Partially fix space only input without special tokens added to the output #3091

Signed-off-by: Morgan Funtowicz <morgan@huggingface.co>
2020-03-06 11:20:53 +01:00
74 changed files with 802 additions and 3336 deletions
@@ -1,11 +1,10 @@
 name: Self-hosted runner (push)

 on: 
-  # push:
-  #   branches:
-  #     - master
-  # pull_request:
-  repository_dispatch:
+  push:
+    branches:
+      - master
+  pull_request:


 jobs:
@@ -32,12 +31,12 @@ jobs:
      run: |
        source .env/bin/activate
        pip install .[sklearn,tf,torch,testing]
-        pip uninstall -y tensorflow

    - name: Are GPUs recognized by our DL frameworks
      run: |
        source .env/bin/activate
        python -c "import torch; print(torch.cuda.is_available())"
+        python -c "import tensorflow as tf; print(tf.test.is_built_with_cuda(), tf.config.list_physical_devices('GPU'))"

    - name: Run all non-slow tests on GPU
      env:
@@ -471,7 +471,7 @@ python ./examples/run_generation.py \

 Starting with `v2.2.2`, you can now upload and share your fine-tuned models with the community, using the <abbr title="Command-line interface">CLI</abbr> that's built-in to the library.

-**First, create an account on [https://huggingface.co/join](https://huggingface.co/join)**. Optionally, join an existing organization or create a new one. Then:
+**First, create an account on [https://huggingface.co/join](https://huggingface.co/join)**. Then:

 ```shell
 transformers-cli login
@@ -490,26 +490,19 @@ transformers-cli upload ./config.json [--filename folder/foobar.json]
 # (you can optionally override its filename, which can be nested inside a folder)
 ```

-If you want your model to be namespaced by your organization name rather than your username, add the following flag to any command:
-```shell
--organization organization_name
-```
-
-Your model will then be accessible through its identifier, a concatenation of your username (or organization name) and the folder name above:
+Your model will then be accessible through its identifier, a concatenation of your username and the folder name above:
 ```python
 "username/pretrained_model"
-# or if an org:
-"organization_name/pretrained_model"
 ```

-**Please add a README.md model card** to the repo under `model_cards/` with: model description, training params (dataset, preprocessing, hardware used, hyperparameters), evaluation results, intended uses & limitations, etc.
+**Please add a README.md model card** to the repo under `model_cards/` with: model description, training params (dataset, preprocessing, hyperparameters), evaluation results, intended uses & limitations, etc.

 Your model now has a page on huggingface.co/models 🔥

 Anyone can load it from code:
 ```python
-tokenizer = AutoTokenizer.from_pretrained("namespace/pretrained_model")
-model = AutoModel.from_pretrained("namespace/pretrained_model")
+tokenizer = AutoTokenizer.from_pretrained("username/pretrained_model")
+model = AutoModel.from_pretrained("username/pretrained_model")
 ```

 List all your files on S3:
@@ -2,7 +2,7 @@

 Starting with `v2.2.2`, you can now upload and share your fine-tuned models with the community, using the <abbr title="Command-line interface">CLI</abbr> that's built-in to the library.

-**First, create an account on [https://huggingface.co/join](https://huggingface.co/join)**. Optionally, join an existing organization or create a new one. Then:
+**First, create an account on [https://huggingface.co/join](https://huggingface.co/join)**. Then:

 ```shell
 transformers-cli login
@@ -21,26 +21,19 @@ transformers-cli upload ./config.json [--filename folder/foobar.json]
 # (you can optionally override its filename, which can be nested inside a folder)
 ```

-If you want your model to be namespaced by your organization name rather than your username, add the following flag to any command:
-```shell
--organization organization_name
-```
-
-Your model will then be accessible through its identifier, a concatenation of your username (or organization name) and the folder name above:
+Your model will then be accessible through its identifier, a concatenation of your username and the folder name above:
 ```python
 "username/pretrained_model"
-# or if an org:
-"organization_name/pretrained_model"
 ```

-**Please add a README.md model card** to the repo under `model_cards/` with: model description, training params (dataset, preprocessing, hardware used, hyperparameters), evaluation results, intended uses & limitations, etc.
+**Please add a README.md model card** to the repo under `model_cards/` with: model description, training params (dataset, preprocessing, hyperparameters), evaluation results, intended uses & limitations, etc.

 Your model now has a page on huggingface.co/models 🔥

 Anyone can load it from code:
 ```python
-tokenizer = AutoTokenizer.from_pretrained("namespace/pretrained_model")
-model = AutoModel.from_pretrained("namespace/pretrained_model")
+tokenizer = AutoTokenizer.from_pretrained("username/pretrained_model")
+model = AutoModel.from_pretrained("username/pretrained_model")
 ```

