  Continuing training from global step %d", global_step)
-        logger.info("  Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch)
-
-    tr_loss, logging_loss = 0.0, 0.0
-    model.zero_grad()
-    train_iterator = trange(
-        epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0],
-    )
-    set_seed(args)  # Added here for reproductibility
-    for _ in train_iterator:
-        epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
-        for step, batch in enumerate(epoch_iterator):
-
-            # Skip past any already trained steps if resuming training
-            if steps_trained_in_current_epoch > 0:
-                steps_trained_in_current_epoch -= 1
-                continue
-
-            model.train()
-            batch = tuple(t.to(args.device) for t in batch)
-            threshold, regu_lambda = schedule_threshold(
-                step=global_step,
-                total_step=t_total,
-                warmup_steps=args.warmup_steps,
-                final_threshold=args.final_threshold,
-                initial_threshold=args.initial_threshold,
-                final_warmup=args.final_warmup,
-                initial_warmup=args.initial_warmup,
-                final_lambda=args.final_lambda,
-            )
-            # Global TopK
-            if args.global_topk:
-                if threshold == 1.0:
-                    threshold = -1e2  # Or an indefinitely low quantity
-                else:
-                    if (threshold_mem is None) or (global_step % args.global_topk_frequency_compute == 0):
-                        # Sort all the values to get the global topK
-                        concat = torch.cat(
-                            [param.view(-1) for name, param in model.named_parameters() if "mask_scores" in name]
-                        )
-                        n = concat.numel()
-                        kth = max(n - (int(n * threshold) + 1), 1)
-                        threshold_mem = concat.kthvalue(kth).values.item()
-                        threshold = threshold_mem
-                    else:
-                        threshold = threshold_mem
-            inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
-            if args.model_type != "distilbert":
-                inputs["token_type_ids"] = (
-                    batch[2] if args.model_type in ["bert", "masked_bert", "xlnet", "albert"] else None
-                )  # XLM, DistilBERT, RoBERTa, and XLM-RoBERTa don't use segment_ids
-
-            if "masked" in args.model_type:
-                inputs["threshold"] = threshold
-
-            outputs = model(**inputs)
-            loss, logits_stu = outputs  # model outputs are always tuple in transformers (see doc)
-
-            # Distillation loss
-            if teacher is not None:
-                if "token_type_ids" not in inputs:
-                    inputs["token_type_ids"] = None if args.teacher_type == "xlm" else batch[2]
-                with torch.no_grad():
-                    (logits_tea,) = teacher(
-                        input_ids=inputs["input_ids"],
-                        token_type_ids=inputs["token_type_ids"],
-                        attention_mask=inputs["attention_mask"],
-                    )
-
-                loss_logits = F.kl_div(
-                    input=F.log_softmax(logits_stu / args.temperature, dim=-1),
-                    target=F.softmax(logits_tea / args.temperature, dim=-1),
-                    reduction="batchmean",
-                ) * (args.temperature ** 2)
-
-                loss = args.alpha_distil * loss_logits + args.alpha_ce * loss
-
-            # Regularization
-            if args.regularization is not None:
-                regu_ = regularization(model=model, mode=args.regularization)
-                loss = loss + regu_lambda * regu_
-
-            if args.n_gpu > 1:
-                loss = loss.mean()  # mean() to average on multi-gpu parallel training
-            if args.gradient_accumulation_steps > 1:
-                loss = loss / args.gradient_accumulation_steps
-
-            if args.fp16:
-                with amp.scale_loss(loss, optimizer) as scaled_loss:
-                    scaled_loss.backward()
-            else:
-                loss.backward()
-
-            tr_loss += loss.item()
-            if (step + 1) % args.gradient_accumulation_steps == 0 or (
-                # last step in epoch but step is always smaller than gradient_accumulation_steps
-                len(epoch_iterator) <= args.gradient_accumulation_steps
-                and (step + 1) == len(epoch_iterator)
-            ):
-                if args.fp16:
-                    torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
-                else:
-                    torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
-
-                if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
-                    tb_writer.add_scalar("threshold", threshold, global_step)
-                    for name, param in model.named_parameters():
-                        if not param.requires_grad:
-                            continue
-                        tb_writer.add_scalar("parameter_mean/" + name, param.data.mean(), global_step)
-                        tb_writer.add_scalar("parameter_std/" + name, param.data.std(), global_step)
-                        tb_writer.add_scalar("parameter_min/" + name, param.data.min(), global_step)
-                        tb_writer.add_scalar("parameter_max/" + name, param.data.max(), global_step)
-                        tb_writer.add_scalar("grad_mean/" + name, param.grad.data.mean(), global_step)
-                        tb_writer.add_scalar("grad_std/" + name, param.grad.data.std(), global_step)
-                        if args.regularization is not None and "mask_scores" in name:
-                            if args.regularization == "l1":
-                                perc = (torch.sigmoid(param) > threshold).sum().item() / param.numel()
-                            elif args.regularization == "l0":
-                                perc = (torch.sigmoid(param - 2 / 3 * np.log(0.1 / 1.1))).sum().item() / param.numel()
-                            tb_writer.add_scalar("retained_weights_perc/" + name, perc, global_step)
-
-                optimizer.step()
-                scheduler.step()  # Update learning rate schedule
-                model.zero_grad()
-                global_step += 1
-
-                if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
-                    logs = {}
-                    if (
-                        args.local_rank == -1 and args.evaluate_during_training
-                    ):  # Only evaluate when single GPU otherwise metrics may not average well
-                        results = evaluate(args, model, tokenizer)
-                        for key, value in results.items():
-                            eval_key = "eval_{}".format(key)
-                            logs[eval_key] = value
-
-                    loss_scalar = (tr_loss - logging_loss) / args.logging_steps
-                    learning_rate_scalar = scheduler.get_lr()
-                    logs["learning_rate"] = learning_rate_scalar[0]
-                    if len(learning_rate_scalar) > 1:
-                        for idx, lr in enumerate(learning_rate_scalar[1:]):
-                            logs[f"learning_rate/{idx+1}"] = lr
-                    logs["loss"] = loss_scalar
-                    if teacher is not None:
-                        logs["loss/distil"] = loss_logits.item()
-                    if args.regularization is not None:
-                        logs["loss/regularization"] = regu_.item()
-                    if (teacher is not None) or (args.regularization is not None):
-                        if (teacher is not None) and (args.regularization is not None):
-                            logs["loss/instant_ce"] = (
-                                loss.item()
-                                - regu_lambda * logs["loss/regularization"]
-                                - args.alpha_distil * logs["loss/distil"]
-                            ) / args.alpha_ce
-                        elif teacher is not None:
-                            logs["loss/instant_ce"] = (
-                                loss.item() - args.alpha_distil * logs["loss/distil"]
-                            ) / args.alpha_ce
-                        else:
-                            logs["loss/instant_ce"] = loss.item() - regu_lambda * logs["loss/regularization"]
-                    logging_loss = tr_loss
-
-                    for key, value in logs.items():
-                        tb_writer.add_scalar(key, value, global_step)
-                    print(json.dumps({**logs, **{"step": global_step}}))
-
-                if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
-                    # Save model checkpoint
-                    output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
-                    if not os.path.exists(output_dir):
-                        os.makedirs(output_dir)
-                    model_to_save = (
-                        model.module if hasattr(model, "module") else model
-                    )  # Take care of distributed/parallel training
-                    model_to_save.save_pretrained(output_dir)
-                    tokenizer.save_pretrained(output_dir)
-
-                    torch.save(args, os.path.join(output_dir, "training_args.bin"))
-                    logger.info("Saving model checkpoint to %s", output_dir)
-
-                    torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
-                    torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
-                    logger.info("Saving optimizer and scheduler states to %s", output_dir)
-
-            if args.max_steps > 0 and global_step > args.max_steps:
-                epoch_iterator.close()
-                break
-        if args.max_steps > 0 and global_step > args.max_steps:
-            train_iterator.close()
-            break
-
-    if args.local_rank in [-1, 0]:
-        tb_writer.close()
-
-    return global_step, tr_loss / global_step
-
-
-def evaluate(args, model, tokenizer, prefix=""):
-    # Loop to handle MNLI double evaluation (matched, mis-matched)
-    eval_task_names = ("mnli", "mnli-mm") if args.task_name == "mnli" else (args.task_name,)
-    eval_outputs_dirs = (args.output_dir, args.output_dir + "/MM") if args.task_name == "mnli" else (args.output_dir,)
-
-    results = {}
-    for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs):
-        eval_dataset = load_and_cache_examples(args, eval_task, tokenizer, evaluate=True)
-
-        if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
-            os.makedirs(eval_output_dir)
-
-        args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
-        # Note that DistributedSampler samples randomly
-        eval_sampler = SequentialSampler(eval_dataset)
-        eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
-
-        # multi-gpu eval
-        if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
-            model = torch.nn.DataParallel(model)
-
-        # Eval!
-        logger.info("***** Running evaluation {} *****".format(prefix))
-        logger.info("  Num examples = %d", len(eval_dataset))
-        logger.info("  Batch size = %d", args.eval_batch_size)
-        eval_loss = 0.0
-        nb_eval_steps = 0
-        preds = None
-        out_label_ids = None
-
-        # Global TopK
-        if args.global_topk:
-            threshold_mem = None
-
-        for batch in tqdm(eval_dataloader, desc="Evaluating"):
-            model.eval()
-            batch = tuple(t.to(args.device) for t in batch)
-
-            with torch.no_grad():
-                inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}
-                if args.model_type != "distilbert":
-                    inputs["token_type_ids"] = (
-                        batch[2] if args.model_type in ["bert", "masked_bert", "xlnet", "albert"] else None
-                    )  # XLM, DistilBERT, RoBERTa, and XLM-RoBERTa don't use segment_ids
-                if "masked" in args.model_type:
-                    inputs["threshold"] = args.final_threshold
-                    if args.global_topk:
-                        if threshold_mem is None:
-                            concat = torch.cat(
-                                [param.view(-1) for name, param in model.named_parameters() if "mask_scores" in name]
-                            )
-                            n = concat.numel()
-                            kth = max(n - (int(n * args.final_threshold) + 1), 1)
-                            threshold_mem = concat.kthvalue(kth).values.item()
-                        inputs["threshold"] = threshold_mem
-                outputs = model(**inputs)
-                tmp_eval_loss, logits = outputs[:2]
-
-                eval_loss += tmp_eval_loss.mean().item()
-            nb_eval_steps += 1
-            if preds is None:
-                preds = logits.detach().cpu().numpy()
-                out_label_ids = inputs["labels"].detach().cpu().numpy()
-            else:
-                preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
-                out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0)
-
-        eval_loss = eval_loss / nb_eval_steps
-        if args.output_mode == "classification":
-            from scipy.special import softmax
-
-            probs = softmax(preds, axis=-1)
-            entropy = np.exp((-probs * np.log(probs)).sum(axis=-1).mean())
-            preds = np.argmax(preds, axis=1)
-        elif args.output_mode == "regression":
-            preds = np.squeeze(preds)
-        result = compute_metrics(eval_task, preds, out_label_ids)
-        results.update(result)
-        if entropy is not None:
-            result["eval_avg_entropy"] = entropy
-
-        output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt")
-        with open(output_eval_file, "w") as writer:
-            logger.info("***** Eval results {} *****".format(prefix))
-            for key in sorted(result.keys()):
-                logger.info("  %s = %s", key, str(result[key]))
-                writer.write("%s = %s\n" % (key, str(result[key])))
-
-    return results
-
-
-def load_and_cache_examples(args, task, tokenizer, evaluate=False):
-    if args.local_rank not in [-1, 0] and not evaluate:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
-
-    processor = processors[task]()
-    output_mode = output_modes[task]
-    # Load data features from cache or dataset file
-    cached_features_file = os.path.join(
-        args.data_dir,
-        "cached_{}_{}_{}_{}".format(
-            "dev" if evaluate else "train",
-            list(filter(None, args.model_name_or_path.split("/"))).pop(),
-            str(args.max_seq_length),
-            str(task),
-        ),
-    )
-    if os.path.exists(cached_features_file) and not args.overwrite_cache:
-        logger.info("Loading features from cached file %s", cached_features_file)
-        features = torch.load(cached_features_file)
-    else:
-        logger.info("Creating features from dataset file at %s", args.data_dir)
-        label_list = processor.get_labels()
-        if task in ["mnli", "mnli-mm"] and args.model_type in ["roberta", "xlmroberta"]:
-            # HACK(label indices are swapped in RoBERTa pretrained model)
-            label_list[1], label_list[2] = label_list[2], label_list[1]
-        examples = (
-            processor.get_dev_examples(args.data_dir) if evaluate else processor.get_train_examples(args.data_dir)
-        )
-        features = convert_examples_to_features(
-            examples, tokenizer, max_length=args.max_seq_length, label_list=label_list, output_mode=output_mode,
-        )
-        if args.local_rank in [-1, 0]:
-            logger.info("Saving features into cached file %s", cached_features_file)
-            torch.save(features, cached_features_file)
-
-    if args.local_rank == 0 and not evaluate:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training process the dataset, and the others will use the cache
-
-    # Convert to Tensors and build dataset
-    all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
-    all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
-    all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long)
-    if output_mode == "classification":
-        all_labels = torch.tensor([f.label for f in features], dtype=torch.long)
-    elif output_mode == "regression":
-        all_labels = torch.tensor([f.label for f in features], dtype=torch.float)
-
-    dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels)
-    return dataset
-
-
-def main():
-    parser = argparse.ArgumentParser()
-
-    # Required parameters
-    parser.add_argument(
-        "--data_dir",
-        default=None,
-        type=str,
-        required=True,
-        help="The input data dir. Should contain the .tsv files (or other data files) for the task.",
-    )
-    parser.add_argument(
-        "--model_type",
-        default=None,
-        type=str,
-        required=True,
-        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
-    )
-    parser.add_argument(
-        "--model_name_or_path",
-        default=None,
-        type=str,
-        required=True,
-        help="Path to pretrained model or model identifier from huggingface.co/models",
-    )
-    parser.add_argument(
-        "--task_name",
-        default=None,
-        type=str,
-        required=True,
-        help="The name of the task to train selected in the list: " + ", ".join(processors.keys()),
-    )
-    parser.add_argument(
-        "--output_dir",
-        default=None,
-        type=str,
-        required=True,
-        help="The output directory where the model predictions and checkpoints will be written.",
-    )
-    # Other parameters
-    parser.add_argument(
-        "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name",
-    )
-    parser.add_argument(
-        "--tokenizer_name",
-        default="",
-        type=str,
-        help="Pretrained tokenizer name or path if not the same as model_name",
-    )
-    parser.add_argument(
-        "--cache_dir",
-        default="",
-        type=str,
-        help="Where do you want to store the pre-trained models downloaded from s3",
-    )
-    parser.add_argument(
-        "--max_seq_length",
-        default=128,
-        type=int,
-        help="The maximum total input sequence length after tokenization. Sequences longer "
-        "than this will be truncated, sequences shorter will be padded.",
-    )
-    parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
-    parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.")
-    parser.add_argument(
-        "--evaluate_during_training", action="store_true", help="Run evaluation during training at each logging step.",
-    )
-    parser.add_argument(
-        "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model.",
-    )
-
-    parser.add_argument(
-        "--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.",
-    )
-    parser.add_argument(
-        "--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation.",
-    )
-    parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
-
-    # Pruning parameters
-    parser.add_argument(
-        "--mask_scores_learning_rate",
-        default=1e-2,
-        type=float,
-        help="The Adam initial learning rate of the mask scores.",
-    )
-    parser.add_argument(
-        "--initial_threshold", default=1.0, type=float, help="Initial value of the threshold (for scheduling)."
-    )
-    parser.add_argument(
-        "--final_threshold", default=0.7, type=float, help="Final value of the threshold (for scheduling)."
-    )
-    parser.add_argument(
-        "--initial_warmup",
-        default=1,
-        type=int,
-        help="Run `initial_warmup` * `warmup_steps` steps of threshold warmup during which threshold stays"
-        "at its `initial_threshold` value (sparsity schedule).",
-    )
-    parser.add_argument(
-        "--final_warmup",
-        default=2,
-        type=int,
-        help="Run `final_warmup` * `warmup_steps` steps of threshold cool-down during which threshold stays"
-        "at its final_threshold value (sparsity schedule).",
-    )
-
-    parser.add_argument(
-        "--pruning_method",
-        default="topK",
-        type=str,
-        help="Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning, sigmoied_threshold = Soft movement pruning).",
-    )
-    parser.add_argument(
-        "--mask_init",
-        default="constant",
-        type=str,
-        help="Initialization method for the mask scores. Choices: constant, uniform, kaiming.",
-    )
-    parser.add_argument(
-        "--mask_scale", default=0.0, type=float, help="Initialization parameter for the chosen initialization method."
-    )
-
-    parser.add_argument("--regularization", default=None, help="Add L0 or L1 regularization to the mask scores.")
-    parser.add_argument(
-        "--final_lambda",
-        default=0.0,
-        type=float,
-        help="Regularization intensity (used in conjunction with `regulariation`.",
-    )
-
-    parser.add_argument("--global_topk", action="store_true", help="Global TopK on the Scores.")
-    parser.add_argument(
-        "--global_topk_frequency_compute",
-        default=25,
-        type=int,
-        help="Frequency at which we compute the TopK global threshold.",
-    )
-
-    # Distillation parameters (optional)
-    parser.add_argument(
-        "--teacher_type",
-        default=None,
-        type=str,
-        help="Teacher type. Teacher tokenizer and student (model) tokenizer must output the same tokenization. Only for distillation.",
-    )
-    parser.add_argument(
-        "--teacher_name_or_path",
-        default=None,
-        type=str,
-        help="Path to the already fine-tuned teacher model. Only for distillation.",
-    )
-    parser.add_argument(
-        "--alpha_ce", default=0.5, type=float, help="Cross entropy loss linear weight. Only for distillation."
-    )
-    parser.add_argument(
-        "--alpha_distil", default=0.5, type=float, help="Distillation loss linear weight. Only for distillation."
-    )
-    parser.add_argument(
-        "--temperature", default=2.0, type=float, help="Distillation temperature. Only for distillation."
-    )
-
-    parser.add_argument(
-        "--gradient_accumulation_steps",
-        type=int,
-        default=1,
-        help="Number of updates steps to accumulate before performing a backward/update pass.",
-    )
-    parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
-    parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
-    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
-    parser.add_argument(
-        "--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform.",
-    )
-    parser.add_argument(
-        "--max_steps",
-        default=-1,
-        type=int,
-        help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
-    )
-    parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
-
-    parser.add_argument("--logging_steps", type=int, default=50, help="Log every X updates steps.")
-    parser.add_argument("--save_steps", type=int, default=50, help="Save checkpoint every X updates steps.")
-    parser.add_argument(
-        "--eval_all_checkpoints",
-        action="store_true",
-        help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
-    )
-    parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available")
-    parser.add_argument(
-        "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory",
-    )
-    parser.add_argument(
-        "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets",
-    )
-    parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
-
-    parser.add_argument(
-        "--fp16",
-        action="store_true",
-        help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
-    )
-    parser.add_argument(
-        "--fp16_opt_level",
-        type=str,
-        default="O1",
-        help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
-        "See details at https://nvidia.github.io/apex/amp.html",
-    )
-    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
-
-    args = parser.parse_args()
-
-    # Regularization
-    if args.regularization == "null":
-        args.regularization = None
-
-    if (
-        os.path.exists(args.output_dir)
-        and os.listdir(args.output_dir)
-        and args.do_train
-        and not args.overwrite_output_dir
-    ):
-        raise ValueError(
-            f"Output directory ({args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
-        )
-
-    # Setup CUDA, GPU & distributed training
-    if args.local_rank == -1 or args.no_cuda:
-        device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
-        args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count()
-    else:  # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
-        torch.cuda.set_device(args.local_rank)
-        device = torch.device("cuda", args.local_rank)
-        torch.distributed.init_process_group(backend="nccl")
-        args.n_gpu = 1
-    args.device = device
-
-    # Setup logging
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,
-    )
-    logger.warning(
-        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
-        args.local_rank,
-        device,
-        args.n_gpu,
-        bool(args.local_rank != -1),
-        args.fp16,
-    )
-
-    # Set seed
-    set_seed(args)
-
-    # Prepare GLUE task
-    args.task_name = args.task_name.lower()
-    if args.task_name not in processors:
-        raise ValueError("Task not found: %s" % (args.task_name))
-    processor = processors[args.task_name]()
-    args.output_mode = output_modes[args.task_name]
-    label_list = processor.get_labels()
-    num_labels = len(label_list)
-
-    # Load pretrained model and tokenizer
-    if args.local_rank not in [-1, 0]:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
-
-    args.model_type = args.model_type.lower()
-    config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
-    config = config_class.from_pretrained(
-        args.config_name if args.config_name else args.model_name_or_path,
-        num_labels=num_labels,
-        finetuning_task=args.task_name,
-        cache_dir=args.cache_dir if args.cache_dir else None,
-        pruning_method=args.pruning_method,
-        mask_init=args.mask_init,
-        mask_scale=args.mask_scale,
-    )
-    tokenizer = tokenizer_class.from_pretrained(
-        args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
-        cache_dir=args.cache_dir if args.cache_dir else None,
-        do_lower_case=args.do_lower_case,
-    )
-    model = model_class.from_pretrained(
-        args.model_name_or_path,
-        from_tf=bool(".ckpt" in args.model_name_or_path),
-        config=config,
-        cache_dir=args.cache_dir if args.cache_dir else None,
-    )
-
-    if args.teacher_type is not None:
-        assert args.teacher_name_or_path is not None
-        assert args.alpha_distil > 0.0
-        assert args.alpha_distil + args.alpha_ce > 0.0
-        teacher_config_class, teacher_model_class, _ = MODEL_CLASSES[args.teacher_type]
-        teacher_config = teacher_config_class.from_pretrained(args.teacher_name_or_path)
-        teacher = teacher_model_class.from_pretrained(
-            args.teacher_name_or_path,
-            from_tf=False,
-            config=teacher_config,
-            cache_dir=args.cache_dir if args.cache_dir else None,
-        )
-        teacher.to(args.device)
-    else:
-        teacher = None
-
-    if args.local_rank == 0:
-        torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
-
-    model.to(args.device)
-
-    logger.info("Training/evaluation parameters %s", args)
-
-    # Training
-    if args.do_train:
-        train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=False)
-        global_step, tr_loss = train(args, train_dataset, model, tokenizer, teacher=teacher)
-        logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
-
-    # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
-    if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
-        # Create output directory if needed
-        if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
-            os.makedirs(args.output_dir)
-
-        logger.info("Saving model checkpoint to %s", args.output_dir)
-        # Save a trained model, configuration and tokenizer using `save_pretrained()`.
-        # They can then be reloaded using `from_pretrained()`
-        model_to_save = (
-            model.module if hasattr(model, "module") else model
-        )  # Take care of distributed/parallel training
-        model_to_save.save_pretrained(args.output_dir)
-        tokenizer.save_pretrained(args.output_dir)
-
-        # Good practice: save your training arguments together with the trained model
-        torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
-
-        # Load a trained model and vocabulary that you have fine-tuned
-        model = model_class.from_pretrained(args.output_dir)
-        tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
-        model.to(args.device)
-
-    # Evaluation
-    results = {}
-    if args.do_eval and args.local_rank in [-1, 0]:
-        tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
-        checkpoints = [args.output_dir]
-        if args.eval_all_checkpoints:
-            checkpoints = list(
-                os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True))
-            )
-            logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN)  # Reduce logging
-        logger.info("Evaluate the following checkpoints: %s", checkpoints)
-        for checkpoint in checkpoints:
-            global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
-            prefix = checkpoint.split("/")[-1] if checkpoint.find("checkpoint") != -1 else ""
-
-            model = model_class.from_pretrained(checkpoint)
-            model.to(args.device)
-            result = evaluate(args, model, tokenizer, prefix=prefix)
-            result = dict((k + "_{}".format(global_step), v) for k, v in result.items())
-            results.update(result)
-
-    return results
-
-
-if __name__ == "__main__":
-    main()

examples/movement-pruning/requirements.txt

@@ -1,6 +0,0 @@
-torch>=1.4.0
--e git+https://github.com/huggingface/transformers.git@352d5472b0c1dec0f420d606d16747d851b4bda8#egg=transformers
-knockknock>=0.1.8.1
-h5py>=2.10.0
-numpy>=1.18.2
-scipy>=1.4.1

examples/multiple-choice/README.md

@@ -19,7 +19,7 @@ python ./examples/multiple-choice/run_multiple_choice.py \
 --max_seq_length 80 \
 --output_dir models_bert/swag_base \
 --per_gpu_eval_batch_size=16 \
---per_device_train_batch_size=16 \
+--per_gpu_train_batch_size=16 \
 --gradient_accumulation_steps 2 \
 --overwrite_output
 ```
@@ -46,7 +46,7 @@ python ./examples/multiple-choice/run_tf_multiple_choice.py \
 --max_seq_length 80 \
 --output_dir models_bert/swag_base \
 --per_gpu_eval_batch_size=16 \
---per_device_train_batch_size=16 \
+--per_gpu_train_batch_size=16 \
 --logging-dir logs \
 --gradient_accumulation_steps 2 \
 --overwrite_output

examples/multiple-choice/utils_multiple_choice.py

@@ -121,7 +121,16 @@ if is_torch_available():
                     else:
                         examples = processor.get_train_examples(data_dir)
                     logger.info("Training examples: %s", len(examples))
-                    self.features = convert_examples_to_features(examples, label_list, max_seq_length, tokenizer,)
+                    # TODO clean up all this to leverage built-in features of tokenizers
+                    self.features = convert_examples_to_features(
+                        examples,
+                        label_list,
+                        max_seq_length,
+                        tokenizer,
+                        pad_on_left=bool(tokenizer.padding_side == "left"),
+                        pad_token=tokenizer.pad_token_id,
+                        pad_token_segment_id=tokenizer.pad_token_type_id,
+                    )
                     logger.info("Saving features into cached file %s", cached_features_file)
                     torch.save(self.features, cached_features_file)
 
@@ -163,8 +172,16 @@ if is_tf_available():
             else:
                 examples = processor.get_train_examples(data_dir)
             logger.info("Training examples: %s", len(examples))
-
-            self.features = convert_examples_to_features(examples, label_list, max_seq_length, tokenizer,)
+            # TODO clean up all this to leverage built-in features of tokenizers
+            self.features = convert_examples_to_features(
+                examples,
+                label_list,
+                max_seq_length,
+                tokenizer,
+                pad_on_left=bool(tokenizer.padding_side == "left"),
+                pad_token=tokenizer.pad_token_id,
+                pad_token_segment_id=tokenizer.pad_token_type_id,
+            )
 
             def gen():
                 for (ex_index, ex) in tqdm.tqdm(enumerate(self.features), desc="convert examples to features"):
@@ -489,7 +506,14 @@ class ArcProcessor(DataProcessor):
 
 
 def convert_examples_to_features(
-    examples: List[InputExample], label_list: List[str], max_length: int, tokenizer: PreTrainedTokenizer,
+    examples: List[InputExample],
+    label_list: List[str],
+    max_length: int,
+    tokenizer: PreTrainedTokenizer,
+    pad_token_segment_id=0,
+    pad_on_left=False,
+    pad_token=0,
+    mask_padding_with_zero=True,
 ) -> List[InputFeatures]:
     """
     Loads a data file into a list of `InputFeatures`

examples/question-answering/README.md

@@ -165,15 +165,17 @@ Larger batch size may improve the performance while costing more memory.
 python run_tf_squad.py \
     --model_name_or_path bert-base-uncased \
     --output_dir model \
-    --max_seq_length 384 \
+    --max-seq-length 384 \
     --num_train_epochs 2 \
     --per_gpu_train_batch_size 8 \
     --per_gpu_eval_batch_size 16 \
     --do_train \
-    --logging_dir logs \    
+    --logging_dir logs \
+    --mode question-answering \
     --logging_steps 10 \
     --learning_rate 3e-5 \
-    --doc_stride 128    
+    --doc_stride 128 \
+    --optimizer_name adamw
 ```
 
-For the moment the evaluation is not available in the Tensorflow Trainer only the training.
+For the moment the evaluation is not available in the Tensorflow Trainer only the training.

examples/question-answering/run_squad.py

@@ -58,6 +58,8 @@ logger = logging.getLogger(__name__)
 MODEL_CONFIG_CLASSES = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
 MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
 
+ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in MODEL_CONFIG_CLASSES), (),)
+
 
 def set_seed(args):
     random.seed(args.seed)
@@ -489,7 +491,7 @@ def main():
         default=None,
         type=str,
         required=True,
-        help="Path to pretrained model or model identifier from huggingface.co/models",
+        help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS),
     )
     parser.add_argument(
         "--output_dir",

examples/summarization/bart/README.md

@@ -1,4 +1,4 @@
-### Get CNN Data
+### Get Preprocessed CNN Data
 To be able to reproduce the authors' results on the CNN/Daily Mail dataset you first need to download both CNN and Daily Mail datasets [from Kyunghyun Cho's website](https://cs.nyu.edu/~kcho/DMQA/) (the links next to "Stories") in the same folder. Then uncompress the archives by running:
 
 ```bash
@@ -6,19 +6,24 @@ wget https://s3.amazonaws.com/datasets.huggingface.co/summarization/cnn_dm.tgz
 tar -xzvf cnn_dm.tgz
 ```
 
-this should make a directory called cnn_dm/ with files like `test.source`.
+this should make a directory called cnn_dm/ with files like `test.source`. 
 To use your own data, copy that files format. Each article to be summarized is on its own line.
 
 ### Evaluation
-
 To create summaries for each article in dataset, run:
 ```bash
-python evaluate_cnn.py <path_to_test.source> test_generations.txt <model-name>  --score_path rouge_scores.txt
+python evaluate_cnn.py <path_to_test.source> cnn_test_summaries.txt
 ```
-The default batch size, 8, fits in 16GB GPU memory, but may need to be adjusted to fit your system.
+the default batch size, 8, fits in 16GB GPU memory, but may need to be adjusted to fit your system.
+
 
 ### Training
-Run/modify `finetune_bart.sh` or `finetune_t5.sh`
+Run/modify `run_train.sh`
+    
+### Where is the code?
+The core model is in `src/transformers/modeling_bart.py`. This directory only contains examples.
+
+## (WIP) Rouge Scores
 