 List all your files on S3:
@@ -52,4 +45,4 @@ You can also delete unneeded files:

 ```shell
 transformers-cli s3 rm …
-```
+```
@@ -379,7 +379,7 @@ export SQUAD_DIR=/path/to/SQUAD

 python run_squad.py \
  --model_type bert \
-  --model_name_or_path bert-base-uncased \
+  --model_name_or_path bert-base-cased \
  --do_train \
  --do_eval \
  --do_lower_case \
@@ -112,13 +112,6 @@ Here is a small comparison between BERT (large, cased), RoBERTa (large, cased) a
 | `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.
-
-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

 To start training, just run:
@@ -1,9 +1,6 @@
-#!/usr/bin/env bash
-
 # Install newest ptl.
 pip install -U git+http://github.com/PyTorchLightning/pytorch-lightning/
-# for seqeval metrics import
-pip install -r ../requirements.txt
+

 curl -L 'https://sites.google.com/site/germeval2014ner/data/NER-de-train.tsv?attredirects=0&d=1' \
 | grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > train.txt.tmp
@@ -18,15 +15,12 @@ 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
 export NUM_EPOCHS=3
+export SAVE_STEPS=750
 export SEED=1

-export OUTPUT_DIR_NAME=germeval-model
-export CURRENT_DIR=${PWD}
-export OUTPUT_DIR=${CURRENT_DIR}/${OUTPUT_DIR_NAME}
-mkdir -p $OUTPUT_DIR
-
 python3 run_pl_ner.py --data_dir ./ \
 --model_type bert \
 --labels ./labels.txt \
@@ -35,6 +29,7 @@ python3 run_pl_ner.py --data_dir ./ \
 --max_seq_length  $MAX_LENGTH \
 --num_train_epochs $NUM_EPOCHS \
 --train_batch_size 32 \
+--save_steps $SAVE_STEPS \
 --seed $SEED \
 --do_train \
--do_predict
+--do_predict
@@ -141,14 +141,10 @@ class NERTransformer(BaseTransformer):
        return ret, preds_list, out_label_list

    def validation_end(self, outputs):
-        # todo: update to validation_epoch_end instead of deprecated validation_end
-        # when stable
        ret, preds, targets = self._eval_end(outputs)
-        logs = ret["log"]
-        return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
+        return ret

-    def test_epoch_end(self, outputs):
-        # updating to test_epoch_end instead of deprecated test_end
+    def test_end(self, outputs):
        ret, predictions, targets = self._eval_end(outputs)

        if self.is_logger():
@@ -176,12 +172,7 @@ class NERTransformer(BaseTransformer):
                            logger.warning(
                                "Maximum sequence length exceeded: No prediction for '%s'.", line.split()[0]
                            )
-        # Converting to the dic required by pl
-        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
-        # pytorch_lightning/trainer/logging.py#L139
-        logs = ret["log"]
-        # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
-        return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
+        return ret

    @staticmethod
    def add_model_specific_args(parser, root_dir):
@@ -226,10 +217,6 @@ if __name__ == "__main__":
    trainer = generic_train(model, args)

    if args.do_predict:
-        # See https://github.com/huggingface/transformers/issues/3159
-        # pl use this format to create a checkpoint:
-        # https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
-        # /pytorch_lightning/callbacks/model_checkpoint.py#L169
-        checkpoints = list(sorted(glob.glob(args.output_dir + "/checkpointepoch=*.ckpt", recursive=True)))
+        checkpoints = list(sorted(glob.glob(args.output_dir + "/checkpoint_*.ckpt", recursive=True)))
        NERTransformer.load_from_checkpoint(checkpoints[-1])
        trainer.test(model)
@@ -1,4 +1,4 @@
-### Get the CNN Data
+### Get the CNN/Daily Mail 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
@@ -32,7 +32,6 @@ unzip stanford-corenlp-full-2018-10-05.zip
 cd stanford-corenlp-full-2018-10-05
 export CLASSPATH=stanford-corenlp-3.9.2.jar:stanford-corenlp-3.9.2-models.jar
 ```
-Then run `ptb_tokenize` on `test.target` and your generated hypotheses.
 ### Rouge Setup
 Install `files2rouge` following the instructions at [here](https://github.com/pltrdy/files2rouge).
 I also needed to run `sudo apt-get install libxml-parser-perl`
@@ -18,7 +18,7 @@ def chunks(lst, n):

 def generate_summaries(lns, out_file, batch_size=8, device=DEFAULT_DEVICE):
    fout = Path(out_file).open("w")
-    model = BartForConditionalGeneration.from_pretrained("bart-large-cnn", output_past=True,).to(device)
+    model = BartForConditionalGeneration.from_pretrained("bart-large-cnn", output_past=True,)
    tokenizer = BartTokenizer.from_pretrained("bart-large")
    for batch in tqdm(list(chunks(lns, batch_size))):
        dct = tokenizer.batch_encode_plus(batch, max_length=1024, return_tensors="pt", pad_to_max_length=True)
@@ -8,7 +8,11 @@ language:

 # bert-base-bg-cs-pl-ru-cased

-SlavicBERT\[1\] \(Slavic \(bg, cs, pl, ru\), cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters\) was trained on Russian News and four Wikipedias: Bulgarian, Czech, Polish, and Russian. Subtoken vocabulary was built using this data. Multilingual BERT was used as an initialization for SlavicBERT.
+SlavicBERT\[1\] \(Slavic \(bg, cs, pl, ru\), cased, 12-layer, 768-hidden, 12-heads, 180M parameters\) was trained
+on Russian News and four Wikipedias: Bulgarian, Czech, Polish, and Russian.
+Subtoken vocabulary was built using this data. Multilingual BERT was used as an initialization for SlavicBERT.


-\[1\]: Arkhipov M., Trofimova M., Kuratov Y., Sorokin A. \(2019\). [Tuning Multilingual Transformers for Language-Specific Named Entity Recognition](https://www.aclweb.org/anthology/W19-3712/). ACL anthology W19-3712.
+\[1\]: Arkhipov M., Trofimova M., Kuratov Y., Sorokin A. \(2019\).
+[Tuning Multilingual Transformers for Language-Specific Named Entity Recognition](https://www.aclweb.org/anthology/W19-3712/).
+ACL anthology W19-3712.
@@ -5,13 +5,19 @@ language:

 # bert-base-cased-conversational

-Conversational BERT \(English, cased, 12‑layer, 768‑hidden, 12‑heads, 110M parameters\) was trained on the English part of Twitter, Reddit, DailyDialogues\[1\], OpenSubtitles\[2\], Debates\[3\], Blogs\[4\], Facebook News Comments. We used this training data to build the vocabulary of English subtokens and took English cased version of BERT‑base as an initialization for English Conversational BERT.
+Conversational BERT \(English, cased, 12-layer, 768-hidden, 12-heads, 110M parameters\) was trained
+on the English part of Twitter, Reddit, DailyDialogues\[1\], OpenSubtitles\[2\], Debates\[3\], Blogs\[4\],
+Facebook News Comments. We used this training data to build the vocabulary of English subtokens and took
+English cased version of BERT-base as an initialization for English Conversational BERT.