 ### Stanford CoreNLP Setup
 ```

examples/summarization/bart/evaluate_cnn.py

@@ -0,0 +1,71 @@
+import argparse
+from pathlib import Path
+
+import torch
+from tqdm import tqdm
+
+from transformers import BartForConditionalGeneration, BartTokenizer
+
+
+DEFAULT_DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+
+def chunks(lst, n):
+    """Yield successive n-sized chunks from lst."""
+    for i in range(0, len(lst), n):
+        yield lst[i : i + n]
+
+
+def generate_summaries(
+    examples: list, out_file: str, model_name: str, batch_size: int = 8, device: str = DEFAULT_DEVICE
+):
+    fout = Path(out_file).open("w")
+    model = BartForConditionalGeneration.from_pretrained(model_name).to(device)
+    tokenizer = BartTokenizer.from_pretrained("bart-large")
+
+    max_length = 140
+    min_length = 55
+
+    for batch in tqdm(list(chunks(examples, batch_size))):
+        dct = tokenizer.batch_encode_plus(batch, max_length=1024, return_tensors="pt", pad_to_max_length=True)
+        summaries = model.generate(
+            input_ids=dct["input_ids"].to(device),
+            attention_mask=dct["attention_mask"].to(device),
+            num_beams=4,
+            length_penalty=2.0,
+            max_length=max_length + 2,  # +2 from original because we start at step=1 and stop before max_length
+            min_length=min_length + 1,  # +1 from original because we start at step=1
+            no_repeat_ngram_size=3,
+            early_stopping=True,
+            decoder_start_token_id=model.config.eos_token_id,
+        )
+        dec = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summaries]
+        for hypothesis in dec:
+            fout.write(hypothesis + "\n")
+            fout.flush()
+
+
+def run_generate():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "source_path", type=str, help="like cnn_dm/test.source",
+    )
+    parser.add_argument(
+        "output_path", type=str, help="where to save summaries",
+    )
+    parser.add_argument(
+        "model_name", type=str, default="bart-large-cnn", help="like bart-large-cnn",
+    )
+    parser.add_argument(
+        "--device", type=str, required=False, default=DEFAULT_DEVICE, help="cuda, cuda:1, cpu etc.",
+    )
+    parser.add_argument(
+        "--bs", type=int, default=8, required=False, help="batch size: how many to summarize at a time",
+    )
+    args = parser.parse_args()
+    examples = [" " + x.rstrip() for x in open(args.source_path).readlines()]
+    generate_summaries(examples, args.output_path, args.model_name, batch_size=args.bs, device=args.device)
+
+
+if __name__ == "__main__":
+    run_generate()

examples/summarization/bart/run_train.sh

@@ -6,7 +6,7 @@ export OUTPUT_DIR=${CURRENT_DIR}/${OUTPUT_DIR_NAME}
 mkdir -p $OUTPUT_DIR
 
 # Add parent directory to python path to access lightning_base.py
-export PYTHONPATH="../":"${PYTHONPATH}"
+export PYTHONPATH="../../":"${PYTHONPATH}"
 
 python finetune.py \
 --data_dir=./cnn-dailymail/cnn_dm \

examples/summarization/bart/run_train_tiny.sh

@@ -13,7 +13,7 @@ export OUTPUT_DIR=${CURRENT_DIR}/${OUTPUT_DIR_NAME}
 mkdir -p $OUTPUT_DIR
 
 # Add parent directory to python path to access lightning_base.py and utils.py
-export PYTHONPATH="../":"${PYTHONPATH}"
+export PYTHONPATH="../../":"${PYTHONPATH}"
 python finetune.py \
 --data_dir=cnn_tiny/ \
 --model_type=bart \

examples/summarization/bart/test_bart_examples.py

@@ -129,7 +129,7 @@ class TestBartExamples(unittest.TestCase):
         summaries = ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
         _dump_articles((tmp_dir / "train.source"), articles)
         _dump_articles((tmp_dir / "train.target"), summaries)
-        tokenizer = BartTokenizer.from_pretrained("facebook/bart-large")
+        tokenizer = BartTokenizer.from_pretrained("bart-large")
         max_len_source = max(len(tokenizer.encode(a)) for a in articles)
         max_len_target = max(len(tokenizer.encode(a)) for a in summaries)
         trunc_target = 4
@@ -146,34 +146,3 @@ class TestBartExamples(unittest.TestCase):
             # show that targets were truncated
             self.assertEqual(batch["target_ids"].shape[1], trunc_target)  # Truncated
             self.assertGreater(max_len_target, trunc_target)  # Truncated
-
-
-class TestT5Examples(unittest.TestCase):
-    def test_t5_cli(self):
-        output_file_name = "output_t5_sum.txt"
-        score_file_name = "score_t5_sum.txt"
-        articles = ["New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
-        stream_handler = logging.StreamHandler(sys.stdout)
-        logger.addHandler(stream_handler)
-        tmp = Path(tempfile.gettempdir()) / "utest_generations_t5_sum.hypo"
-        with tmp.open("w", encoding="utf-8") as f:
-            f.write("\n".join(articles))
-
-        output_file_name = Path(tempfile.gettempdir()) / "utest_output_t5_sum.hypo"
-        score_file_name = Path(tempfile.gettempdir()) / "utest_score_t5_sum.hypo"
-
-        testargs = [
-            "evaluate_cnn.py",
-            str(tmp),
-            str(output_file_name),
-            "patrickvonplaten/t5-tiny-random",
-            "--reference_path",
-            str(tmp),
-            "--score_path",
-            str(score_file_name),
-        ]
-
-        with patch.object(sys, "argv", testargs):
-            run_generate()
-            self.assertTrue(Path(output_file_name).exists())
-            self.assertTrue(Path(score_file_name).exists())

examples/summarization/bart/utils.py

@@ -3,6 +3,8 @@ import os
 import torch
 from torch.utils.data import Dataset
 
+from transformers.tokenization_utils import trim_batch
+
 
 def encode_file(tokenizer, data_path, max_length, pad_to_max_length=True, return_tensors="pt"):
     examples = []
@@ -15,17 +17,6 @@ def encode_file(tokenizer, data_path, max_length, pad_to_max_length=True, return
     return examples
 
 
-def trim_batch(
-    input_ids, pad_token_id, attention_mask=None,
-):
-    """Remove columns that are populated exclusively by pad_token_id"""
-    keep_column_mask = input_ids.ne(pad_token_id).any(dim=0)
-    if attention_mask is None:
-        return input_ids[:, keep_column_mask]
-    else:
-        return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
-
-
 class SummarizationDataset(Dataset):
     def __init__(
         self,

examples/summarization/bertabs/configuration_bertabs.py

@@ -61,6 +61,7 @@ class BertAbsConfig(PretrainedConfig):
             the decoder.
     """
 
+    pretrained_config_archive_map = BERTABS_FINETUNED_CONFIG_MAP
     model_type = "bertabs"
 
     def __init__(

examples/summarization/bertabs/modeling_bertabs.py

@@ -33,13 +33,14 @@ from transformers import BertConfig, BertModel, PreTrainedModel
 
 MAX_SIZE = 5000
 
-BERTABS_FINETUNED_MODEL_ARCHIVE_LIST = [
-    "remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization",
-]
+BERTABS_FINETUNED_MODEL_MAP = {
+    "bertabs-finetuned-cnndm": "https://cdn.huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin",
+}
 
 
 class BertAbsPreTrainedModel(PreTrainedModel):
     config_class = BertAbsConfig
+    pretrained_model_archive_map = BERTABS_FINETUNED_MODEL_MAP
     load_tf_weights = False
     base_model_prefix = "bert"
 

examples/summarization/evaluate_cnn.py

@@ -1,100 +0,0 @@
-import argparse
-from pathlib import Path
-
-import torch
-from rouge_score import rouge_scorer, scoring
-from tqdm import tqdm
-
-from transformers import AutoModelWithLMHead, AutoTokenizer
-
-
-DEFAULT_DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-
-
-def chunks(lst, n):
-    """Yield successive n-sized chunks from lst."""
-    for i in range(0, len(lst), n):
-        yield lst[i : i + n]
-
-
-def generate_summaries(
-    examples: list, out_file: str, model_name: str, batch_size: int = 8, device: str = DEFAULT_DEVICE
-):
-    fout = Path(out_file).open("w", encoding="utf-8")
-    model = AutoModelWithLMHead.from_pretrained(model_name).to(device)
-
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-    # update config with summarization specific params
-    task_specific_params = model.config.task_specific_params
-    if task_specific_params is not None:
-        model.config.update(task_specific_params.get("summarization", {}))
-
-    for batch in tqdm(list(chunks(examples, batch_size))):
-        if "t5" in model_name:
-            batch = [model.config.prefix + text for text in batch]
-        dct = tokenizer.batch_encode_plus(batch, max_length=1024, return_tensors="pt", pad_to_max_length=True).to(
-            device
-        )
-        summaries = model.generate(**dct)
-
-        dec = tokenizer.batch_decode(summaries, skip_special_tokens=True, clean_up_tokenization_spaces=False)
-        for hypothesis in dec:
-            fout.write(hypothesis + "\n")
-            fout.flush()
-
-
-def calculate_rouge(output_lns, reference_lns, score_path):
-    score_file = Path(score_path).open("w")
-    scorer = rouge_scorer.RougeScorer(["rouge1", "rouge2", "rougeL"], use_stemmer=True)
-    aggregator = scoring.BootstrapAggregator()
-
-    for reference_ln, output_ln in zip(reference_lns, output_lns):
-        scores = scorer.score(reference_ln, output_ln)
-        aggregator.add_scores(scores)
-
-    result = aggregator.aggregate()
-    score_file.write(
-        "ROUGE_1: \n{} \n\n ROUGE_2: \n{} \n\n ROUGE_L: \n{} \n\n".format(
-            result["rouge1"], result["rouge2"], result["rougeL"]
-        )
-    )
-
-
-def run_generate():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "input_path", type=str, help="like cnn_dm/test.source or cnn_dm/test_articles_input.txt",
-    )
-    parser.add_argument(
-        "output_path", type=str, help="where to save summaries",
-    )
-    parser.add_argument(
-        "model_name",
-        type=str,
-        default="facebook/bart-large-cnn",
-        help="like bart-large-cnn,'t5-small', 't5-base', 't5-large', 't5-3b', 't5-11b",
-    )
-    parser.add_argument("--reference_path", type=str, required=False, help="like cnn_dm/test_reference_summaries.txt")
-    parser.add_argument(
-        "--score_path", type=str, required=False, help="where to save the rouge score",
-    )
-    parser.add_argument(
-        "--device", type=str, required=False, default=DEFAULT_DEVICE, help="cuda, cuda:1, cpu etc.",
-    )
-    parser.add_argument(
-        "--bs", type=int, default=8, required=False, help="batch size: how many to summarize at a time",
-    )
-    args = parser.parse_args()
-    examples = [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path).readlines()]
-
-    generate_summaries(examples, args.output_path, args.model_name, batch_size=args.bs, device=args.device)
-    if args.score_path is not None:
-        output_lns = [x.rstrip() for x in open(args.output_path).readlines()]
-        reference_lns = [x.rstrip() for x in open(args.reference_path).readlines()]
-
-        calculate_rouge(output_lns, reference_lns, args.score_path)
-
-
-if __name__ == "__main__":
-    run_generate()

examples/summarization/finetune_t5.sh

@@ -1,19 +0,0 @@
-export OUTPUT_DIR_NAME=t5
-export CURRENT_DIR=${PWD}
-export OUTPUT_DIR=${CURRENT_DIR}/${OUTPUT_DIR_NAME}
-
-# Make output directory if it doesn't exist
-mkdir -p $OUTPUT_DIR
-
-# Add parent directory to python path to access lightning_base.py
-export PYTHONPATH="../":"${PYTHONPATH}"
-
-python finetune.py \
---data_dir=./cnn-dailymail/cnn_dm \
---model_name_or_path=t5-large \
---model_type=t5
---learning_rate=3e-5 \
---train_batch_size=4 \
---eval_batch_size=4 \
---output_dir=$OUTPUT_DIR \
---do_train  $@

examples/summarization/t5/README.md

@@ -0,0 +1,29 @@
+***This script evaluates the the multitask pre-trained checkpoint for ``t5-base`` (see paper [here](https://arxiv.org/pdf/1910.10683.pdf)) on the CNN/Daily Mail test dataset. Please note that the results in the paper were attained using a model fine-tuned on summarization, so that results will be worse here by approx. 0.5 ROUGE points***
+
+### Get the CNN Data
+First, you need to download the CNN data. It's about ~400 MB and can be downloaded by 
+running 
+
+```bash
+python download_cnn_daily_mail.py cnn_articles_input_data.txt cnn_articles_reference_summaries.txt
+```
+
+You should confirm that each file has 11490 lines:
+
+```bash
+wc -l cnn_articles_input_data.txt # should print 11490
+wc -l cnn_articles_reference_summaries.txt # should print 11490
+```
+
+### Generating Summaries
+
+To create summaries for each article in dataset, run:
+```bash
+python evaluate_cnn.py cnn_articles_input_data.txt cnn_generated_articles_summaries.txt cnn_articles_reference_summaries.txt rouge_score.txt
+```
+The default batch size, 8, fits in 16GB GPU memory, but may need to be adjusted to fit your system.
+The rouge scores "rouge1, rouge2, rougeL" are automatically created and saved in ``rouge_score.txt``.
+
+
+### Finetuning
+Pass model_type=t5 and model `examples/summarization/bart/finetune.py`

examples/summarization/t5/download_cnn_daily_mail.py

@@ -0,0 +1,31 @@
+import argparse
+from pathlib import Path
+
+import tensorflow_datasets as tfds
+
+
+def main(input_path, reference_path, data_dir):
+    cnn_ds = tfds.load("cnn_dailymail", split="test", shuffle_files=False, data_dir=data_dir)
+    cnn_ds_iter = tfds.as_numpy(cnn_ds)
+
+    test_articles_file = Path(input_path).open("w")
+    test_summaries_file = Path(reference_path).open("w")
+
+    for example in cnn_ds_iter:
+        test_articles_file.write(example["article"].decode("utf-8") + "\n")
+        test_articles_file.flush()
+        test_summaries_file.write(example["highlights"].decode("utf-8").replace("\n", " ") + "\n")
+        test_summaries_file.flush()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("input_path", type=str, help="where to save the articles input data")
+    parser.add_argument(
+        "reference_path", type=str, help="where to save the reference summaries",
+    )
+    parser.add_argument(
+        "--data_dir", type=str, default="~/tensorflow_datasets", help="where to save the tensorflow datasets.",
+    )
+    args = parser.parse_args()
+    main(args.input_path, args.reference_path, args.data_dir)

examples/summarization/t5/evaluate_cnn.py

@@ -0,0 +1,101 @@
+import argparse
+from pathlib import Path
+
+import torch
+from rouge_score import rouge_scorer, scoring
+from tqdm import tqdm
+
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+
+
+def chunks(lst, n):
+    """Yield successive n-sized chunks from lst."""
+    for i in range(0, len(lst), n):
+        yield lst[i : i + n]
+
+
+def generate_summaries(lns, output_file_path, model_size, batch_size, device):
+    output_file = Path(output_file_path).open("w")
+
+    model = T5ForConditionalGeneration.from_pretrained(model_size)
+    model.to(device)
+
+    tokenizer = T5Tokenizer.from_pretrained(model_size)
+
+    # update config with summarization specific params
+    task_specific_params = model.config.task_specific_params
+    if task_specific_params is not None:
+        model.config.update(task_specific_params.get("summarization", {}))
+
+    for batch in tqdm(list(chunks(lns, batch_size))):
+        batch = [model.config.prefix + text for text in batch]
+
+        dct = tokenizer.batch_encode_plus(batch, max_length=512, return_tensors="pt", pad_to_max_length=True)
+        input_ids = dct["input_ids"].to(device)
+        attention_mask = dct["attention_mask"].to(device)
+
+        summaries = model.generate(input_ids=input_ids, attention_mask=attention_mask)
+        dec = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summaries]
+
+        for hypothesis in dec:
+            output_file.write(hypothesis + "\n")
+            output_file.flush()
+
+
+def calculate_rouge(output_lns, reference_lns, score_path):
+    score_file = Path(score_path).open("w")
+    scorer = rouge_scorer.RougeScorer(["rouge1", "rouge2", "rougeL"], use_stemmer=True)
+    aggregator = scoring.BootstrapAggregator()
+
+    for reference_ln, output_ln in zip(reference_lns, output_lns):
+        scores = scorer.score(reference_ln, output_ln)
+        aggregator.add_scores(scores)
+
+    result = aggregator.aggregate()
+    score_file.write(
+        "ROUGE_1: \n{} \n\n ROUGE_2: \n{} \n\n ROUGE_L: \n{} \n\n".format(
+            result["rouge1"], result["rouge2"], result["rougeL"]
+        )
+    )
+
+
+def run_generate():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "model_size",
+        type=str,
+        help="T5 model size, either 't5-small', 't5-base', 't5-large', 't5-3b', 't5-11b'. Defaults to 't5-base'.",
+        default="t5-base",
+    )
+    parser.add_argument(
+        "input_path", type=str, help="like cnn_dm/test_articles_input.txt",
+    )
+    parser.add_argument(
+        "output_path", type=str, help="where to save summaries",
+    )
+    parser.add_argument("reference_path", type=str, help="like cnn_dm/test_reference_summaries.txt")
+    parser.add_argument(
+        "score_path", type=str, help="where to save the rouge score",
+    )
+    parser.add_argument(
+        "--batch_size", type=int, default=8, required=False, help="batch size: how many to summarize at a time",
+    )
+    parser.add_argument(
+        "--no_cuda", default=False, type=bool, help="Whether to force the execution on CPU.",
+    )
+
+    args = parser.parse_args()
+    args.device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
+
+    source_lns = [x.rstrip() for x in open(args.input_path).readlines()]
+
+    generate_summaries(source_lns, args.output_path, args.model_size, args.batch_size, args.device)
+
+    output_lns = [x.rstrip() for x in open(args.output_path).readlines()]
+    reference_lns = [x.rstrip() for x in open(args.reference_path).readlines()]
+
+    calculate_rouge(output_lns, reference_lns, args.score_path)
+
+
+if __name__ == "__main__":
+    run_generate()

examples/summarization/t5/test_t5_examples.py

@@ -0,0 +1,44 @@
+import logging
+import sys
+import tempfile
+import unittest
+from pathlib import Path
+from unittest.mock import patch
+
+from .evaluate_cnn import run_generate
+
+
+output_file_name = "output_t5_sum.txt"
+score_file_name = "score_t5_sum.txt"
+
+articles = ["New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
+
+logging.basicConfig(level=logging.DEBUG)
+
+logger = logging.getLogger()
+
+
+class TestT5Examples(unittest.TestCase):
+    def test_t5_cli(self):
+        stream_handler = logging.StreamHandler(sys.stdout)
+        logger.addHandler(stream_handler)
+        tmp = Path(tempfile.gettempdir()) / "utest_generations_t5_sum.hypo"
+        with tmp.open("w") as f:
+            f.write("\n".join(articles))
+
+        output_file_name = Path(tempfile.gettempdir()) / "utest_output_t5_sum.hypo"
+        score_file_name = Path(tempfile.gettempdir()) / "utest_score_t5_sum.hypo"
+
+        testargs = [
+            "evaluate_cnn.py",
+            "patrickvonplaten/t5-tiny-random",
+            str(tmp),
+            str(output_file_name),
+            str(tmp),
+            str(score_file_name),
+        ]
+
+        with patch.object(sys, "argv", testargs):
+            run_generate()
+            self.assertTrue(Path(output_file_name).exists())
+            self.assertTrue(Path(score_file_name).exists())

examples/test_examples.py

@@ -61,8 +61,8 @@ class ExamplesTests(unittest.TestCase):
             --do_train
             --do_eval
             --output_dir ./tests/fixtures/tests_samples/temp_dir
-            --per_device_train_batch_size=2
-            --per_device_eval_batch_size=1
+            --per_gpu_train_batch_size=2
+            --per_gpu_eval_batch_size=1
             --learning_rate=1e-4
             --max_steps=10
             --warmup_steps=2

examples/text-classification/README.md

@@ -61,28 +61,17 @@ export GLUE_DIR=/path/to/glue
 export TASK_NAME=MRPC
 
 python run_glue.py \
+  --model_type bert \
   --model_name_or_path bert-base-cased \
   --task_name $TASK_NAME \
   --do_train \
   --do_eval \
   --data_dir $GLUE_DIR/$TASK_NAME \
   --max_seq_length 128 \
-  --per_device_train_batch_size 32 \
+  --per_gpu_train_batch_size 32 \
   --learning_rate 2e-5 \
   --num_train_epochs 3.0 \
   --output_dir /tmp/$TASK_NAME/
-
-python run_nlp_glue.py \
-  --model_name_or_path bert-base-cased \
-  --task_name mrpc \
-  --do_train \
-  --do_eval \
-  --max_seq_length 128 \
-  --per_device_train_batch_size 32 \
-  --learning_rate 2e-5 \
-  --num_train_epochs 3.0 \
-  --output_dir /tmp/mrpc/ \
-  --overwrite_output_dir
 ```
 
 where task name can be one of CoLA, SST-2, MRPC, STS-B, QQP, MNLI, QNLI, RTE, WNLI.
@@ -152,7 +141,7 @@ python run_glue.py \
   --do_eval \
   --data_dir $GLUE_DIR/MRPC/ \
   --max_seq_length 128 \
-  --per_device_train_batch_size 32 \
+  --per_gpu_train_batch_size 32 \
   --learning_rate 2e-5 \
   --num_train_epochs 3.0 \
   --output_dir /tmp/mrpc_output/
@@ -177,7 +166,7 @@ python run_glue.py \
   --do_eval \
   --data_dir $GLUE_DIR/MRPC/ \
   --max_seq_length 128 \
-  --per_device_train_batch_size 32 \
+  --per_gpu_train_batch_size 32 \
   --learning_rate 2e-5 \
   --num_train_epochs 3.0 \
   --output_dir /tmp/mrpc_output/ \
@@ -200,7 +189,7 @@ python -m torch.distributed.launch \
     --do_eval \
     --data_dir $GLUE_DIR/MRPC/ \
     --max_seq_length 128 \
-    --per_device_train_batch_size 8 \
+    --per_gpu_train_batch_size 8 \
     --learning_rate 2e-5 \
     --num_train_epochs 3.0 \
     --output_dir /tmp/mrpc_output/
@@ -232,7 +221,7 @@ python -m torch.distributed.launch \
     --do_eval \
     --data_dir $GLUE_DIR/MNLI/ \
     --max_seq_length 128 \
-    --per_device_train_batch_size 8 \
+    --per_gpu_train_batch_size 8 \
     --learning_rate 2e-5 \
     --num_train_epochs 3.0 \
     --output_dir output_dir \
@@ -269,7 +258,7 @@ TEST RESULTS {'val_loss': tensor(0.0707), 'precision': 0.852427800698191, 'recal
 
 Based on the script [`run_xnli.py`](https://github.com/huggingface/transformers/blob/master/examples/text-classification/run_xnli.py).
 
-[XNLI](https://www.nyu.edu/projects/bowman/xnli/) is a crowd-sourced dataset based on [MultiNLI](http://www.nyu.edu/projects/bowman/multinli/). It is an evaluation benchmark for cross-lingual text representations. Pairs of text are labeled with textual entailment annotations for 15 different languages (including both high-resource language such as English and low-resource languages such as Swahili).
+[XNLI](https://www.nyu.edu/projects/bowman/xnli/) is crowd-sourced dataset based on [MultiNLI](http://www.nyu.edu/projects/bowman/multinli/). It is an evaluation benchmark for cross-lingual text representations. Pairs of text are labeled with textual entailment annotations for 15 different languages (including both high-resource language such as English and low-resource languages such as Swahili).
 
 #### Fine-tuning on XNLI
 
@@ -284,13 +273,14 @@ on a single tesla V100 16GB. The data for XNLI can be downloaded with the follow
 export XNLI_DIR=/path/to/XNLI
 
 python run_xnli.py \
+  --model_type bert \
   --model_name_or_path bert-base-multilingual-cased \
   --language de \
   --train_language en \
   --do_train \
   --do_eval \
   --data_dir $XNLI_DIR \
-  --per_device_train_batch_size 32 \
+  --per_gpu_train_batch_size 32 \
   --learning_rate 5e-5 \
   --num_train_epochs 2.0 \
   --max_seq_length 128 \

examples/text-classification/run_glue.py

@@ -21,7 +21,7 @@ import logging
 import os
 import sys
 from dataclasses import dataclass, field
-from typing import Callable, Dict, Optional
+from typing import Dict, Optional
 
 import numpy as np
 
@@ -134,29 +134,16 @@ def main():
     )
 
     # Get datasets
-    train_dataset = (
-        GlueDataset(data_args, tokenizer=tokenizer, cache_dir=model_args.cache_dir) if training_args.do_train else None
-    )
-    eval_dataset = (
-        GlueDataset(data_args, tokenizer=tokenizer, mode="dev", cache_dir=model_args.cache_dir)
-        if training_args.do_eval
-        else None
-    )
-    test_dataset = (
-        GlueDataset(data_args, tokenizer=tokenizer, mode="test", cache_dir=model_args.cache_dir)
-        if training_args.do_predict
-        else None
-    )
+    train_dataset = GlueDataset(data_args, tokenizer=tokenizer) if training_args.do_train else None
+    eval_dataset = GlueDataset(data_args, tokenizer=tokenizer, mode="dev") if training_args.do_eval else None
+    test_dataset = GlueDataset(data_args, tokenizer=tokenizer, mode="test") if training_args.do_predict else None
 
-    def build_compute_metrics_fn(task_name: str) -> Callable[[EvalPrediction], Dict]:
-        def compute_metrics_fn(p: EvalPrediction):
-            if output_mode == "classification":
-                preds = np.argmax(p.predictions, axis=1)
-            elif output_mode == "regression":
-                preds = np.squeeze(p.predictions)
-            return glue_compute_metrics(task_name, preds, p.label_ids)
-
-        return compute_metrics_fn
+    def compute_metrics(p: EvalPrediction) -> Dict:
+        if output_mode == "classification":
+            preds = np.argmax(p.predictions, axis=1)
+        elif output_mode == "regression":
+            preds = np.squeeze(p.predictions)
+        return glue_compute_metrics(data_args.task_name, preds, p.label_ids)
 
     # Initialize our Trainer
     trainer = Trainer(
@@ -164,7 +151,7 @@ def main():
         args=training_args,
         train_dataset=train_dataset,
         eval_dataset=eval_dataset,
-        compute_metrics=build_compute_metrics_fn(data_args.task_name),
+        compute_metrics=compute_metrics,
     )
 
     # Training
@@ -187,12 +174,9 @@ def main():
         eval_datasets = [eval_dataset]
         if data_args.task_name == "mnli":
             mnli_mm_data_args = dataclasses.replace(data_args, task_name="mnli-mm")
-            eval_datasets.append(
-                GlueDataset(mnli_mm_data_args, tokenizer=tokenizer, mode="dev", cache_dir=model_args.cache_dir)
-            )
+            eval_datasets.append(GlueDataset(mnli_mm_data_args, tokenizer=tokenizer, mode="dev"))
 
         for eval_dataset in eval_datasets:
-            trainer.compute_metrics = build_compute_metrics_fn(eval_dataset.args.task_name)
             eval_result = trainer.evaluate(eval_dataset=eval_dataset)
 
             output_eval_file = os.path.join(
@@ -212,9 +196,7 @@ def main():
         test_datasets = [test_dataset]
         if data_args.task_name == "mnli":
             mnli_mm_data_args = dataclasses.replace(data_args, task_name="mnli-mm")
-            test_datasets.append(
-                GlueDataset(mnli_mm_data_args, tokenizer=tokenizer, mode="test", cache_dir=model_args.cache_dir)
-            )
+            test_datasets.append(GlueDataset(mnli_mm_data_args, tokenizer=tokenizer, mode="test"))
 
         for test_dataset in test_datasets:
             predictions = trainer.predict(test_dataset=test_dataset).predictions

examples/text-classification/run_nlp_glue.py

@@ -1,289 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Finetuning the library models for sequence classification on GLUE (Bert, XLM, XLNet, RoBERTa, Albert, XLM-RoBERTa)."""
-
-
-import dataclasses
-import logging
-import os
-import sys
-from dataclasses import dataclass, field
-from typing import Callable, Dict, Optional
-
-import numpy as np
-
-import torch
-
-import nlp
-
-from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, EvalPrediction
-from transformers import (
-    HfArgumentParser,
-    Trainer,
-    TrainingArguments,
-    glue_compute_metrics,
-    set_seed,
-)
-
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class GlueDataTrainingArguments:
-    """
-    Arguments pertaining to what data we are going to input our model for training and eval.
-
-    Using `HfArgumentParser` we can turn this class
-    into argparse arguments to be able to specify them on
-    the command line.
-    """
-
-    task_name: str = field(metadata={"help": "The name of the task to train and/or evaluate on: "
-        "['cola', 'sst2', 'mrpc', 'qqp', 'stsb', 'mnli', 'mnli_mismatched', 'mnli_matched', 'qnli', 'rte', 'wnli', 'ax']"})
-
-    max_seq_length: int = field(
-        default=128,
-        metadata={
-            "help": "The maximum total input sequence length after tokenization. Sequences longer "
-            "than this will be truncated, sequences shorter will be padded."
-        },
-    )
-
-    def __post_init__(self):
-        self.task_name = self.task_name.lower().replace('-', '')  # We used to allow 'sts-b' for 'stsb'
-
-
-@dataclass
-class ModelArguments:
-    """
-    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
-    """
-
-    model_name_or_path: str = field(
-        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
-    )
-    config_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
-    )
-    tokenizer_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
-    )
-    cache_dir: Optional[str] = field(
-        default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
-    )
-
-
-def main():
-    # See all possible arguments in src/transformers/training_args.py
-    # or by passing the --help flag to this script.
-    # We now keep distinct sets of args, for a cleaner separation of concerns.
-
-    parser = HfArgumentParser((ModelArguments, GlueDataTrainingArguments, TrainingArguments))
-
-    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
-        # If we pass only one argument to the script and it's the path to a json file,
-        # let's parse it to get our arguments.
-        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
-    else:
-        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
-
-    if (
-        os.path.exists(training_args.output_dir)
-        and os.listdir(training_args.output_dir)
-        and training_args.do_train
-        and not training_args.overwrite_output_dir
-    ):
-        raise ValueError(
-            f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
-        )
-
-    # Setup logging
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,
-    )
-    logger.warning(
-        "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
-        training_args.local_rank,
-        training_args.device,
-        training_args.n_gpu,
-        bool(training_args.local_rank != -1),
-        training_args.fp16,
-    )
-    logger.info("Training/evaluation parameters %s", training_args)
-
-    # Set seed
-    set_seed(training_args.seed)
-
-    # Get tokenizer
-    tokenizer = AutoTokenizer.from_pretrained(
-        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
-        cache_dir=model_args.cache_dir,
-        # use_fast=True,
-    )
-
-    # Download, tokenize and prepare datasets for training a PyTorch model
-    task_name = data_args.task_name.replace('-', '')  # We used to allow 'sts-b' for 'stsb'
-    glue = nlp.load_dataset('glue', name=task_name)
-
-    def tokenize(example):
-        return tokenizer.batch_encode_plus(list(zip(example['sentence1'], example['sentence2'])),
-                                           max_length=data_args.max_seq_length,
-                                           pad_to_max_length=True)
-    for split_name, split in glue.items():
-        # Tokenize the dataset (this adds columns to the dataset)
-        glue[split_name] = split.map(tokenize, batched=True)
-        # Set the format of the dataset to output only the column our model can digest
-        glue[split_name].set_format(columns=['input_ids', 'attention_mask', 'token_type_ids', 'label'])
-
-    # Get the splits
-    train_dataset, eval_dataset, test_dataset = None, None, None
-    if training_args.do_train:
-        train_dataset = glue['train']
-    if training_args.do_eval:
-        eval_dataset = glue['validation' + ('_matched' if task_name == 'mnli' else '')]
-    if training_args.do_predict:
-        test_dataset = glue['test' + ('_matched' if task_name == 'mnli' else '')]
-
-    # Define output mode (regression or classification) and num labels (to define the size of the last layer of the model)
-    output_mode = "regression" if 'float' in glue['train'].features['label'].dtype else "classification"
-    if output_mode == "regression":
-        num_labels = 1
-    else:
-        num_labels = len(train_dataset.unique('label'))
-
-
-    if task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
-        "RobertaTokenizer",
-        "RobertaTokenizerFast",
-        "XLMRobertaTokenizer",
-    ):
-        raise NotImplementedError("""
-        # HACK(label indices are swapped in RoBERTa pretrained model)
-        label_list[1], label_list[2] = label_list[2], label_list[1]
-        """)
-
-    # Load pretrained model and tokenizer
-    #
-    # Distributed training:
-    # The .from_pretrained methods guarantee that only one local process can concurrently
-    # download model & vocab.
-
-    config = AutoConfig.from_pretrained(
-        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
-        num_labels=num_labels,
-        finetuning_task=data_args.task_name,
-        cache_dir=model_args.cache_dir,
-    )
-    model = AutoModelForSequenceClassification.from_pretrained(
-        model_args.model_name_or_path,
-        from_tf=bool(".ckpt" in model_args.model_name_or_path),
-        config=config,
-        cache_dir=model_args.cache_dir,
-    )
-
-    def build_compute_metrics_fn(task_name: str) -> Callable[[EvalPrediction], Dict]:
-        def compute_metrics_fn(p: EvalPrediction):
-            if output_mode == "classification":
-                preds = np.argmax(p.predictions, axis=1)
-            elif output_mode == "regression":
-                preds = np.squeeze(p.predictions)
-            return glue_compute_metrics(task_name, preds, p.label_ids)
-
-        return compute_metrics_fn
-
-    # Initialize our Trainer
-    trainer = Trainer(
-        model=model,
-        args=training_args,
-        train_dataset=train_dataset,
-        eval_dataset=eval_dataset,
-        compute_metrics=build_compute_metrics_fn(data_args.task_name),
-    )
-
-    # Training
-    if training_args.do_train:
-        trainer.train(
-            model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None
-        )
-        trainer.save_model()
-        # For convenience, we also re-save the tokenizer to the same directory,
-        # so that you can share your model easily on huggingface.co/models =)
-        if trainer.is_world_master():
-            tokenizer.save_pretrained(training_args.output_dir)
-
-    # Evaluation
-    eval_results = {}
-    if training_args.do_eval:
-        logger.info("*** Evaluate ***")
-
-        # Loop to handle MNLI double evaluation (matched, mis-matched)
-        eval_datasets = [eval_dataset]
-        if data_args.task_name == "mnli":
-            eval_datasets.append(glue["validation_mismatched"])
-
-        for eval_dataset in eval_datasets:
-            trainer.compute_metrics = build_compute_metrics_fn(eval_dataset.args.task_name)
-            eval_result = trainer.evaluate(eval_dataset=eval_dataset)
-
-            output_eval_file = os.path.join(
-                training_args.output_dir, f"eval_results_{eval_dataset.args.task_name}.txt"
-            )
-            if trainer.is_world_master():
-                with open(output_eval_file, "w") as writer:
-                    logger.info("***** Eval results {} *****".format(eval_dataset.args.task_name))
-                    for key, value in eval_result.items():
-                        logger.info("  %s = %s", key, value)
-                        writer.write("%s = %s\n" % (key, value))
-
-            eval_results.update(eval_result)
-
-    if training_args.do_predict:
-        logging.info("*** Test ***")
-        test_datasets = [test_dataset]
-        if data_args.task_name == "mnli":
-            test_datasets.append(glue["test_mismatched"])
-
-        for test_dataset in test_datasets:
-            predictions = trainer.predict(test_dataset=test_dataset).predictions
-            if output_mode == "classification":
-                predictions = np.argmax(predictions, axis=1)
-
-            output_test_file = os.path.join(
-                training_args.output_dir, f"test_results_{test_dataset.args.task_name}.txt"
-            )
-            if trainer.is_world_master():
-                with open(output_test_file, "w") as writer:
-                    logger.info("***** Test results {} *****".format(test_dataset.args.task_name))
-                    writer.write("index\tprediction\n")
-                    for index, item in enumerate(predictions):
-                        if output_mode == "regression":
-                            writer.write("%d\t%3.3f\n" % (index, item))
-                        else:
-                            item = test_dataset.get_labels()[item]
-                            writer.write("%d\t%s\n" % (index, item))
-    return eval_results
-
-
-def _mp_fn(index):
-    # For xla_spawn (TPUs)
-    main()
-
-
-if __name__ == "__main__":
-    main()

examples/text-classification/run_xnli.py

@@ -13,7 +13,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-""" Finetuning multi-lingual models on XNLI (e.g. Bert, DistilBERT, XLM).
+""" Finetuning multi-lingual models on XNLI (Bert, DistilBERT, XLM).
     Adapted from `examples/text-classification/run_glue.py`"""
 
 
@@ -32,9 +32,15 @@ from tqdm import tqdm, trange
 from transformers import (
     WEIGHTS_NAME,
     AdamW,
-    AutoConfig,
-    AutoModelForSequenceClassification,
-    AutoTokenizer,
+    BertConfig,
+    BertForSequenceClassification,
+    BertTokenizer,
+    DistilBertConfig,
+    DistilBertForSequenceClassification,
+    DistilBertTokenizer,
+    XLMConfig,
+    XLMForSequenceClassification,
+    XLMTokenizer,
     get_linear_schedule_with_warmup,
 )
 from transformers import glue_convert_examples_to_features as convert_examples_to_features
@@ -51,6 +57,16 @@ except ImportError:
 
 logger = logging.getLogger(__name__)
 
+ALL_MODELS = sum(
+    (tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, DistilBertConfig, XLMConfig)), ()
+)
+
+MODEL_CLASSES = {
+    "bert": (BertConfig, BertForSequenceClassification, BertTokenizer),
+    "xlm": (XLMConfig, XLMForSequenceClassification, XLMTokenizer),
+    "distilbert": (DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer),
+}
+
 
 def set_seed(args):
     random.seed(args.seed)
@@ -361,12 +377,19 @@ def main():
         required=True,
         help="The input data dir. Should contain the .tsv files (or other data files) for the task.",
     )
+    parser.add_argument(
+        "--model_type",
+        default=None,
+        type=str,
+        required=True,
+        help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()),
+    )
     parser.add_argument(
         "--model_name_or_path",
         default=None,
         type=str,
         required=True,
-        help="Path to pretrained model or model identifier from huggingface.co/models",
+        help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS),
     )
     parser.add_argument(
         "--language",
@@ -398,7 +421,7 @@ def main():
     )
     parser.add_argument(
         "--cache_dir",
-        default=None,
+        default="",
         type=str,
         help="Where do you want to store the pre-trained models downloaded from s3",
     )
@@ -539,23 +562,24 @@ def main():
     if args.local_rank not in [-1, 0]:
         torch.distributed.barrier()  # Make sure only the first process in distributed training will download model & vocab
 
-    config = AutoConfig.from_pretrained(
+    args.model_type = args.model_type.lower()
+    config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
+    config = config_class.from_pretrained(
         args.config_name if args.config_name else args.model_name_or_path,
         num_labels=num_labels,
         finetuning_task=args.task_name,
-        cache_dir=args.cache_dir,
+        cache_dir=args.cache_dir if args.cache_dir else None,
     )
-    args.model_type = config.model_type
-    tokenizer = AutoTokenizer.from_pretrained(
+    tokenizer = tokenizer_class.from_pretrained(
         args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
         do_lower_case=args.do_lower_case,
-        cache_dir=args.cache_dir,
+        cache_dir=args.cache_dir if args.cache_dir else None,
     )
-    model = AutoModelForSequenceClassification.from_pretrained(
+    model = model_class.from_pretrained(
         args.model_name_or_path,
         from_tf=bool(".ckpt" in args.model_name_or_path),
         config=config,
-        cache_dir=args.cache_dir,
+        cache_dir=args.cache_dir if args.cache_dir else None,
     )
 
     if args.local_rank == 0:
@@ -590,13 +614,14 @@ def main():
         torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
 
         # Load a trained model and vocabulary that you have fine-tuned
-        model = AutoModelForSequenceClassification.from_pretrained(args.output_dir)
-        tokenizer = AutoTokenizer.from_pretrained(args.output_dir)
+        model = model_class.from_pretrained(args.output_dir)
+        tokenizer = tokenizer_class.from_pretrained(args.output_dir)
         model.to(args.device)
 
     # Evaluation
     results = {}
     if args.do_eval and args.local_rank in [-1, 0]:
+        tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
         checkpoints = [args.output_dir]
         if args.eval_all_checkpoints:
             checkpoints = list(
@@ -608,7 +633,7 @@ def main():
             global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
             prefix = checkpoint.split("/")[-1] if checkpoint.find("checkpoint") != -1 else ""
 