-\[1\]: Yanran Li, Hui Su, Xiaoyu Shen, Wenjie Li, Ziqiang Cao, and Shuzi Niu. DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset. IJCNLP 2017.
+\[1\]: Yanran Li, Hui Su, Xiaoyu Shen, Wenjie Li, Ziqiang Cao, and Shuzi Niu. DailyDialog: A Manually Labelled
+Multi-turn Dialogue Dataset. IJCNLP 2017.

-\[2\]: P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation \(LREC 2016\)
+\[2\]: P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles.
+In Proceedings of the 10th International Conference on Language Resources and Evaluation \(LREC 2016\)

 \[3\]: Justine Zhang, Ravi Kumar, Sujith Ravi, Cristian Danescu-Niculescu-Mizil. Proceedings of NAACL, 2016.

-\[4\]: J. Schler, M. Koppel, S. Argamon and J. Pennebaker \(2006\). Effects of Age and Gender on Blogging in Proceedings of 2006 AAAI Spring Symposium on Computational Approaches for Analyzing Weblogs.
+\[4\]: J. Schler, M. Koppel, S. Argamon and J. Pennebaker \(2006\). Effects of Age and Gender on Blogging
+in Proceedings of 2006 AAAI Spring Symposium on Computational Approaches for Analyzing Weblogs.
@@ -5,11 +5,18 @@ language:

 # bert-base-multilingual-cased-sentence

-Sentence Multilingual BERT \(101 languages, cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters\) is a representation‑based sentence encoder for 101 languages of Multilingual BERT. It is initialized with Multilingual BERT and then fine‑tuned on english MultiNLI\[1\] and on dev set of multilingual XNLI\[2\]. Sentence representations are mean pooled token embeddings in the same manner as in Sentence‑BERT\[3\].
+Sentence Multilingual BERT \(101 languages, cased, 12-layer, 768-hidden, 12-heads, 180M parameters\)
+is a representation-based sentence encoder for 101 languages of Multilingual BERT.
+It is initialized with Multilingual BERT and then fine-tuned on english MultiNLI\[1\] and on dev set
+of multilingual XNLI\[2\].
+Sentence representations are mean pooled token embeddings in the same manner as in Sentence-BERT\[3\].


-\[1\]: Williams A., Nangia N. & Bowman S. \(2017\) A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference. arXiv preprint [arXiv:1704.05426](https://arxiv.org/abs/1704.05426)
+\[1\]: Williams A., Nangia N. & Bowman S. \(2017\) A Broad-Coverage Challenge Corpus for Sentence Understanding
+through Inference. arXiv preprint [arXiv:1704.05426](https://arxiv.org/abs/1704.05426)

-\[2\]: Williams A., Bowman S. \(2018\) XNLI: Evaluating Cross-lingual Sentence Representations. arXiv preprint [arXiv:1809.05053](https://arxiv.org/abs/1809.05053)
+\[2\]: Williams A., Bowman S. \(2018\) XNLI: Evaluating Cross-lingual Sentence Representations.
+arXiv preprint [arXiv:1809.05053](https://arxiv.org/abs/1809.05053)

-\[3\]: N. Reimers, I. Gurevych \(2019\) Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. arXiv preprint [arXiv:1908.10084](https://arxiv.org/abs/1908.10084)
+\[3\]: N. Reimers, I. Gurevych \(2019\) Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.
+arXiv preprint [arXiv:1908.10084](https://arxiv.org/abs/1908.10084)
@@ -5,9 +5,14 @@ language:

 # rubert-base-cased-conversational

-Conversational RuBERT \(Russian, cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters\) was trained on OpenSubtitles\[1\], [Dirty](https://d3.ru/), [Pikabu](https://pikabu.ru/), and a Social Media segment of Taiga corpus\[2\]. We assembled a new vocabulary for Conversational RuBERT model on this data and initialized the model with [RuBERT](../rubert-base-cased).
+Conversational RuBERT \(Russian, cased, 12-layer, 768-hidden, 12-heads, 180M parameters\) was trained
+on OpenSubtitles\[1\], [Dirty](https://d3.ru/), [Pikabu](https://pikabu.ru/),
+and a Social Media segment of Taiga corpus\[2\]. We assembled a new vocabulary for Conversational RuBERT model
+on this data and initialized the model with [RuBERT](../rubert-base-cased).