-            model = AutoModelForSequenceClassification.from_pretrained(checkpoint)
+            model = model_class.from_pretrained(checkpoint)
             model.to(args.device)
             result = evaluate(args, model, tokenizer, prefix=prefix)
             result = dict((k + "_{}".format(global_step), v) for k, v in result.items())

examples/text-generation/pplm/README.md

@@ -17,7 +17,7 @@ Please check out the repo under uber-research for more information: https://gith
 git clone https://github.com/huggingface/transformers && cd transformers
 pip install .
 pip install nltk torchtext # additional requirements.
-cd examples/text-generation/pplm
+cd examples/pplm
 ```
 
 ## PPLM-BoW 

examples/text-generation/pplm/run_pplm.py

@@ -31,6 +31,7 @@ from typing import List, Optional, Tuple, Union
 import numpy as np
 import torch
 import torch.nn.functional as F
+from torch.autograd import Variable
 from tqdm import trange
 
 from pplm_classification_head import ClassificationHead
@@ -75,6 +76,14 @@ DISCRIMINATOR_MODELS_PARAMS = {
 }
 
 
+def to_var(x, requires_grad=False, volatile=False, device="cuda"):
+    if torch.cuda.is_available() and device == "cuda":
+        x = x.cuda()
+    elif device != "cuda":
+        x = x.to(device)
+    return Variable(x, requires_grad=requires_grad, volatile=volatile)
+
+
 def top_k_filter(logits, k, probs=False):
     """
     Masks everything but the k top entries as -infinity (1e10).
@@ -147,10 +156,9 @@ def perturb_past(
     new_accumulated_hidden = None
     for i in range(num_iterations):
         print("Iteration ", i + 1)
-        curr_perturbation = [torch.from_numpy(p_).requires_grad_(True).to(device=device) for p_ in grad_accumulator]
-        # make sure p_.grad is not None
-        for p_ in curr_perturbation:
-            p_.retain_grad()
+        curr_perturbation = [
+            to_var(torch.from_numpy(p_), requires_grad=True, device=device) for p_ in grad_accumulator
+        ]
 
         # Compute hidden using perturbed past
         perturbed_past = list(map(add, past, curr_perturbation))
@@ -239,7 +247,7 @@ def perturb_past(
         past = new_past
 
     # apply the accumulated perturbations to the past
-    grad_accumulator = [torch.from_numpy(p_).requires_grad_(True).to(device=device) for p_ in grad_accumulator]
+    grad_accumulator = [to_var(torch.from_numpy(p_), requires_grad=True, device=device) for p_ in grad_accumulator]
     pert_past = list(map(add, past, grad_accumulator))
 
     return pert_past, new_accumulated_hidden, grad_norms, loss_per_iter
@@ -258,7 +266,7 @@ def get_classifier(
     elif "path" in params:
         resolved_archive_file = params["path"]
     else:
-        raise ValueError("Either url or path have to be specified in the discriminator model parameters")
+        raise ValueError("Either url or path have to be specified " "in the discriminator model parameters")
     classifier.load_state_dict(torch.load(resolved_archive_file, map_location=device))
     classifier.eval()
 
@@ -561,9 +569,9 @@ def generate_text_pplm(
 
 def set_generic_model_params(discrim_weights, discrim_meta):
     if discrim_weights is None:
-        raise ValueError("When using a generic discriminator, discrim_weights need to be specified")
+        raise ValueError("When using a generic discriminator, " "discrim_weights need to be specified")
     if discrim_meta is None:
-        raise ValueError("When using a generic discriminator, discrim_meta need to be specified")
+        raise ValueError("When using a generic discriminator, " "discrim_meta need to be specified")
 
     with open(discrim_meta, "r") as discrim_meta_file:
         meta = json.load(discrim_meta_file)
@@ -611,7 +619,7 @@ def run_pplm_example(
 
     if discrim is not None:
         pretrained_model = DISCRIMINATOR_MODELS_PARAMS[discrim]["pretrained_model"]
-        print("discrim = {}, pretrained_model set to discriminator's = {}".format(discrim, pretrained_model))
+        print("discrim = {}, pretrained_model set " "to discriminator's = {}".format(discrim, pretrained_model))
 
     # load pretrained model
     model = GPT2LMHeadModel.from_pretrained(pretrained_model, output_hidden_states=True)
@@ -698,7 +706,7 @@ def run_pplm_example(
                 for word_id in pert_gen_tok_text.tolist()[0]:
                     if word_id in bow_word_ids:
                         pert_gen_text += "{}{}{}".format(
-                            colorama.Fore.RED, tokenizer.decode([word_id]), colorama.Style.RESET_ALL,
+                            colorama.Fore.RED, tokenizer.decode([word_id]), colorama.Style.RESET_ALL
                         )
                     else:
                         pert_gen_text += tokenizer.decode([word_id])
@@ -736,11 +744,9 @@ if __name__ == "__main__":
         "-B",
         type=str,
         default=None,
-        help=(
-            "Bags of words used for PPLM-BoW. "
-            "Either a BOW id (see list in code) or a filepath. "
-            "Multiple BoWs separated by ;"
-        ),
+        help="Bags of words used for PPLM-BoW. "
+        "Either a BOW id (see list in code) or a filepath. "
+        "Multiple BoWs separated by ;",
     )
     parser.add_argument(
         "--discrim",
@@ -750,11 +756,9 @@ if __name__ == "__main__":
         choices=("clickbait", "sentiment", "toxicity", "generic"),
         help="Discriminator to use",
     )
+    parser.add_argument("--discrim_weights", type=str, default=None, help="Weights for the generic discriminator")
     parser.add_argument(
-        "--discrim_weights", type=str, default=None, help="Weights for the generic discriminator",
-    )
-    parser.add_argument(
-        "--discrim_meta", type=str, default=None, help="Meta information for the generic discriminator",
+        "--discrim_meta", type=str, default=None, help="Meta information for the generic discriminator"
     )
     parser.add_argument(
         "--class_label", type=int, default=-1, help="Class label used for the discriminator",
@@ -770,7 +774,7 @@ if __name__ == "__main__":
         "--window_length",
         type=int,
         default=0,
-        help="Length of past which is being optimized; 0 corresponds to infinite window length",
+        help="Length of past which is being optimized; " "0 corresponds to infinite window length",
     )
     parser.add_argument(
         "--horizon_length", type=int, default=1, help="Length of future to optimize over",

examples/token-classification/README.md

@@ -2,17 +2,10 @@
 
 Based on the scripts [`run_ner.py`](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_ner.py) for Pytorch and
 [`run_tf_ner.py`](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_tf_ner.py) for Tensorflow 2.
-
-The following examples are covered in this section:
-
-* NER on the GermEval 2014 (German NER) dataset
-* Emerging and Rare Entities task: WNUT’17 (English NER) dataset
-
+This example fine-tune Bert Multilingual on GermEval 2014 (German NER).
 Details and results for the fine-tuning provided by @stefan-it.
 
-### GermEval 2014 (German NER) dataset
-
-#### Data (Download and pre-processing steps)
+### Data (Download and pre-processing steps)
 
 Data can be obtained from the [GermEval 2014](https://sites.google.com/site/germeval2014ner/data) shared task page.
 
@@ -27,10 +20,11 @@ curl -L 'https://sites.google.com/site/germeval2014ner/data/NER-de-test.tsv?attr
 | grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > test.txt.tmp
 ```
 
-The GermEval 2014 dataset contains some strange "control character" tokens like `'\x96', '\u200e', '\x95', '\xad' or '\x80'`.
-One problem with these tokens is, that `BertTokenizer` returns an empty token for them, resulting in misaligned `InputExample`s.
-The `preprocess.py` script located in the `scripts` folder a) filters these tokens and b) splits longer sentences into smaller ones (once the max. subtoken length is reached).
+The GermEval 2014 dataset contains some strange "control character" tokens like `'\x96', '\u200e', '\x95', '\xad' or '\x80'`. One problem with these tokens is, that `BertTokenizer` returns an empty token for them, resulting in misaligned `InputExample`s. I wrote a script that a) filters these tokens and b) splits longer sentences into smaller ones (once the max. subtoken length is reached).
 
+```bash
+wget "https://raw.githubusercontent.com/stefan-it/fine-tuned-berts-seq/master/scripts/preprocess.py"
+```
 Let's define some variables that we need for further pre-processing steps and training the model:
 
 ```bash
@@ -41,9 +35,9 @@ export BERT_MODEL=bert-base-multilingual-cased
 Run the pre-processing script on training, dev and test datasets:
 
 ```bash
-python3 scripts/preprocess.py train.txt.tmp $BERT_MODEL $MAX_LENGTH > train.txt
-python3 scripts/preprocess.py dev.txt.tmp $BERT_MODEL $MAX_LENGTH > dev.txt
-python3 scripts/preprocess.py test.txt.tmp $BERT_MODEL $MAX_LENGTH > test.txt
+python3 preprocess.py train.txt.tmp $BERT_MODEL $MAX_LENGTH > train.txt
+python3 preprocess.py dev.txt.tmp $BERT_MODEL $MAX_LENGTH > dev.txt
+python3 preprocess.py test.txt.tmp $BERT_MODEL $MAX_LENGTH > test.txt
 ```
 
 The GermEval 2014 dataset has much more labels than CoNLL-2002/2003 datasets, so an own set of labels must be used:
@@ -52,7 +46,7 @@ The GermEval 2014 dataset has much more labels than CoNLL-2002/2003 datasets, so
 cat train.txt dev.txt test.txt | cut -d " " -f 2 | grep -v "^$"| sort | uniq > labels.txt
 ```
 
-#### Prepare the run
+### Prepare the run
 
 Additional environment variables must be set:
 
@@ -64,7 +58,7 @@ export SAVE_STEPS=750
 export SEED=1
 ```
 
-#### Run the Pytorch version
+### Run the Pytorch version
 
 To start training, just run:
 
@@ -75,7 +69,7 @@ python3 run_ner.py --data_dir ./ \
 --output_dir $OUTPUT_DIR \
 --max_seq_length  $MAX_LENGTH \
 --num_train_epochs $NUM_EPOCHS \
---per_device_train_batch_size $BATCH_SIZE \
+--per_gpu_train_batch_size $BATCH_SIZE \
 --save_steps $SAVE_STEPS \
 --seed $SEED \
 --do_train \
@@ -85,7 +79,7 @@ python3 run_ner.py --data_dir ./ \
 
 If your GPU supports half-precision training, just add the `--fp16` flag. After training, the model will be both evaluated on development and test datasets.
 
-#### JSON-based configuration file
+### JSON-based configuration file
 
 Instead of passing all parameters via commandline arguments, the `run_ner.py` script also supports reading parameters from a json-based configuration file:
 
@@ -97,7 +91,7 @@ Instead of passing all parameters via commandline arguments, the `run_ner.py` sc
     "output_dir": "germeval-model",
     "max_seq_length": 128,
     "num_train_epochs": 3,
-    "per_device_train_batch_size": 32,
+    "per_gpu_train_batch_size": 32,
     "save_steps": 750,
     "seed": 1,
     "do_train": true,
@@ -130,6 +124,16 @@ On the test dataset the following results could be achieved:
 10/04/2019 00:42:42 - INFO - __main__ -     recall = 0.8624150210424085
 ```
 
+#### Comparing BERT (large, cased), RoBERTa (large, cased) and DistilBERT (base, uncased)
+
+Here is a small comparison between BERT (large, cased), RoBERTa (large, cased) and DistilBERT (base, uncased) with the same hyperparameters as specified in the [example documentation](https://huggingface.co/transformers/examples.html#named-entity-recognition) (one run):
+
+| Model | F-Score Dev | F-Score Test
+| --------------------------------- | ------- | --------
+| `bert-large-cased`            | 95.59 | 91.70
+| `roberta-large`                  | 95.96 | 91.87
+| `distilbert-base-uncased` | 94.34 | 90.32
+
 #### Run PyTorch version using PyTorch-Lightning
 
 Run `bash run_pl.sh` from the `ner` directory. This would also install `pytorch-lightning` and the `examples/requirements.txt`. It is a shell pipeline which would automatically download, pre-process the data and run the models in `germeval-model` directory. Logs are saved in `lightning_logs` directory.
@@ -137,7 +141,7 @@ Run `bash run_pl.sh` from the `ner` directory. This would also install `pytorch-
 Pass `--n_gpu` flag to change the number of GPUs. Default uses 1. At the end, the expected results are: `TEST RESULTS {'val_loss': tensor(0.0707), 'precision': 0.852427800698191, 'recall': 0.869537067011978, 'f1': 0.8608974358974358}`
 
 
-#### Run the Tensorflow 2 version
+### Run the Tensorflow 2 version
 
 To start training, just run:
 