-\[1\]: P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation \(LREC 2016\)
+\[1\]: P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles.
+In Proceedings of the 10th International Conference on Language Resources and Evaluation \(LREC 2016\)

-\[2\]: Shavrina T., Shapovalova O. \(2017\) TO THE METHODOLOGY OF CORPUS CONSTRUCTION FOR MACHINE LEARNING: «TAIGA» SYNTAX TREE CORPUS AND PARSER. in proc. of “CORPORA2017”, international conference , Saint-Petersbourg, 2017.
+\[2\]: Shavrina T., Shapovalova O. \(2017\) TO THE METHODOLOGY OF CORPUS CONSTRUCTION FOR MACHINE LEARNING:
+«TAIGA» SYNTAX TREE CORPUS AND PARSER. in proc. of “CORPORA2017”, international conference , Saint-Petersbourg, 2017.
@@ -5,11 +5,17 @@ language:

 # rubert-base-cased-sentence

-Sentence RuBERT \(Russian, cased, 12-layer, 768-hidden, 12-heads, 180M parameters\) is a representation‑based sentence encoder for Russian. It is initialized with RuBERT and fine‑tuned on SNLI\[1\] google-translated to russian and on russian part of XNLI dev set\[2\]. Sentence representations are mean pooled token embeddings in the same manner as in Sentence‑BERT\[3\].
+Sentence RuBERT \(Russian, cased, 12-layer, 768-hidden, 12-heads, 180M parameters\)
+is a representation-based sentence encoder for Russian. It is initialized with RuBERT and fine-tuned on SNLI\[1\]
+google-translated to russian and on russian part of XNLI dev set\[2\]. Sentence representations are mean pooled
+token embeddings in the same manner as in Sentence-BERT\[3\].


-\[1\]: S. R. Bowman, G. Angeli, C. Potts, and C. D. Manning. \(2015\) A large annotated corpus for learning natural language inference. arXiv preprint [arXiv:1508.05326](https://arxiv.org/abs/1508.05326)
+\[1\]: S. R. Bowman, G. Angeli, C. Potts, and C. D. Manning. \(2015\) A large annotated corpus for learning
+natural language inference. arXiv preprint [arXiv:1508.05326](https://arxiv.org/abs/1508.05326)

-\[2\]: Williams A., Bowman S. \(2018\) XNLI: Evaluating Cross-lingual Sentence Representations. arXiv preprint [arXiv:1809.05053](https://arxiv.org/abs/1809.05053)
+\[2\]: Williams A., Bowman S. \(2018\) XNLI: Evaluating Cross-lingual Sentence Representations.
+arXiv preprint [arXiv:1809.05053](https://arxiv.org/abs/1809.05053)

-\[3\]: N. Reimers, I. Gurevych \(2019\) Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. arXiv preprint [arXiv:1908.10084](https://arxiv.org/abs/1908.10084)
+\[3\]: N. Reimers, I. Gurevych \(2019\) Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.
+arXiv preprint [arXiv:1908.10084](https://arxiv.org/abs/1908.10084)
@@ -5,7 +5,10 @@ language:

 # rubert-base-cased

-RuBERT \(Russian, cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters\) was trained on the Russian part of Wikipedia and news data. We used this training data to build a vocabulary of Russian subtokens and took a multilingual version of BERT‑base as an initialization for RuBERT\[1\].
+RuBERT \(Russian, cased, 12-layer, 768-hidden, 12-heads, 180M parameters\) was trained on the Russian part of Wikipedia
+and news data. We used this training data to build a vocabulary of Russian subtokens and took a multilingual version
+of BERT-base as an initialization for RuBERT\[1\].