@@ -201,102 +205,3 @@ On the test dataset the following results could be achieved:
 micro avg     0.8722    0.8774    0.8748     13869
 macro avg     0.8712    0.8774    0.8740     13869
 ```
-
-### Emerging and Rare Entities task: WNUT’17 (English NER) dataset
-
-Description of the WNUT’17 task from the [shared task website](http://noisy-text.github.io/2017/index.html):
-
-> The WNUT’17 shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions.
-> Named entities form the basis of many modern approaches to other tasks (like event clustering and summarization), but recall on
-> them is a real problem in noisy text - even among annotators. This drop tends to be due to novel entities and surface forms.
-
-Six labels are available in the dataset. An overview can be found on this [page](http://noisy-text.github.io/2017/files/).
-
-#### Data (Download and pre-processing steps)
-
-The dataset can be downloaded from the [official GitHub](https://github.com/leondz/emerging_entities_17) repository.
-
-The following commands show how to prepare the dataset for fine-tuning:
-
-```bash
-mkdir -p data_wnut_17
-
-curl -L 'https://github.com/leondz/emerging_entities_17/raw/master/wnut17train.conll'  | tr '\t' ' ' > data_wnut_17/train.txt.tmp
-curl -L 'https://github.com/leondz/emerging_entities_17/raw/master/emerging.dev.conll' | tr '\t' ' ' > data_wnut_17/dev.txt.tmp
-curl -L 'https://raw.githubusercontent.com/leondz/emerging_entities_17/master/emerging.test.annotated' | tr '\t' ' ' > data_wnut_17/test.txt.tmp
-```
-
-Let's define some variables that we need for further pre-processing steps:
-
-```bash
-export MAX_LENGTH=128
-export BERT_MODEL=bert-large-cased
-```
-
-Here we use the English BERT large model for fine-tuning.
-The `preprocess.py` scripts splits longer sentences into smaller ones (once the max. subtoken length is reached):
-
-```bash
-python3 scripts/preprocess.py data_wnut_17/train.txt.tmp $BERT_MODEL $MAX_LENGTH > data_wnut_17/train.txt
-python3 scripts/preprocess.py data_wnut_17/dev.txt.tmp $BERT_MODEL $MAX_LENGTH > data_wnut_17/dev.txt
-python3 scripts/preprocess.py data_wnut_17/test.txt.tmp $BERT_MODEL $MAX_LENGTH > data_wnut_17/test.txt
-```
-
-In the last pre-processing step, the `labels.txt` file needs to be generated. This file contains all available labels:
-
-```bash
-cat data_wnut_17/train.txt data_wnut_17/dev.txt data_wnut_17/test.txt | cut -d " " -f 2 | grep -v "^$"| sort | uniq > data_wnut_17/labels.txt
-```
-
-#### Run the Pytorch version
-
-Fine-tuning with the PyTorch version can be started using the `run_ner.py` script. In this example we use a JSON-based configuration file.
-
-This configuration file looks like:
-
-```json
-{
-    "data_dir": "./data_wnut_17",
-    "labels": "./data_wnut_17/labels.txt",
-    "model_name_or_path": "bert-large-cased",
-    "output_dir": "wnut-17-model-1",
-    "max_seq_length": 128,
-    "num_train_epochs": 3,
-    "per_device_train_batch_size": 32,
-    "save_steps": 425,
-    "seed": 1,
-    "do_train": true,
-    "do_eval": true,
-    "do_predict": true,
-    "fp16": false
-}
-```
-
-If your GPU supports half-precision training, please set `fp16` to `true`.
-
-Save this JSON-based configuration under `wnut_17.json`. The fine-tuning can be started with `python3 run_ner.py wnut_17.json`.
-
-#### Evaluation
-
-Evaluation on development dataset outputs the following:
-
-```bash
-05/29/2020 23:33:44 - INFO - __main__ -   ***** Eval results *****
-05/29/2020 23:33:44 - INFO - __main__ -     eval_loss = 0.26505235286212275
-05/29/2020 23:33:44 - INFO - __main__ -     eval_precision = 0.7008264462809918
-05/29/2020 23:33:44 - INFO - __main__ -     eval_recall = 0.507177033492823
-05/29/2020 23:33:44 - INFO - __main__ -     eval_f1 = 0.5884802220680084
-05/29/2020 23:33:44 - INFO - __main__ -     epoch = 3.0
-```
-
-On the test dataset the following results could be achieved:
-
-```bash
-05/29/2020 23:33:44 - INFO - transformers.trainer -   ***** Running Prediction *****
-05/29/2020 23:34:02 - INFO - __main__ -     eval_loss = 0.30948806500973547
-05/29/2020 23:34:02 - INFO - __main__ -     eval_precision = 0.5840108401084011
-05/29/2020 23:34:02 - INFO - __main__ -     eval_recall = 0.3994439295644115
-05/29/2020 23:34:02 - INFO - __main__ -     eval_f1 = 0.47440836543753434
-```
-
-WNUT’17 is a very difficult task. Current state-of-the-art results on this dataset can be found [here](http://nlpprogress.com/english/named_entity_recognition.html).

examples/token-classification/run.sh

@@ -4,12 +4,12 @@ curl -L 'https://sites.google.com/site/germeval2014ner/data/NER-de-dev.tsv?attre
 | grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > dev.txt.tmp
 curl -L 'https://sites.google.com/site/germeval2014ner/data/NER-de-test.tsv?attredirects=0&d=1' \
 | grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > test.txt.tmp
-
+wget "https://raw.githubusercontent.com/stefan-it/fine-tuned-berts-seq/master/scripts/preprocess.py"
 export MAX_LENGTH=128
 export BERT_MODEL=bert-base-multilingual-cased
-python3 scripts/preprocess.py train.txt.tmp $BERT_MODEL $MAX_LENGTH > train.txt
-python3 scripts/preprocess.py dev.txt.tmp $BERT_MODEL $MAX_LENGTH > dev.txt
-python3 scripts/preprocess.py test.txt.tmp $BERT_MODEL $MAX_LENGTH > test.txt
+python3 preprocess.py train.txt.tmp $BERT_MODEL $MAX_LENGTH > train.txt
+python3 preprocess.py dev.txt.tmp $BERT_MODEL $MAX_LENGTH > dev.txt
+python3 preprocess.py test.txt.tmp $BERT_MODEL $MAX_LENGTH > test.txt
 cat train.txt dev.txt test.txt | cut -d " " -f 2 | grep -v "^$"| sort | uniq > labels.txt
 export OUTPUT_DIR=germeval-model
 export BATCH_SIZE=32

examples/token-classification/run_ner.py

@@ -13,7 +13,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-""" Fine-tuning the library models for named entity recognition on CoNLL-2003. """
+""" Fine-tuning the library models for named entity recognition on CoNLL-2003 (Bert or Roberta). """
 
 
 import logging

examples/token-classification/run_pl.sh

@@ -11,12 +11,12 @@ curl -L 'https://sites.google.com/site/germeval2014ner/data/NER-de-dev.tsv?attre
 | grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > dev.txt.tmp
 curl -L 'https://sites.google.com/site/germeval2014ner/data/NER-de-test.tsv?attredirects=0&d=1' \
 | grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > test.txt.tmp
-
+wget "https://raw.githubusercontent.com/stefan-it/fine-tuned-berts-seq/master/scripts/preprocess.py"
 export MAX_LENGTH=128
 export BERT_MODEL=bert-base-multilingual-cased
-python3 scripts/preprocess.py train.txt.tmp $BERT_MODEL $MAX_LENGTH > train.txt
-python3 scripts/preprocess.py dev.txt.tmp $BERT_MODEL $MAX_LENGTH > dev.txt
-python3 scripts/preprocess.py test.txt.tmp $BERT_MODEL $MAX_LENGTH > test.txt
+python3 preprocess.py train.txt.tmp $BERT_MODEL $MAX_LENGTH > train.txt
+python3 preprocess.py dev.txt.tmp $BERT_MODEL $MAX_LENGTH > dev.txt
+python3 preprocess.py test.txt.tmp $BERT_MODEL $MAX_LENGTH > test.txt
 cat train.txt dev.txt test.txt | cut -d " " -f 2 | grep -v "^$"| sort | uniq > labels.txt
 export BATCH_SIZE=32
 export NUM_EPOCHS=3

examples/token-classification/run_pl_ner.py

@@ -65,7 +65,7 @@ class NERTransformer(BaseTransformer):
                     cls_token=self.tokenizer.cls_token,
                     cls_token_segment_id=2 if self.config.model_type in ["xlnet"] else 0,
                     sep_token=self.tokenizer.sep_token,
-                    sep_token_extra=False,
+                    sep_token_extra=bool(self.config.model_type in ["roberta"]),
                     pad_on_left=bool(self.config.model_type in ["xlnet"]),
                     pad_token=self.tokenizer.pad_token_id,
                     pad_token_segment_id=self.tokenizer.pad_token_type_id,

examples/token-classification/scripts/preprocess.py

@@ -1,41 +0,0 @@
-import sys
-
-from transformers import AutoTokenizer
-
-
-dataset = sys.argv[1]
-model_name_or_path = sys.argv[2]
-max_len = int(sys.argv[3])
-
-subword_len_counter = 0
-
-tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
-max_len -= tokenizer.num_special_tokens_to_add()
-
-with open(dataset, "rt") as f_p:
-    for line in f_p:
-        line = line.rstrip()
-
-        if not line:
-            print(line)
-            subword_len_counter = 0
-            continue
-
-        token = line.split()[0]
-
-        current_subwords_len = len(tokenizer.tokenize(token))
-
-        # Token contains strange control characters like \x96 or \x95
-        # Just filter out the complete line
-        if current_subwords_len == 0:
-            continue
-
-        if (subword_len_counter + current_subwords_len) > max_len:
-            print("")
-            print(line)
-            subword_len_counter = current_subwords_len
-            continue
-
-        subword_len_counter += current_subwords_len
-
-        print(line)

examples/token-classification/utils_ner.py

@@ -119,7 +119,7 @@ if is_torch_available():
                         cls_token=tokenizer.cls_token,
                         cls_token_segment_id=2 if model_type in ["xlnet"] else 0,
                         sep_token=tokenizer.sep_token,
-                        sep_token_extra=False,
+                        sep_token_extra=bool(model_type in ["roberta"]),
                         # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805
                         pad_on_left=bool(tokenizer.padding_side == "left"),
                         pad_token=tokenizer.pad_token_id,
@@ -172,7 +172,7 @@ if is_tf_available():
                 cls_token=tokenizer.cls_token,
                 cls_token_segment_id=2 if model_type in ["xlnet"] else 0,
                 sep_token=tokenizer.sep_token,
-                sep_token_extra=False,
+                sep_token_extra=bool(model_type in ["roberta"]),
                 # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805
                 pad_on_left=bool(tokenizer.padding_side == "left"),
                 pad_token=tokenizer.pad_token_id,

model_cards/HooshvareLab/bert-base-parsbert-armanner-uncased/README.md

@@ -1,124 +0,0 @@
-## ParsBERT: Transformer-based Model for Persian Language Understanding
-
-ParsBERT is a monolingual language model based on Google’s BERT architecture with the same configurations as BERT-Base. 
-
-Paper presenting ParsBERT: [arXiv:2005.12515](https://arxiv.org/abs/2005.12515)
-
-All the models (downstream tasks) are uncased and trained with whole word masking. (coming soon stay tuned)
-
-
-## Persian NER [ARMAN, PEYMA, ARMAN+PEYMA]
-
-This task aims to extract named entities in the text, such as names and label with appropriate `NER` classes such as locations, organizations, etc. The datasets used for this task contain sentences that are marked with `IOB` format. In this format, tokens that are not part of an entity are tagged as `”O”` the `”B”`tag corresponds to the first word of an object, and the `”I”` tag corresponds to the rest of the terms of the same entity. Both `”B”` and `”I”` tags are followed by a hyphen (or underscore), followed by the entity category. Therefore, the NER task is a multi-class token classification problem that labels the tokens upon being fed a raw text. There are two primary datasets used in Persian NER, `ARMAN`, and `PEYMA`. In ParsBERT, we prepared ner for both datasets as well as a combination of both datasets.
-
-
-
-### PEYMA
-
-PEYMA dataset includes 7,145 sentences with a total of 302,530 tokens from which 41,148 tokens are tagged with seven different classes.
-
-1. Organization
-2. Money
-3. Location
-4. Date
-5. Time
-6. Person
-7. Percent
-
-
-|     Label    |   #   |
-|:------------:|:-----:|
-| Organization | 16964 |
-|     Money    |  2037 |
-|   Location   |  8782 |
-|     Date     |  4259 |
-|     Time     |  732  |
-|    Person    |  7675 |
-|    Percent   |  699  |
-
-
-
-**Download**
-You can download the dataset from [here](http://nsurl.org/tasks/task-7-named-entity-recognition-ner-for-farsi/)
-
----
-
-### ARMAN
-
-ARMAN dataset holds 7,682 sentences with 250,015 sentences tagged over six different classes.
-
-1. Organization
-2. Location
-3. Facility
-4. Event
-5. Product
-6. Person
-
-
-|     Label    |   #   |
-|:------------:|:-----:|
-| Organization | 30108 |
-|   Location   | 12924 |
-|   Facility   |  4458 |
-|     Event    |  7557 |
-|    Product   |  4389 |
-|    Person    | 15645 |
-
-
-
-**Download**
-You can download the dataset from [here](https://github.com/HaniehP/PersianNER)
-
-
-
-## Results
-
-The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.
-
-| Dataset         | ParsBERT | MorphoBERT |  Beheshti-NER  |  LSTM-CRF  |  Rule-Based CRF  |  BiLSTM-CRF  |
-|:---------------:|:--------:|:----------:|:--------------:|:----------:|:----------------:|:------------:|
-|  ARMAN + PEYMA  |   95.13* |      -     |        -       |      -     |         -        |       -      |
-|  PEYMA          |   98.79* |      -     |      90.59     |      -     |       84.00      |       -      |
-|  ARMAN          |   93.10* |    89.9    |      84.03     |    86.55   |         -        |     77.45    |
-
-
-## How to use :hugs:
-| Notebook     |      Description      |   |
-|:----------|:-------------|------:|
-| [How to use Pipelines](https://github.com/hooshvare/parsbert-ner/blob/master/persian-ner-pipeline.ipynb)  | Simple and efficient way to use State-of-the-Art models on downstream tasks through transformers | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/hooshvare/parsbert-ner/blob/master/persian-ner-pipeline.ipynb) |
-
-
-## Cite 
-
-Please cite the following paper in your publication if you are using [ParsBERT](https://arxiv.org/abs/2005.12515) in your research:
-
-```markdown
-@article{ParsBERT,
-    title={ParsBERT: Transformer-based Model for Persian Language Understanding},
-    author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
-    journal={ArXiv},
-    year={2020},
-    volume={abs/2005.12515}
-}
-```
-
-
-## Acknowledgments
-
-We hereby, express our gratitude to the [Tensorflow Research Cloud (TFRC) program](https://tensorflow.org/tfrc) for providing us with the necessary computation resources. We also thank [Hooshvare](https://hooshvare.com) Research Group for facilitating dataset gathering and scraping online text resources.
-
-
-## Contributors
-
-- Mehrdad Farahani: [Linkedin](https://www.linkedin.com/in/m3hrdadfi/), [Twitter](https://twitter.com/m3hrdadfi), [Github](https://github.com/m3hrdadfi)
-- Mohammad Gharachorloo:  [Linkedin](https://www.linkedin.com/in/mohammad-gharachorloo/), [Twitter](https://twitter.com/MGharachorloo), [Github](https://github.com/baarsaam)
-- Marzieh Farahani:  [Linkedin](https://www.linkedin.com/in/marziehphi/), [Twitter](https://twitter.com/marziehphi), [Github](https://github.com/marziehphi)
-- Mohammad Manthouri:  [Linkedin](https://www.linkedin.com/in/mohammad-manthouri-aka-mansouri-07030766/), [Twitter](https://twitter.com/mmanthouri), [Github](https://github.com/mmanthouri)
-- Hooshvare Team:  [Official Website](https://hooshvare.com/), [Linkedin](https://www.linkedin.com/company/hooshvare), [Twitter](https://twitter.com/hooshvare), [Github](https://github.com/hooshvare), [Instagram](https://www.instagram.com/hooshvare/)
-
-+ And a special thanks to Sara Tabrizi for her fantastic poster design. Follow her on: [Linkedin](https://www.linkedin.com/in/sara-tabrizi-64548b79/), [Behance](https://www.behance.net/saratabrizi), [Instagram](https://www.instagram.com/sara_b_tabrizi/)
-
-## Releases
-
-### Release v0.1 (May 29, 2019)
-This is the first version of our ParsBERT NER!

model_cards/HooshvareLab/bert-base-parsbert-ner-uncased/README.md

@@ -1,124 +0,0 @@
-## ParsBERT: Transformer-based Model for Persian Language Understanding
-
-ParsBERT is a monolingual language model based on Google’s BERT architecture with the same configurations as BERT-Base. 
-
-Paper presenting ParsBERT: [arXiv:2005.12515](https://arxiv.org/abs/2005.12515)
-
-All the models (downstream tasks) are uncased and trained with whole word masking. (coming soon stay tuned)
-
-
-## Persian NER [ARMAN, PEYMA, ARMAN+PEYMA]
-
-This task aims to extract named entities in the text, such as names and label with appropriate `NER` classes such as locations, organizations, etc. The datasets used for this task contain sentences that are marked with `IOB` format. In this format, tokens that are not part of an entity are tagged as `”O”` the `”B”`tag corresponds to the first word of an object, and the `”I”` tag corresponds to the rest of the terms of the same entity. Both `”B”` and `”I”` tags are followed by a hyphen (or underscore), followed by the entity category. Therefore, the NER task is a multi-class token classification problem that labels the tokens upon being fed a raw text. There are two primary datasets used in Persian NER, `ARMAN`, and `PEYMA`. In ParsBERT, we prepared ner for both datasets as well as a combination of both datasets.
-
-
-
-### PEYMA
-
-PEYMA dataset includes 7,145 sentences with a total of 302,530 tokens from which 41,148 tokens are tagged with seven different classes.
-
-1. Organization
-2. Money
-3. Location
-4. Date
-5. Time
-6. Person
-7. Percent
-
-
-|     Label    |   #   |
-|:------------:|:-----:|
-| Organization | 16964 |
-|     Money    |  2037 |
-|   Location   |  8782 |
-|     Date     |  4259 |
-|     Time     |  732  |
-|    Person    |  7675 |
-|    Percent   |  699  |
-
-
-
-**Download**
-You can download the dataset from [here](http://nsurl.org/tasks/task-7-named-entity-recognition-ner-for-farsi/)
-
----
-
-### ARMAN
-
-ARMAN dataset holds 7,682 sentences with 250,015 sentences tagged over six different classes.
-
-1. Organization
-2. Location
-3. Facility
-4. Event
-5. Product
-6. Person
-
-
-|     Label    |   #   |
-|:------------:|:-----:|
-| Organization | 30108 |
-|   Location   | 12924 |
-|   Facility   |  4458 |
-|     Event    |  7557 |
-|    Product   |  4389 |
-|    Person    | 15645 |
-
-
-
-**Download**
-You can download the dataset from [here](https://github.com/HaniehP/PersianNER)
-
-
-
-## Results
-
-The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.
-
-| Dataset         | ParsBERT | MorphoBERT |  Beheshti-NER  |  LSTM-CRF  |  Rule-Based CRF  |  BiLSTM-CRF  |
-|:---------------:|:--------:|:----------:|:--------------:|:----------:|:----------------:|:------------:|
-|  ARMAN + PEYMA  |   95.13* |      -     |        -       |      -     |         -        |       -      |
-|  PEYMA          |   98.79* |      -     |      90.59     |      -     |       84.00      |       -      |
-|  ARMAN          |   93.10* |    89.9    |      84.03     |    86.55   |         -        |     77.45    |
-
-
-## How to use :hugs:
-| Notebook     |      Description      |   |
-|:----------|:-------------|------:|
-| [How to use Pipelines](https://github.com/hooshvare/parsbert-ner/blob/master/persian-ner-pipeline.ipynb)  | Simple and efficient way to use State-of-the-Art models on downstream tasks through transformers | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/hooshvare/parsbert-ner/blob/master/persian-ner-pipeline.ipynb) |
-
-
-## Cite 
-
-Please cite the following paper in your publication if you are using [ParsBERT](https://arxiv.org/abs/2005.12515) in your research:
-
-```markdown
-@article{ParsBERT,
-    title={ParsBERT: Transformer-based Model for Persian Language Understanding},
-    author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
-    journal={ArXiv},
-    year={2020},
-    volume={abs/2005.12515}
-}
-```
-
-
-## Acknowledgments
-
-We hereby, express our gratitude to the [Tensorflow Research Cloud (TFRC) program](https://tensorflow.org/tfrc) for providing us with the necessary computation resources. We also thank [Hooshvare](https://hooshvare.com) Research Group for facilitating dataset gathering and scraping online text resources.
-
-
-## Contributors
-
-- Mehrdad Farahani: [Linkedin](https://www.linkedin.com/in/m3hrdadfi/), [Twitter](https://twitter.com/m3hrdadfi), [Github](https://github.com/m3hrdadfi)
-- Mohammad Gharachorloo:  [Linkedin](https://www.linkedin.com/in/mohammad-gharachorloo/), [Twitter](https://twitter.com/MGharachorloo), [Github](https://github.com/baarsaam)
-- Marzieh Farahani:  [Linkedin](https://www.linkedin.com/in/marziehphi/), [Twitter](https://twitter.com/marziehphi), [Github](https://github.com/marziehphi)
-- Mohammad Manthouri:  [Linkedin](https://www.linkedin.com/in/mohammad-manthouri-aka-mansouri-07030766/), [Twitter](https://twitter.com/mmanthouri), [Github](https://github.com/mmanthouri)
-- Hooshvare Team:  [Official Website](https://hooshvare.com/), [Linkedin](https://www.linkedin.com/company/hooshvare), [Twitter](https://twitter.com/hooshvare), [Github](https://github.com/hooshvare), [Instagram](https://www.instagram.com/hooshvare/)
-
-+ And a special thanks to Sara Tabrizi for her fantastic poster design. Follow her on: [Linkedin](https://www.linkedin.com/in/sara-tabrizi-64548b79/), [Behance](https://www.behance.net/saratabrizi), [Instagram](https://www.instagram.com/sara_b_tabrizi/)
-
-## Releases
-
-### Release v0.1 (May 29, 2019)
-This is the first version of our ParsBERT NER!

model_cards/HooshvareLab/bert-base-parsbert-peymaner-uncased/README.md

@@ -1,124 +0,0 @@
-## ParsBERT: Transformer-based Model for Persian Language Understanding
-
-ParsBERT is a monolingual language model based on Google’s BERT architecture with the same configurations as BERT-Base. 
-
-Paper presenting ParsBERT: [arXiv:2005.12515](https://arxiv.org/abs/2005.12515)
-
-All the models (downstream tasks) are uncased and trained with whole word masking. (coming soon stay tuned)
-
-
-## Persian NER [ARMAN, PEYMA, ARMAN+PEYMA]
-
-This task aims to extract named entities in the text, such as names and label with appropriate `NER` classes such as locations, organizations, etc. The datasets used for this task contain sentences that are marked with `IOB` format. In this format, tokens that are not part of an entity are tagged as `”O”` the `”B”`tag corresponds to the first word of an object, and the `”I”` tag corresponds to the rest of the terms of the same entity. Both `”B”` and `”I”` tags are followed by a hyphen (or underscore), followed by the entity category. Therefore, the NER task is a multi-class token classification problem that labels the tokens upon being fed a raw text. There are two primary datasets used in Persian NER, `ARMAN`, and `PEYMA`. In ParsBERT, we prepared ner for both datasets as well as a combination of both datasets.
-
-
-
-### PEYMA
-
-PEYMA dataset includes 7,145 sentences with a total of 302,530 tokens from which 41,148 tokens are tagged with seven different classes.
-
-1. Organization
-2. Money
-3. Location
-4. Date
-5. Time
-6. Person
-7. Percent
-
-
-|     Label    |   #   |
-|:------------:|:-----:|
-| Organization | 16964 |
-|     Money    |  2037 |
-|   Location   |  8782 |
-|     Date     |  4259 |
-|     Time     |  732  |
-|    Person    |  7675 |
-|    Percent   |  699  |
-
-
-
-**Download**
-You can download the dataset from [here](http://nsurl.org/tasks/task-7-named-entity-recognition-ner-for-farsi/)
-
----
-
-### ARMAN
-
-ARMAN dataset holds 7,682 sentences with 250,015 sentences tagged over six different classes.
-
-1. Organization
-2. Location
-3. Facility
-4. Event
-5. Product
-6. Person
-
-
-|     Label    |   #   |
-|:------------:|:-----:|
-| Organization | 30108 |
-|   Location   | 12924 |
-|   Facility   |  4458 |
-|     Event    |  7557 |
-|    Product   |  4389 |
-|    Person    | 15645 |
-
-
-
-**Download**
-You can download the dataset from [here](https://github.com/HaniehP/PersianNER)
-
-
-
-## Results
-
-The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.
-
-| Dataset         | ParsBERT | MorphoBERT |  Beheshti-NER  |  LSTM-CRF  |  Rule-Based CRF  |  BiLSTM-CRF  |
-|:---------------:|:--------:|:----------:|:--------------:|:----------:|:----------------:|:------------:|
-|  ARMAN + PEYMA  |   95.13* |      -     |        -       |      -     |         -        |       -      |
-|  PEYMA          |   98.79* |      -     |      90.59     |      -     |       84.00      |       -      |
-|  ARMAN          |   93.10* |    89.9    |      84.03     |    86.55   |         -        |     77.45    |
-
-
-## How to use :hugs:
-| Notebook     |      Description      |   |
-|:----------|:-------------|------:|
-| [How to use Pipelines](https://github.com/hooshvare/parsbert-ner/blob/master/persian-ner-pipeline.ipynb)  | Simple and efficient way to use State-of-the-Art models on downstream tasks through transformers | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/hooshvare/parsbert-ner/blob/master/persian-ner-pipeline.ipynb) |
-
-
-## Cite 
-
-Please cite the following paper in your publication if you are using [ParsBERT](https://arxiv.org/abs/2005.12515) in your research:
-
-```markdown
-@article{ParsBERT,
-    title={ParsBERT: Transformer-based Model for Persian Language Understanding},
-    author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
-    journal={ArXiv},
-    year={2020},
-    volume={abs/2005.12515}
-}
-```
-
-
-## Acknowledgments
-
-We hereby, express our gratitude to the [Tensorflow Research Cloud (TFRC) program](https://tensorflow.org/tfrc) for providing us with the necessary computation resources. We also thank [Hooshvare](https://hooshvare.com) Research Group for facilitating dataset gathering and scraping online text resources.
-
-
-## Contributors
-
-- Mehrdad Farahani: [Linkedin](https://www.linkedin.com/in/m3hrdadfi/), [Twitter](https://twitter.com/m3hrdadfi), [Github](https://github.com/m3hrdadfi)
-- Mohammad Gharachorloo:  [Linkedin](https://www.linkedin.com/in/mohammad-gharachorloo/), [Twitter](https://twitter.com/MGharachorloo), [Github](https://github.com/baarsaam)
-- Marzieh Farahani:  [Linkedin](https://www.linkedin.com/in/marziehphi/), [Twitter](https://twitter.com/marziehphi), [Github](https://github.com/marziehphi)
-- Mohammad Manthouri:  [Linkedin](https://www.linkedin.com/in/mohammad-manthouri-aka-mansouri-07030766/), [Twitter](https://twitter.com/mmanthouri), [Github](https://github.com/mmanthouri)