-\[1\]: Kuratov, Y., Arkhipov, M. \(2019\). Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language. arXiv preprint [arXiv:1905.07213](https://arxiv.org/abs/1905.07213).
+\[1\]: Kuratov, Y., Arkhipov, M. \(2019\). Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language.
+arXiv preprint [arXiv:1905.07213](https://arxiv.org/abs/1905.07213).
@@ -1,76 +0,0 @@
---
-language: turkish
---
-
-# 🤗 + 📚 dbmdz Distilled Turkish BERT model
-
-In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State
-Library open sources a (cased) distilled model for Turkish 🎉
-
-# 🇹🇷 DistilBERTurk
-
-DistilBERTurk is a community-driven cased distilled BERT model for Turkish.
-
-DistilBERTurk was trained on 7GB of the original training data that was used
-for training [BERTurk](https://github.com/stefan-it/turkish-bert/tree/master#stats),
-using the cased version of BERTurk as teacher model.
-
-*DistilBERTurk* was trained with the official Hugging Face implementation from
-[here](https://github.com/huggingface/transformers/tree/master/examples/distillation)
-for 5 days on 4 RTX 2080 TI.
-
-More details about distillation can be found in the
-["DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter"](https://arxiv.org/abs/1910.01108)
-paper by Sanh et al. (2019).
-
-## Model weights
-
-Currently only PyTorch-[Transformers](https://github.com/huggingface/transformers)
-compatible weights are available. If you need access to TensorFlow checkpoints,
-please raise an issue in the [BERTurk](https://github.com/stefan-it/turkish-bert) repository!
-
-| Model                             | Downloads
-| --------------------------------- | ---------------------------------------------------------------------------------------------------------------
-| `dbmdz/distilbert-base-turkish-cased`   | [`config.json`](https://cdn.huggingface.co/dbmdz/distilbert-base-turkish-cased/config.json) • [`pytorch_model.bin`](https://cdn.huggingface.co/dbmdz/distilbert-base-turkish-cased/pytorch_model.bin) • [`vocab.txt`](https://cdn.huggingface.co/dbmdz/distilbert-base-turkish-cased/vocab.txt)
-
-## Usage
-
-With Transformers >= 2.3 our DistilBERTurk model can be loaded like:
-
-```python
-from transformers import AutoModel, AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("dbmdz/distilbert-base-turkish-cased")
-model = AutoModel.from_pretrained("dbmdz/distilbert-base-turkish-cased")
-```
-
-## Results
-
-For results on PoS tagging or NER tasks, please refer to
-[this repository](https://github.com/stefan-it/turkish-bert).
-
-For PoS tagging, DistilBERTurk outperforms the 24-layer XLM-RoBERTa model.
-
-The overall performance difference between DistilBERTurk and the original
-(teacher) BERTurk model is ~1.18%.
-
-# Huggingface model hub
-
-All models are available on the [Huggingface model hub](https://huggingface.co/dbmdz).
-
-# Contact (Bugs, Feedback, Contribution and more)
-
-For questions about our BERT models just open an issue
-[here](https://github.com/dbmdz/berts/issues/new) 🤗
-
-# Acknowledgments
-
-Thanks to [Kemal Oflazer](http://www.andrew.cmu.edu/user/ko/) for providing us
-additional large corpora for Turkish. Many thanks to Reyyan Yeniterzi for providing
-us the Turkish NER dataset for evaluation.
-
-Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC).
-Thanks for providing access to the TFRC ❤️
-
-Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team,
-it is possible to download both cased and uncased models from their S3 storage 🤗
@@ -1,79 +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! 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)
-
-## 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. 
-
-| 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 |
-
-## Pre-training details
-* 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 
-
-## Usage
-Polbert is released via [HuggingFace Transformers library](https://huggingface.co/transformers/).
-
-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 *
-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}
-```
-
-See the next section for an example usage of Polbert in downstream tasks. 
-
-## Evaluation
-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. 
-
-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. 
-
-| 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. 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
-* PolEmo 2.0 Sentiment Analysis Dataset for CoNLL: https://clarin-pl.eu/dspace/handle/11321/710
-
@@ -1,39 +0,0 @@
-
-# ClinicalBERT - Bio + Clinical BERT Model
-
-The [Publicly Available Clinical BERT Embeddings](https://arxiv.org/abs/1904.03323) paper contains four unique clinicalBERT models: initialized with BERT-Base (`cased_L-12_H-768_A-12`) or BioBERT (`BioBERT-Base v1.0 + PubMed 200K + PMC 270K`) & trained on either all MIMIC notes or only discharge summaries. 
-
-This model card describes the Bio+Clinical BERT model, which was initialized from [BioBERT](https://arxiv.org/abs/1901.08746) & trained on all MIMIC notes. 
-
-## Pretraining Data
-The `Bio_ClinicalBERT` model was trained on all notes from [MIMIC III](https://www.nature.com/articles/sdata201635), a database containing electronic health records from ICU patients at the Beth Israel Hospital in Boston, MA. For more details on MIMIC, see [here](https://mimic.physionet.org/). All notes from the `NOTEEVENTS` table were included (~880M words).
-
-## Model Pretraining 
-
-### Note Preprocessing
-Each note in MIMIC was first split into sections using a rules-based section splitter (e.g. discharge summary notes were split into "History of Present Illness", "Family History", "Brief Hospital Course", etc. sections). Then each section was split into sentences using SciSpacy (`en core sci md` tokenizer). 
-
-### Pretraining Procedures
-The model was trained using code from [Google's BERT repository](https://github.com/google-research/bert) on a GeForce GTX TITAN X 12 GB GPU. Model parameters were initialized with BioBERT (`BioBERT-Base v1.0 + PubMed 200K + PMC 270K`).
-
-### Pretraining Hyperparameters
-We used a batch size of 32, a maximum sequence length of 128, and a learning rate of 5 · 10−5 for pre-training our models. The models trained on all MIMIC notes  were trained for 150,000 steps. The dup factor for duplicating input data with different masks was set to 5. All other default parameters were used (specifically, masked language model probability = 0.15
-and max predictions per sequence = 20).
-
-## How to use the model
-
-Load the model via the transformers library:
-```
-from transformers import AutoTokenizer, AutoModel
-tokenizer = AutoTokenizer.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
-model = AutoModel.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
-```
-
-## More Information
-
-Refer to the original paper, [Publicly Available Clinical BERT Embeddings](https://arxiv.org/abs/1904.03323) (NAACL Clinical NLP Workshop 2019) for additional details and performance on NLI and NER tasks.