-- Hooshvare Team:  [Official Website](https://hooshvare.com/), [Linkedin](https://www.linkedin.com/company/hooshvare), [Twitter](https://twitter.com/hooshvare), [Github](https://github.com/hooshvare), [Instagram](https://www.instagram.com/hooshvare/)
-
-+ And a special thanks to Sara Tabrizi for her fantastic poster design. Follow her on: [Linkedin](https://www.linkedin.com/in/sara-tabrizi-64548b79/), [Behance](https://www.behance.net/saratabrizi), [Instagram](https://www.instagram.com/sara_b_tabrizi/)
-
-## Releases
-
-### Release v0.1 (May 29, 2019)
-This is the first version of our ParsBERT NER!

model_cards/HooshvareLab/bert-base-parsbert-uncased/README.md

@@ -1,124 +0,0 @@
-## ParsBERT: Transformer-based Model for Persian Language Understanding
-
-ParsBERT is a monolingual language model based on Google’s BERT architecture with the same configurations as BERT-Base. 
-
-Paper presenting ParsBERT: [arXiv:2005.12515](https://arxiv.org/abs/2005.12515)
-
-All the models (downstream tasks) are uncased and trained with whole word masking. (coming soon stay tuned)
-
-
----
-
-## Introduction
-
-This model is pre-trained on a large Persian corpus with various writing styles from numerous subjects (e.g., scientific, novels, news) with more than 2M documents. A large subset of this corpus was crawled manually.
-
-As a part of ParsBERT methodology, an extensive pre-processing combining POS tagging and WordPiece segmentation was carried out to bring the corpus into a proper format. This process produces more than 40M true sentences. 
-
-
-## Evaluation
-
-ParsBERT is evaluated on three NLP downstream tasks: Sentiment Analysis (SA), Text Classification, and Named Entity Recognition (NER). For this matter and due to insufficient resources, two large datasets for SA and two for text classification were manually composed, which are available for public use and benchmarking. ParsBERT outperformed all other language models, including multilingual BERT and other hybrid deep learning models for all tasks, improving the state-of-the-art performance in Persian language modeling.
-
-## Results
-
-The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.
-
-
-
-### Sentiment Analysis (SA) task
-
-|           Dataset          |  ParsBERT | mBERT | DeepSentiPers |
-|:--------------------------:|:---------:|:-----:|:-------------:|
-|   Digikala User Comments   |   81.74*  | 80.74 |       -       |
-|   SnappFood User Comments  |   88.12*  | 87.87 |       -       |
-|   SentiPers (Multi Class)  |   71.11*  |   -   |     69.33     |
-|  SentiPers (Binary Class)  |   92.13*  |   -   |     91.98     |
-
-
-
-### Text Classification (TC) task
-
-|      Dataset      | ParsBERT | mBERT |
-|:-----------------:|:--------:|:-----:|
-| Digikala Magazine |   93.59* | 90.72 |
-|    Persian News   |   97.19* | 95.79 |
-
-
-### Named Entity Recognition (NER) task
-
-| Dataset | ParsBERT |  mBERT   | MorphoBERT |  Beheshti-NER  |  LSTM-CRF  |  Rule-Based CRF  |  BiLSTM-CRF  |
-|:-------:|:--------:|:--------:|:----------:|:--------------:|:----------:|:----------------:|:------------:|
-|  PEYMA  |   93.10* |   86.64  |      -     |      90.59     |      -     |       84.00      |       -      |
-|  ARMAN  |   98.79* |   95.89  |    89.9    |      84.03     |    86.55   |         -        |     77.45    |
-
-
-**If you tested ParsBERT on a public dataset and you want to add your results to the table above, open a pull request or contact us. Also make sure to have your code available online so we can add it as a reference**
-
-## How to use
-
-### TensorFlow 2.0
-
-```python
-from transformers import AutoConfig, AutoTokenizer, TFAutoModel
-
-config = AutoConfig.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")
-tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")
-model = AutoModel.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")
-
-text = "ما در هوشواره معتقدیم با انتقال صحیح دانش و آگاهی، همه افراد می‌توانند از ابزارهای هوشمند استفاده کنند. شعار ما هوش مصنوعی برای همه است."
-tokenizer.tokenize(text)
-
->>> ['ما', 'در', 'هوش', '##واره', 'معتقدیم', 'با', 'انتقال', 'صحیح', 'دانش', 'و', 'اگاهی', '،', 'همه', 'افراد', 'میتوانند', 'از', 'ابزارهای', 'هوشمند', 'استفاده', 'کنند', '.', 'شعار', 'ما', 'هوش', 'مصنوعی', 'برای', 'همه', 'است', '.']
-
-```
-
-### Pytorch
-
-```python
-from transformers import AutoConfig, AutoTokenizer, AutoModel
-
-config = AutoConfig.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")
-tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")
-model = AutoModel.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")
-```
-
-
-## NLP Tasks Tutorial 
-
-Coming soon stay tuned
-
-
-## Cite 
-
-Please cite the following paper in your publication if you are using [ParsBERT](https://arxiv.org/abs/2005.12515) in your research:
-
-```markdown
-@article{ParsBERT,
-    title={ParsBERT: Transformer-based Model for Persian Language Understanding},
-    author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
-    journal={ArXiv},
-    year={2020},
-    volume={abs/2005.12515}
-}
-```
-
-
-## Acknowledgments
-
-We hereby, express our gratitude to the [Tensorflow Research Cloud (TFRC) program](https://tensorflow.org/tfrc) for providing us with the necessary computation resources. We also thank [Hooshvare](https://hooshvare.com) Research Group for facilitating dataset gathering and scraping online text resources.
-
-
-## Contributors
-
-- Mehrdad Farahani: [Linkedin](https://www.linkedin.com/in/m3hrdadfi/), [Twitter](https://twitter.com/m3hrdadfi), [Github](https://github.com/m3hrdadfi)
-- Mohammad Gharachorloo:  [Linkedin](https://www.linkedin.com/in/mohammad-gharachorloo/), [Twitter](https://twitter.com/MGharachorloo), [Github](https://github.com/baarsaam)
-- Marzieh Farahani:  [Linkedin](https://www.linkedin.com/in/marziehphi/), [Twitter](https://twitter.com/marziehphi), [Github](https://github.com/marziehphi)
-- Mohammad Manthouri:  [Linkedin](https://www.linkedin.com/in/mohammad-manthouri-aka-mansouri-07030766/), [Twitter](https://twitter.com/mmanthouri), [Github](https://github.com/mmanthouri)
-- Hooshvare Team:  [Official Website](https://hooshvare.com/), [Linkedin](https://www.linkedin.com/company/hooshvare), [Twitter](https://twitter.com/hooshvare), [Github](https://github.com/hooshvare), [Instagram](https://www.instagram.com/hooshvare/)
-
-
-## Releases
-
-### Release v0.1 (May 27, 2019)
-This is the first version of our ParsBERT based on BERT<sub>BASE</sub>

model_cards/NeuML/bert-small-cord19-squad2/README.md

@@ -1,26 +0,0 @@
-# BERT-Small CORD-19 fine-tuned on SQuAD 2.0
-
-[bert-small-cord19 model](https://huggingface.co/NeuML/bert-small-cord19) fine-tuned on SQuAD 2.0
-
-## Building the model
-
-```bash
-python run_squad.py
-    --model_type bert
-    --model_name_or_path bert-small-cord19
-    --do_train
-    --do_eval
-    --do_lower_case
-    --version_2_with_negative
-    --train_file train-v2.0.json
-    --predict_file dev-v2.0.json
-    --per_gpu_train_batch_size 8
-    --learning_rate 3e-5
-    --num_train_epochs 3.0
-    --max_seq_length 384
-    --doc_stride 128
-    --output_dir bert-small-cord19-squad2
-    --save_steps 0
-    --threads 8
-    --overwrite_cache
-    --overwrite_output_dir

model_cards/NeuML/bert-small-cord19/README.md

@@ -1,25 +0,0 @@
-# BERT-Small fine-tuned on CORD-19 dataset
-
-[BERT L6_H-512_A-8 model](https://huggingface.co/google/bert_uncased_L-6_H-512_A-8) fine-tuned on the [CORD-19 dataset](https://www.semanticscholar.org/cord19).
-
-## CORD-19 data subset
-The training data for this dataset is stored as a [Kaggle dataset](https://www.kaggle.com/davidmezzetti/cord19-qa?select=cord19.txt). The training
-data is a subset of the full corpus, focusing on high-quality, study-design detected articles.
-
-## Building the model
-
-```bash
-python run_language_modeling.py
-    --model_type bert
-    --model_name_or_path google/bert_uncased_L-6_H-512_A-8
-    --do_train
-    --mlm
-    --line_by_line
-    --block_size 512
-    --train_data_file cord19.txt
-    --per_gpu_train_batch_size 4
-    --learning_rate 3e-5
-    --num_train_epochs 3.0
-    --output_dir bert-small-cord19
-    --save_steps 0
-    --overwrite_output_dir

model_cards/NeuML/bert-small-cord19qa/README.md

@@ -1,63 +0,0 @@
-# BERT-Small fine-tuned on CORD-19 QA dataset
-
-[bert-small-cord19-squad model](https://huggingface.co/NeuML/bert-small-cord19-squad2) fine-tuned on the [CORD-19 QA dataset](https://www.kaggle.com/davidmezzetti/cord19-qa?select=cord19-qa.json).
-
-## CORD-19 QA dataset
-The CORD-19 QA dataset is a SQuAD 2.0 formatted list of question, context, answer combinations covering the [CORD-19 dataset](https://www.semanticscholar.org/cord19).
-
-## Building the model
-
-```bash
-python run_squad.py \
-    --model_type bert \
-    --model_name_or_path bert-small-cord19-squad \
-    --do_train \
-    --do_lower_case \
-    --version_2_with_negative \
-    --train_file cord19-qa.json \
-    --per_gpu_train_batch_size 8 \
-    --learning_rate 5e-5 \
-    --num_train_epochs 10.0 \
-    --max_seq_length 384 \
-    --doc_stride 128 \
-    --output_dir bert-small-cord19qa \
-    --save_steps 0 \
-    --threads 8 \
-    --overwrite_cache \
-    --overwrite_output_dir
-```
-
-## Testing the model
-
-Example usage below:
-
-```python
-from transformers import pipeline
-
-qa = pipeline(
-    "question-answering",
-    model="NeuML/bert-small-cord19qa",
-    tokenizer="NeuML/bert-small-cord19qa"
-)
-
-qa({
-    "question": "What is the median incubation period?",
-    "context": "The incubation period is around 5 days (range: 4-7 days) with a maximum of 12-13 day"
-})
-
-qa({
-    "question": "What is the incubation period range?",
-    "context": "The incubation period is around 5 days (range: 4-7 days) with a maximum of 12-13 day"
-})
-
-qa({
-    "question": "What type of surfaces does it persist?",
-    "context": "The virus can survive on surfaces for up to 72 hours such as plastic and stainless steel ."
-})
-```
-
-```json
-{"score": 0.5970273583242793, "start": 32, "end": 38, "answer": "5 days"}
-{"score": 0.999555868193891, "start": 39, "end": 56, "answer": "(range: 4-7 days)"}
-{"score": 0.9992726505196998, "start": 61, "end": 88, "answer": "plastic and stainless steel"}
-```

model_cards/allenai/longformer-base-4096-extra.pos.embd.only/README.md

@@ -1,20 +0,0 @@
-
-# longformer-base-4096-extra.pos.embd.only
-
-This model is similar to `longformer-base-4096` but it was pretrained to preserve RoBERTa weights by freezing all RoBERTa weights and only train the additional position embeddings. 
-
-
-### Citing
-
-If you use `Longformer` in your research, please cite [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150).
-```
-@article{Beltagy2020Longformer,
-  title={Longformer: The Long-Document Transformer},
-  author={Iz Beltagy and Matthew E. Peters and Arman Cohan},
-  journal={arXiv:2004.05150},
-  year={2020},
-}
-```
-
-`Longformer` is an open-source project developed by [the Allen Institute for Artificial Intelligence (AI2)](http://www.allenai.org).
-AI2 is a non-profit institute with the mission to contribute to humanity through high-impact AI research and engineering.

model_cards/allenai/longformer-base-4096/README.md

@@ -1,24 +0,0 @@
-
-# longformer-base-4096
-[Longformer](https://arxiv.org/abs/2004.05150) is a transformer model for long documents. 
-
-`longformer-base-4096` is a BERT-like model started from the RoBERTa checkpoint and pretrained for MLM on long documents. It supports sequences of length up to 4,096. 
- 
-Longformer uses a combination of a sliding window (local) attention and global attention. Global attention is user-configured based on the task to allow the model to learn task-specific representations.
-Please refer to the examples in `modeling_longformer.py` and the paper for more details on how to set global attention.
-
-
-### Citing
-
-If you use `Longformer` in your research, please cite [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150).
-```
-@article{Beltagy2020Longformer,
-  title={Longformer: The Long-Document Transformer},
-  author={Iz Beltagy and Matthew E. Peters and Arman Cohan},
-  journal={arXiv:2004.05150},
-  year={2020},
-}
-```
-
-`Longformer` is an open-source project developed by [the Allen Institute for Artificial Intelligence (AI2)](http://www.allenai.org).
-AI2 is a non-profit institute with the mission to contribute to humanity through high-impact AI research and engineering.

model_cards/canwenxu/BERT-of-Theseus-MNLI/README.md

@@ -1,7 +1,5 @@
 ---
 thumbnail: https://raw.githubusercontent.com/JetRunner/BERT-of-Theseus/master/bert-of-theseus.png
-datasets:
-- multi_nli
 ---
 
 # BERT-of-Theseus

model_cards/deepset/roberta-base-squad2/README.md

@@ -1,8 +1,3 @@
----
-datasets:
-- squad_v2
----
-
 # roberta-base for QA 
 
 ## Overview

model_cards/dkleczek/bert-base-polish-cased-v1/README.md

@@ -1,135 +0,0 @@
----
-language: polish
-thumbnail: https://raw.githubusercontent.com/kldarek/polbert/master/img/polbert.png
----
-
-# Polbert - Polish BERT
-Polish version of BERT language model is here! It is now available in two variants: cased and uncased, both can be downloaded and used via HuggingFace transformers library. I recommend using the cased model, more info on the differences and benchmark results below. 
-
-![PolBERT image](https://raw.githubusercontent.com/kldarek/polbert/master/img/polbert.png)
-
-## Cased and uncased variants
-
-* I initially trained the uncased model, the corpus and training details are referenced below. Here are some issues I found after I published the uncased model:
-    * Some Polish characters and accents are not tokenized correctly through the BERT tokenizer when applying lowercase. This doesn't impact sequence classification much, but may influence token classfication tasks significantly.
-    * I noticed a lot of duplicates in the Open Subtitles dataset, which dominates the training corpus.
-    * I didn't use Whole Word Masking. 
-* The cased model improves on the uncased model in the following ways:
-    * All Polish characters and accents should now be tokenized correctly. 
-    * I removed duplicates from Open Subtitles dataset. The corpus is smaller, but more balanced now. 
-    * The model is trained with Whole Word Masking. 
-
-## Pre-training corpora
-
-Below is the list of corpora used along with the output of `wc` command (counting lines, words and characters). These corpora were divided into sentences with srxsegmenter (see references), concatenated and tokenized with HuggingFace BERT Tokenizer. 
-
-### Uncased
-
-| Tables        | Lines           | Words  | Characters  |
-| ------------- |--------------:| -----:| -----:|
-| [Polish subset of Open Subtitles](http://opus.nlpl.eu/OpenSubtitles-v2018.php)      | 236635408| 1431199601 | 7628097730 |
-| [Polish subset of ParaCrawl](http://opus.nlpl.eu/ParaCrawl.php)     | 8470950      |   176670885 | 1163505275 |
-| [Polish Parliamentary Corpus](http://clip.ipipan.waw.pl/PPC) | 9799859      |    121154785 | 938896963 |
-| [Polish Wikipedia - Feb 2020](https://dumps.wikimedia.org/plwiki/latest/plwiki-latest-pages-articles.xml.bz2) | 8014206      |    132067986 | 1015849191 |
-| Total | 262920423      |    1861093257 | 10746349159 |
-
-### Cased
-
-| Tables        | Lines           | Words  | Characters  |
-| ------------- |--------------:| -----:| -----:|
-| [Polish subset of Open Subtitles (Deduplicated) ](http://opus.nlpl.eu/OpenSubtitles-v2018.php)      | 41998942| 213590656 | 1424873235 |
-| [Polish subset of ParaCrawl](http://opus.nlpl.eu/ParaCrawl.php)     | 8470950      |   176670885 | 1163505275 |
-| [Polish Parliamentary Corpus](http://clip.ipipan.waw.pl/PPC) | 9799859      |    121154785 | 938896963 |
-| [Polish Wikipedia - Feb 2020](https://dumps.wikimedia.org/plwiki/latest/plwiki-latest-pages-articles.xml.bz2) | 8014206      |    132067986 | 1015849191 |
-| Total | 68283960      |    646479197 | 4543124667 |
-
-
-## Pre-training details
-
-### Uncased 
-
-* Polbert was trained with code provided in Google BERT's github repository (https://github.com/google-research/bert)
-* Currently released model follows bert-base-uncased model architecture (12-layer, 768-hidden, 12-heads, 110M parameters)
-* Training set-up: in total 1 million training steps: 
-    * 100.000 steps - 128 sequence length, batch size 512, learning rate 1e-4 (10.000 steps warmup)
-    * 800.000 steps - 128 sequence length, batch size 512, learning rate 5e-5
-    * 100.000 steps - 512 sequence length, batch size 256, learning rate 2e-5
-* The model was trained on a single Google Cloud TPU v3-8 
-
-### Cased
-
-* Same approach as uncased model, with the following differences:
-    * Whole Word Masking
-* Training set-up:
-    * 100.000 steps - 128 sequence length, batch size 2048, learning rate 1e-4 (10.000 steps warmup)
-    * 100.000 steps - 128 sequence length, batch size 2048, learning rate 5e-5
-    * 100.000 steps - 512 sequence length, batch size 256, learning rate 2e-5
-
-
-## Usage
-Polbert is released via [HuggingFace Transformers library](https://huggingface.co/transformers/).
-
-For an example use as language model, see [this notebook](/LM_testing.ipynb) file. 
-
-### Uncased
-
-```python
-from transformers import *
-model = BertForMaskedLM.from_pretrained("dkleczek/bert-base-polish-uncased-v1")
-tokenizer = BertTokenizer.from_pretrained("dkleczek/bert-base-polish-uncased-v1")
-nlp = pipeline('fill-mask', model=model, tokenizer=tokenizer)
-for pred in nlp(f"Adam Mickiewicz wielkim polskim {nlp.tokenizer.mask_token} był."):
-  print(pred)
-# Output:
-# {'sequence': '[CLS] adam mickiewicz wielkim polskim poeta był. [SEP]', 'score': 0.47196975350379944, 'token': 26596}
-# {'sequence': '[CLS] adam mickiewicz wielkim polskim bohaterem był. [SEP]', 'score': 0.09127858281135559, 'token': 10953}
-# {'sequence': '[CLS] adam mickiewicz wielkim polskim człowiekiem był. [SEP]', 'score': 0.0647173821926117, 'token': 5182}
-# {'sequence': '[CLS] adam mickiewicz wielkim polskim pisarzem był. [SEP]', 'score': 0.05232388526201248, 'token': 24293}
-# {'sequence': '[CLS] adam mickiewicz wielkim polskim politykiem był. [SEP]', 'score': 0.04554257541894913, 'token': 44095}
-```
-
-### Cased
-
-```python
-model = BertForMaskedLM.from_pretrained("dkleczek/bert-base-polish-cased-v1")
-tokenizer = BertTokenizer.from_pretrained("dkleczek/bert-base-polish-cased-v1")
-nlp = pipeline('fill-mask', model=model, tokenizer=tokenizer)
-for pred in nlp(f"Adam Mickiewicz wielkim polskim {nlp.tokenizer.mask_token} był."):
-  print(pred)
-# Output:
-# {'sequence': '[CLS] Adam Mickiewicz wielkim polskim pisarzem był. [SEP]', 'score': 0.5391148328781128, 'token': 37120}
-# {'sequence': '[CLS] Adam Mickiewicz wielkim polskim człowiekiem był. [SEP]', 'score': 0.11683262139558792, 'token': 6810}
-# {'sequence': '[CLS] Adam Mickiewicz wielkim polskim bohaterem był. [SEP]', 'score': 0.06021466106176376, 'token': 17709}
-# {'sequence': '[CLS] Adam Mickiewicz wielkim polskim mistrzem był. [SEP]', 'score': 0.051870670169591904, 'token': 14652}
-# {'sequence': '[CLS] Adam Mickiewicz wielkim polskim artystą był. [SEP]', 'score': 0.031787533313035965, 'token': 35680}
-```
-
-See the next section for an example usage of Polbert in downstream tasks. 
-
-## Evaluation
-Thanks to Allegro, we now have the [KLEJ benchmark](https://klejbenchmark.com/leaderboard/), a set of nine evaluation tasks for the Polish language understanding. The following results are achieved by running standard set of evaluation scripts (no tricks!) utilizing both cased and uncased variants of Polbert.
-
-| Model	| Average |	NKJP-NER | CDSC-E |	CDSC-R |	CBD	| PolEmo2.0-IN |	PolEmo2.0-OUT |	DYK	| PSC |	AR |
-| ------------- |--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|
-| Polbert cased | 81.7 |	93.6 |	93.4 |	93.8 |	52.7 |	87.4 |	71.1 |	59.1 |	98.6 |	85.2 |
-| Polbert uncased | 81.4 |	90.1 |	93.9 |	93.5 |	55.0 |	88.1 |	68.8 |	59.4 |	98.8 |	85.4 |
-
-Note how the uncased model performs better than cased on some tasks? My guess this is because of the oversampling of Open Subtitles dataset and its similarity to data in some of these tasks. All these benchmark tasks are sequence classification, so the relative strength of the cased model is not so visible here. 
-
-## Bias
-The data used to train the model is biased. It may reflect stereotypes related to gender, ethnicity etc. Please be careful when using the model for downstream task to consider these biases and mitigate them.  
-
-## Acknowledgements
-* I'd like to express my gratitude to Google [TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc) for providing the free TPU credits - thank you!
-* Also appreciate the help from Timo Möller from [deepset](https://deepset.ai) for sharing tips and scripts based on their experience training German BERT model.
-* Big thanks to Allegro for releasing KLEJ Benchmark and specifically to Piotr Rybak for help with the evaluation and pointing out some issues with the tokenization. 
-* Finally, thanks to Rachel Thomas, Jeremy Howard and Sylvain Gugger from [fastai](https://www.fast.ai) for their NLP and Deep Learning courses! 
-
-## Author
-Darek Kłeczek - contact me on Twitter [@dk21](https://twitter.com/dk21)
-
-## References
-* https://github.com/google-research/bert
-* https://github.com/narusemotoki/srx_segmenter
-* SRX rules file for sentence splitting in Polish, written by Marcin Miłkowski: https://raw.githubusercontent.com/languagetool-org/languagetool/master/languagetool-core/src/main/resources/org/languagetool/resource/segment.srx
-* [KLEJ benchmark](https://klejbenchmark.com/leaderboard/)

model_cards/dkleczek/bert-base-polish-uncased-v1/README.md

@@ -4,27 +4,14 @@ thumbnail: https://raw.githubusercontent.com/kldarek/polbert/master/img/polbert.
 ---
 
 # Polbert - Polish BERT
-Polish version of BERT language model is here! It is now available in two variants: cased and uncased, both can be downloaded and used via HuggingFace transformers library. I recommend using the cased model, more info on the differences and benchmark results below. 
+Polish version of BERT language model is here! While this is still work in progress, I'm happy to share the first model, similar to BERT-Base and trained on a large Polish corpus. If you'd like to contribute to this project, please reach out to me!
 
 ![PolBERT image](https://raw.githubusercontent.com/kldarek/polbert/master/img/polbert.png)
 
-## Cased and uncased variants
-
-* I initially trained the uncased model, the corpus and training details are referenced below. Here are some issues I found after I published the uncased model:
-    * Some Polish characters and accents are not tokenized correctly through the BERT tokenizer when applying lowercase. This doesn't impact sequence classification much, but may influence token classfication tasks significantly.
-    * I noticed a lot of duplicates in the Open Subtitles dataset, which dominates the training corpus.
-    * I didn't use Whole Word Masking. 
-* The cased model improves on the uncased model in the following ways:
-    * All Polish characters and accents should now be tokenized correctly. 
-    * I removed duplicates from Open Subtitles dataset. The corpus is smaller, but more balanced now. 
-    * The model is trained with Whole Word Masking. 
-
 ## Pre-training corpora
 
 Below is the list of corpora used along with the output of `wc` command (counting lines, words and characters). These corpora were divided into sentences with srxsegmenter (see references), concatenated and tokenized with HuggingFace BERT Tokenizer. 
 
-### Uncased
-
 | Tables        | Lines           | Words  | Characters  |
 | ------------- |--------------:| -----:| -----:|
 | [Polish subset of Open Subtitles](http://opus.nlpl.eu/OpenSubtitles-v2018.php)      | 236635408| 1431199601 | 7628097730 |
@@ -33,21 +20,7 @@ Below is the list of corpora used along with the output of `wc` command (countin
 | [Polish Wikipedia - Feb 2020](https://dumps.wikimedia.org/plwiki/latest/plwiki-latest-pages-articles.xml.bz2) | 8014206      |    132067986 | 1015849191 |
 | Total | 262920423      |    1861093257 | 10746349159 |
 
-### Cased
-
-| Tables        | Lines           | Words  | Characters  |
-| ------------- |--------------:| -----:| -----:|
-| [Polish subset of Open Subtitles (Deduplicated) ](http://opus.nlpl.eu/OpenSubtitles-v2018.php)      | 41998942| 213590656 | 1424873235 |
-| [Polish subset of ParaCrawl](http://opus.nlpl.eu/ParaCrawl.php)     | 8470950      |   176670885 | 1163505275 |
-| [Polish Parliamentary Corpus](http://clip.ipipan.waw.pl/PPC) | 9799859      |    121154785 | 938896963 |
-| [Polish Wikipedia - Feb 2020](https://dumps.wikimedia.org/plwiki/latest/plwiki-latest-pages-articles.xml.bz2) | 8014206      |    132067986 | 1015849191 |
-| Total | 68283960      |    646479197 | 4543124667 |
-
-
 ## Pre-training details
-
-### Uncased 
-
 * Polbert was trained with code provided in Google BERT's github repository (https://github.com/google-research/bert)
 * Currently released model follows bert-base-uncased model architecture (12-layer, 768-hidden, 12-heads, 110M parameters)
 * Training set-up: in total 1 million training steps: 
@@ -56,22 +29,10 @@ Below is the list of corpora used along with the output of `wc` command (countin
     * 100.000 steps - 512 sequence length, batch size 256, learning rate 2e-5
 * The model was trained on a single Google Cloud TPU v3-8 
 
-### Cased
-
-* Same approach as uncased model, with the following differences:
-    * Whole Word Masking
-* Training set-up:
-    * 100.000 steps - 128 sequence length, batch size 2048, learning rate 1e-4 (10.000 steps warmup)
-    * 100.000 steps - 128 sequence length, batch size 2048, learning rate 5e-5
-    * 100.000 steps - 512 sequence length, batch size 256, learning rate 2e-5
-
-
 ## Usage
 Polbert is released via [HuggingFace Transformers library](https://huggingface.co/transformers/).
 
-For an example use as language model, see [this notebook](/LM_testing.ipynb) file. 
-
-### Uncased
+For an example use as language model, see [this notebook](https://github.com/kldarek/polbert/blob/master/LM_testing.ipynb) file. 
 