-
-## Questions?
-
-Post a Github issue on the [clinicalBERT repo](https://github.com/EmilyAlsentzer/clinicalBERT) or email emilya@mit.edu with any questions.
-
@@ -1,39 +0,0 @@
-
-# ClinicalBERT - Bio + Discharge Summary BERT Model
-
-The [Publicly Available Clinical BERT Embeddings](https://arxiv.org/abs/1904.03323) paper contains four unique clinicalBERT models: initialized with BERT-Base (`cased_L-12_H-768_A-12`) or BioBERT (`BioBERT-Base v1.0 + PubMed 200K + PMC 270K`) & trained on either all MIMIC notes or only discharge summaries. 
-
-This model card describes the Bio+Discharge Summary BERT model, which was initialized from [BioBERT](https://arxiv.org/abs/1901.08746) & trained on only discharge summaries from MIMIC. 
-
-## Pretraining Data
-The `Bio_Discharge_Summary_BERT` model was trained on all discharge summaries from [MIMIC III](https://www.nature.com/articles/sdata201635), a database containing electronic health records from ICU patients at the Beth Israel Hospital in Boston, MA. For more details on MIMIC, see [here](https://mimic.physionet.org/). All notes from the `NOTEEVENTS` table were included (~880M words).
-
-## Model Pretraining 
-
-### Note Preprocessing
-Each note in MIMIC was first split into sections using a rules-based section splitter (e.g. discharge summary notes were split into "History of Present Illness", "Family History", "Brief Hospital Course", etc. sections). Then each section was split into sentences using SciSpacy (`en core sci md` tokenizer). 
-
-### Pretraining Procedures
-The model was trained using code from [Google's BERT repository](https://github.com/google-research/bert) on a GeForce GTX TITAN X 12 GB GPU. Model parameters were initialized with BioBERT (`BioBERT-Base v1.0 + PubMed 200K + PMC 270K`).
-
-### Pretraining Hyperparameters
-We used a batch size of 32, a maximum sequence length of 128, and a learning rate of 5 · 10−5 for pre-training our models. The models trained on all MIMIC notes  were trained for 150,000 steps. The dup factor for duplicating input data with different masks was set to 5. All other default parameters were used (specifically, masked language model probability = 0.15
-and max predictions per sequence = 20).
-
-## How to use the model
-
-Load the model via the transformers library:
-```
-from transformers import AutoTokenizer, AutoModel
-tokenizer = AutoTokenizer.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
-model = AutoModel.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
-```
-
-## More Information
-
-Refer to the original paper, [Publicly Available Clinical BERT Embeddings](https://arxiv.org/abs/1904.03323) (NAACL Clinical NLP Workshop 2019) for additional details and performance on NLI and NER tasks.
-
-## Questions?
-
-Post a Github issue on the [clinicalBERT repo](https://github.com/EmilyAlsentzer/clinicalBERT) or email emilya@mit.edu with any questions.
-
@@ -1,53 +0,0 @@
---
-thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
-tags:
- conversational
---
-
-## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
-
-DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations. 
-The [human evaluation results](https://github.com/dreasysnail/Dialogpt_dev#human-evaluation) indicate that the response generated from DialoGPT is comparable to human response quality under a single-turn conversation Turing test.
-The model is trained on 147M multi-turn dialogue from Reddit discussion thread. 
-
-* Multi-turn generation examples from an interactive environment:
-
-|Role | Response |
-|---------|--------|
-|User | Does money buy happiness? |
-| Bot |  Depends how much money you spend on it .|
-|User | What is the best way to buy happiness ? |
-| Bot | You just have to be a millionaire by your early 20s, then you can be happy . |
-|User |This is so difficult ! |
-| Bot | You have no idea how hard it is to be a millionaire and happy . There is a reason the rich have a lot of money |
-
-Please find the information about preprocessing, training and full details of the DialoGPT in the [original DialoGPT repository](https://github.com/microsoft/DialoGPT)
-
-ArXiv paper: [https://arxiv.org/abs/1911.00536](https://arxiv.org/abs/1911.00536)
-
-### How to use
-
-Now we are ready to try out how the model works as a chatting partner!
-
-```python
-from transformers import AutoModelWithLMHead, AutoTokenizer
-import torch
-
-
-tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large")
-model = AutoModelWithLMHead.from_pretrained("microsoft/DialoGPT-large")
-
-# Let's chat for 5 lines
-for step in range(5):
-	# encode the new user input, add the eos_token and return a tensor in Pytorch
-	new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
-
-	# append the new user input tokens to the chat history
-	bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
-
-	# generated a response while limiting the total chat history to 1000 tokens, 
-	chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
-
-	# pretty print last ouput tokens from bot
-	print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
-```
@@ -1,53 +0,0 @@
---
-thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
-tags:
- conversational
---
-
-## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
-
-DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations. 
-The [human evaluation results](https://github.com/dreasysnail/Dialogpt_dev#human-evaluation) indicate that the response generated from DialoGPT is comparable to human response quality under a single-turn conversation Turing test.
-The model is trained on 147M multi-turn dialogue from Reddit discussion thread. 
-
-* Multi-turn generation examples from an interactive environment:
-
-|Role | Response |
-|---------|--------|
-|User | Does money buy happiness? |
-| Bot |  Depends how much money you spend on it .|
-|User | What is the best way to buy happiness ? |
-| Bot | You just have to be a millionaire by your early 20s, then you can be happy . |
-|User |This is so difficult ! |
-| Bot | You have no idea how hard it is to be a millionaire and happy . There is a reason the rich have a lot of money |
-
-Please find the information about preprocessing, training and full details of the DialoGPT in the [original DialoGPT repository](https://github.com/microsoft/DialoGPT)
-
-ArXiv paper: [https://arxiv.org/abs/1911.00536](https://arxiv.org/abs/1911.00536)
-
-### How to use
-
-Now we are ready to try out how the model works as a chatting partner!
-
-```python
-from transformers import AutoModelWithLMHead, AutoTokenizer
-import torch
-
-
-tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
-model = AutoModelWithLMHead.from_pretrained("microsoft/DialoGPT-medium")
-
-# Let's chat for 5 lines
-for step in range(5):
-	# encode the new user input, add the eos_token and return a tensor in Pytorch
-	new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
-
-	# append the new user input tokens to the chat history
-	bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
-
-	# generated a response while limiting the total chat history to 1000 tokens, 
-	chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
-
-	# pretty print last ouput tokens from bot
-	print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
-```
@@ -1,53 +0,0 @@
---
-thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
-tags:
- conversational
---
-
-## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)
-
-DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations. 
-The [human evaluation results](https://github.com/dreasysnail/Dialogpt_dev#human-evaluation) indicate that the response generated from DialoGPT is comparable to human response quality under a single-turn conversation Turing test.
-The model is trained on 147M multi-turn dialogue from Reddit discussion thread. 
-
-* Multi-turn generation examples from an interactive environment:
-
-|Role | Response |
-|---------|--------|
-|User | Does money buy happiness? |
-| Bot |  Depends how much money you spend on it .|
-|User | What is the best way to buy happiness ? |
-| Bot | You just have to be a millionaire by your early 20s, then you can be happy . |
-|User |This is so difficult ! |
-| Bot | You have no idea how hard it is to be a millionaire and happy . There is a reason the rich have a lot of money |
-
-Please find the information about preprocessing, training and full details of the DialoGPT in the [original DialoGPT repository](https://github.com/microsoft/DialoGPT)
-
-ArXiv paper: [https://arxiv.org/abs/1911.00536](https://arxiv.org/abs/1911.00536)
-
-### How to use
-
-Now we are ready to try out how the model works as a chatting partner!
-
-```python
-from transformers import AutoModelWithLMHead, AutoTokenizer
-import torch
-
-
-tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")
-model = AutoModelWithLMHead.from_pretrained("microsoft/DialoGPT-small")
-
-# Let's chat for 5 lines
-for step in range(5):
-	# encode the new user input, add the eos_token and return a tensor in Pytorch
-	new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
-
-	# append the new user input tokens to the chat history
-	bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
-
-	# generated a response while limiting the total chat history to 1000 tokens, 
-	chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
-
-	# pretty print last ouput tokens from bot
-	print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
-```
@@ -1,78 +0,0 @@
---
-language: multilingual
-thumbnail:
---
-
-# A fine-tuned model on GoldP task from Tydi QA dataset
-
-This model uses [bert-multi-cased-finetuned-xquadv1](https://huggingface.co/mrm8488/bert-multi-cased-finetuned-xquadv1) and fine-tuned on [Tydi QA](https://github.com/google-research-datasets/tydiqa) dataset for Gold Passage task [(GoldP)](https://github.com/google-research-datasets/tydiqa#the-tasks)
-
-## Details of the language model
-The base language model [(bert-multi-cased-finetuned-xquadv1)](https://huggingface.co/mrm8488/bert-multi-cased-finetuned-xquadv1) is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) for the **Q&A** downstream task
-
-
-## Details of the Tydi QA dataset
-
-TyDi QA contains 200k human-annotated question-answer pairs in 11 Typologically Diverse languages, written without seeing the answer and without the use of translation, and is designed for the **training and evaluation** of automatic question answering systems. This repository provides evaluation code and a baseline system for the dataset. https://ai.google.com/research/tydiqa
-
-
-## Details of the downstream task (Gold Passage or GoldP aka the secondary task)
-
-Given a passage that is guaranteed to contain the answer, predict the single contiguous span of characters that answers the question. the gold passage task differs from the [primary task](https://github.com/google-research-datasets/tydiqa/blob/master/README.md#the-tasks) in several ways:
-*   only the gold answer passage is provided rather than the entire Wikipedia article;
-*   unanswerable questions have been discarded, similar to MLQA and XQuAD;
-*   we evaluate with the SQuAD 1.1 metrics like XQuAD; and
-*   Thai and Japanese are removed since the lack of whitespace breaks some tools.
-
-
-## Model training
-
-The model was fine-tuned on a Tesla P100 GPU and 25GB of RAM.
-The script is the following:
-
-```python
-python run_squad.py \
-  --model_type bert \
-  --model_name_or_path mrm8488/bert-multi-cased-finetuned-xquadv1 \
-  --do_train \
-  --do_eval \
-  --train_file /content/dataset/train.json \
-  --predict_file /content/dataset/dev.json \
-  --per_gpu_train_batch_size 24 \
-  --per_gpu_eval_batch_size 24 \
-  --learning_rate 3e-5 \
-  --num_train_epochs 2.5 \
-  --max_seq_length 384 \
-  --doc_stride 128 \
-  --output_dir /content/model_output \
-  --overwrite_output_dir \
-  --save_steps 5000 \
-  --threads 40
-  ```
-
-## Global Results (dev set):
-
-| Metric    | # Value     |
-| --------- | ----------- |
-| **Exact** | **71.06** |
-| **F1**    | **82.16** |
-
-## Specific Results (per language):
-
-| Language    | # Samples     | # Exact | # F1 |
-| --------- | ----------- |--------| ------ |
-| Arabic    | 1314  | 73.29 | 84.72 |
-| Bengali   | 180   | 64.60 | 77.84 |
-| English   | 654   | 72.12 |   82.24   |
-| Finnish   | 1031  | 70.14 | 80.36 |
-| Indonesian| 773   | 77.25 | 86.36 |
-| Korean    | 414   | 68.92 | 70.95 |
-| Russian   | 1079    | 62.65 | 78.55 |
-| Swahili   | 596   | 80.11 | 86.18 |
-| Telegu    | 874   | 71.00 | 84.24 |
-
-
-
-> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
-
-> Made with <span style="color: #e25555;">&hearts;</span> in Spain
@@ -3,9 +3,9 @@ language: multilingual
 thumbnail:
 ---

-# BERT (base-multilingual-cased) fine-tuned for multilingual Q&A
+# BERT (base-multilingual-cased) fine-tuned on XQuAD

-This model was created by [Google](https://github.com/google-research/bert/blob/master/multilingual.md) and fine-tuned on [XQuAD](https://github.com/deepmind/xquad) like data for multilingual (`11 different languages`) **Q&A** downstream task.
+This model was created by [Google](https://github.com/google-research/bert/blob/master/multilingual.md) and fine-tuned on [XQuAD](https://github.com/deepmind/xquad) for multilingual (`11 different languages`) **Q&A** downstream task.

 ## Details of the language model('bert-base-multilingual-cased')

@@ -50,8 +50,7 @@ for the other languages.
 Citation:

 <details>
-
-```bibtex
+```
@article{Artetxe:etal:2019,
      author    = {Mikel Artetxe and Sebastian Ruder and Dani Yogatama},
      title     = {On the cross-lingual transferability of monolingual representations},
@@ -62,10 +61,9 @@ Citation:
      eprint    = {1910.11856}
 }
 ```
-
 </details>