 ```python
 from transformers import *
@@ -80,6 +41,7 @@ tokenizer = BertTokenizer.from_pretrained("dkleczek/bert-base-polish-uncased-v1"
 nlp = pipeline('fill-mask', model=model, tokenizer=tokenizer)
 for pred in nlp(f"Adam Mickiewicz wielkim polskim {nlp.tokenizer.mask_token} był."):
   print(pred)
+
 # Output:
 # {'sequence': '[CLS] adam mickiewicz wielkim polskim poeta był. [SEP]', 'score': 0.47196975350379944, 'token': 26596}
 # {'sequence': '[CLS] adam mickiewicz wielkim polskim bohaterem był. [SEP]', 'score': 0.09127858281135559, 'token': 10953}
@@ -88,42 +50,23 @@ for pred in nlp(f"Adam Mickiewicz wielkim polskim {nlp.tokenizer.mask_token} by
 # {'sequence': '[CLS] adam mickiewicz wielkim polskim politykiem był. [SEP]', 'score': 0.04554257541894913, 'token': 44095}
 ```
 
-### Cased
-
-```python
-model = BertForMaskedLM.from_pretrained("dkleczek/bert-base-polish-cased-v1")
-tokenizer = BertTokenizer.from_pretrained("dkleczek/bert-base-polish-cased-v1")
-nlp = pipeline('fill-mask', model=model, tokenizer=tokenizer)
-for pred in nlp(f"Adam Mickiewicz wielkim polskim {nlp.tokenizer.mask_token} był."):
-  print(pred)
-# Output:
-# {'sequence': '[CLS] Adam Mickiewicz wielkim polskim pisarzem był. [SEP]', 'score': 0.5391148328781128, 'token': 37120}
-# {'sequence': '[CLS] Adam Mickiewicz wielkim polskim człowiekiem był. [SEP]', 'score': 0.11683262139558792, 'token': 6810}
-# {'sequence': '[CLS] Adam Mickiewicz wielkim polskim bohaterem był. [SEP]', 'score': 0.06021466106176376, 'token': 17709}
-# {'sequence': '[CLS] Adam Mickiewicz wielkim polskim mistrzem był. [SEP]', 'score': 0.051870670169591904, 'token': 14652}
-# {'sequence': '[CLS] Adam Mickiewicz wielkim polskim artystą był. [SEP]', 'score': 0.031787533313035965, 'token': 35680}
-```
-
 See the next section for an example usage of Polbert in downstream tasks. 
 
 ## Evaluation
-Thanks to Allegro, we now have the [KLEJ benchmark](https://klejbenchmark.com/leaderboard/), a set of nine evaluation tasks for the Polish language understanding. The following results are achieved by running standard set of evaluation scripts (no tricks!) utilizing both cased and uncased variants of Polbert.
+I'd love to get some help from the Polish NLP community here! If you feel like evaluating Polbert on some benchmark tasks, it would be great if you can share the results. 
 
-| Model	| Average |	NKJP-NER | CDSC-E |	CDSC-R |	CBD	| PolEmo2.0-IN |	PolEmo2.0-OUT |	DYK	| PSC |	AR |
-| ------------- |--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|--------------:|
-| Polbert cased | 81.7 |	93.6 |	93.4 |	93.8 |	52.7 |	87.4 |	71.1 |	59.1 |	98.6 |	85.2 |
-| Polbert uncased | 81.4 |	90.1 |	93.9 |	93.5 |	55.0 |	88.1 |	68.8 |	59.4 |	98.8 |	85.4 |
+So far, I've compared the performance of Polbert vs Multilingual BERT on PolEmo 2.0 sentiment classification, here are the results. These results are are produced with a linear classification layer on top of pooled output, trained for 10 epochs with learning rate 3e-5. The checkpoint with the lowest loss on validation set is evaluated on the test set. 
 
-Note how the uncased model performs better than cased on some tasks? My guess this is because of the oversampling of Open Subtitles dataset and its similarity to data in some of these tasks. All these benchmark tasks are sequence classification, so the relative strength of the cased model is not so visible here. 
+| PolEmo 2.0 Sentiment Classifcation | Test Accuracy | 
+| ------------- |--------------:|
+| Multilingual BERT | 0.78 |
+| Polbert | 0.85 |
 
 ## Bias
 The data used to train the model is biased. It may reflect stereotypes related to gender, ethnicity etc. Please be careful when using the model for downstream task to consider these biases and mitigate them.  
 
 ## Acknowledgements
-* I'd like to express my gratitude to Google [TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc) for providing the free TPU credits - thank you!
-* Also appreciate the help from Timo Möller from [deepset](https://deepset.ai) for sharing tips and scripts based on their experience training German BERT model.
-* Big thanks to Allegro for releasing KLEJ Benchmark and specifically to Piotr Rybak for help with the evaluation and pointing out some issues with the tokenization. 
-* Finally, thanks to Rachel Thomas, Jeremy Howard and Sylvain Gugger from [fastai](https://www.fast.ai) for their NLP and Deep Learning courses! 
+I'd like to express my gratitude to Google [TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc) for providing the free TPU credits - thank you! Also appreciate the help from Timo Möller from [deepset](https://deepset.ai) for sharing tips and scripts based on their experience training German BERT model. Finally, thanks to Rachel Thomas, Jeremy Howard and Sylvain Gugger from [fastai](https://www.fast.ai) for their NLP and Deep Learning courses!
 
 ## Author
 Darek Kłeczek - contact me on Twitter [@dk21](https://twitter.com/dk21)
@@ -132,4 +75,5 @@ Darek Kłeczek - contact me on Twitter [@dk21](https://twitter.com/dk21)
 * https://github.com/google-research/bert
 * https://github.com/narusemotoki/srx_segmenter
 * SRX rules file for sentence splitting in Polish, written by Marcin Miłkowski: https://raw.githubusercontent.com/languagetool-org/languagetool/master/languagetool-core/src/main/resources/org/languagetool/resource/segment.srx
-* [KLEJ benchmark](https://klejbenchmark.com/leaderboard/)
+* PolEmo 2.0 Sentiment Analysis Dataset for CoNLL: https://clarin-pl.eu/dspace/handle/11321/710
+

model_cards/facebook/bart-large/README.md

@@ -1,10 +0,0 @@
-The Bart model was proposed by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019. According to the abstract,
-
-Bart uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a left-to-right decoder (like GPT).
-
-The pretraining task involves randomly shuffling the order of the original sentences and a novel in-filling scheme, where spans of text are replaced with a single mask token.
-
-BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE.
-
-The Authors’ code can be found here:
-https://github.com/pytorch/fairseq/tree/master/examples/bart