-As **XQuAD** is just an evaluation dataset, I used `Data augmentation techniques` (scraping, neural machine translation, etc) to obtain more samples and splited the dataset in order to have a train and test set. The test set was created in a way that contains the same number of samples for each language. Finally, I got:
+I used `Data augmentation techniques` and splited the dataset in order to have a train and test set. The test set was created in a way that contains the same number of samples for each language. Finally, I got:

 | Dataset     | # samples |
 | ----------- | --------- |
@@ -77,6 +75,14 @@ As **XQuAD** is just an evaluation dataset, I used `Data augmentation techniques
 The model was trained on a Tesla P100 GPU and 25GB of RAM.
 The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/distillation/run_squad_w_distillation.py)

+## Results:
+
+| Metric    | # Value     |
+| --------- | ----------- |
+| **Exact** | **91.43** |
+| **F1**    | **94.14** |
+
+

 ## Model in action

@@ -1,129 +0,0 @@
---
-language: multilingual
-thumbnail:
---
-
-# BERT (base-multilingual-uncased) fine-tuned for multilingual Q&A
-
-This model was created by [Google](https://github.com/google-research/bert/blob/master/multilingual.md) and fine-tuned on [XQuAD](https://github.com/deepmind/xquad) like data for multilingual (`11 different languages`) **Q&A** downstream task.
-
-## Details of the language model('bert-base-multilingual-uncased')
-
-[Language model](https://github.com/google-research/bert/blob/master/multilingual.md)
-
-| Languages | Heads | Layers | Hidden | Params |
-| --------- | ----- | ------ | ------ | ------ |
-| 102       | 12    | 12     | 768    | 100 M  |
-
-## Details of the downstream task (multilingual Q&A) - Dataset
-
-Deepmind [XQuAD](https://github.com/deepmind/xquad)
-
-Languages covered:
-
- Arabic: `ar`
- German: `de`
- Greek: `el`
- English: `en`
- Spanish: `es`
- Hindi: `hi`
- Russian: `ru`
- Thai: `th`
- Turkish: `tr`
- Vietnamese: `vi`
- Chinese: `zh`
-
-As the dataset is based on SQuAD v1.1, there are no unanswerable questions in the data. We chose this
-setting so that models can focus on cross-lingual transfer.
-
-We show the average number of tokens per paragraph, question, and answer for each language in the
-table below. The statistics were obtained using [Jieba](https://github.com/fxsjy/jieba) for Chinese
-and the [Moses tokenizer](https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/tokenizer.perl)
-for the other languages.
-
-|           |  en   |  es   |  de   |  el   |  ru   |  tr   |  ar   |  vi   |  th   |  zh   |  hi   |
-| --------- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
-| Paragraph | 142.4 | 160.7 | 139.5 | 149.6 | 133.9 | 126.5 | 128.2 | 191.2 | 158.7 | 147.6 | 232.4 |
-| Question  | 11.5  | 13.4  | 11.0  | 11.7  | 10.0  |  9.8  | 10.7  | 14.8  | 11.5  | 10.5  | 18.7  |
-| Answer    |  3.1  |  3.6  |  3.0  |  3.3  |  3.1  |  3.1  |  3.1  |  4.5  |  4.1  |  3.5  |  5.6  |
-
-Citation:
-
-<details>
-
-```bibtex
-@article{Artetxe:etal:2019,
-      author    = {Mikel Artetxe and Sebastian Ruder and Dani Yogatama},
-      title     = {On the cross-lingual transferability of monolingual representations},
-      journal   = {CoRR},
-      volume    = {abs/1910.11856},
-      year      = {2019},
-      archivePrefix = {arXiv},
-      eprint    = {1910.11856}
-}
-```
-
-</details>
-
-As **XQuAD** is just an evaluation dataset, I used `Data augmentation techniques` (scraping, neural machine translation, etc) to obtain more samples and splited the dataset in order to have a train and test set. The test set was created in a way that contains the same number of samples for each language. Finally, I got:
-
-| Dataset     | # samples |
-| ----------- | --------- |
-| XQUAD train | 50 K      |
-| XQUAD test  | 8 K       |
-
-## Model training
-
-The model was trained on a Tesla P100 GPU and 25GB of RAM.
-The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/distillation/run_squad_w_distillation.py)
-
-
-## Model in action
-
-Fast usage with **pipelines**:
-
-```python
-from transformers import pipeline
-
-qa_pipeline = pipeline(
-    "question-answering",
-    model="mrm8488/bert-multi-uncased-finetuned-xquadv1",
-    tokenizer="mrm8488/bert-multi-uncased-finetuned-xquadv1"
-)
-
-
-# context: Coronavirus is seeding panic in the West because it expands so fast.
-
-# question: Where is seeding panic Coronavirus?
-qa_pipeline({
-    'context': "कोरोनावायरस पश्चिम में आतंक बो रहा है क्योंकि यह इतनी तेजी से फैलता है।",
-    'question': "कोरोनावायरस घबराहट कहां है?"
-    
-})
-# output: {'answer': 'पश्चिम', 'end': 18, 'score': 0.7037217439689059, 'start': 12}
-
-qa_pipeline({
-    'context': "Manuel Romero has been working hardly in the repository hugginface/transformers lately",
-    'question': "Who has been working hard for hugginface/transformers lately?"
-    
-})
-# output: {'answer': 'Manuel Romero', 'end': 13, 'score': 0.7254485993702389, 'start': 0}
-
-qa_pipeline({
-    'context': "Manuel Romero a travaillé à peine dans le référentiel hugginface / transformers ces derniers temps",
-    'question': "Pour quel référentiel a travaillé Manuel Romero récemment?"
-    
-})
-#output: {'answer': 'hugginface / transformers', 'end': 79, 'score': 0.6482061613915384, 'start': 54}
-```
-![model in action](https://media.giphy.com/media/MBlire8Wj7ng73VBQ5/giphy.gif)
-
-Try it on a Colab:
-
-<a href="https://colab.research.google.com/github/mrm8488/shared_colab_notebooks/blob/master/Try_mrm8488_xquad_finetuned_uncased_model.ipynb" target="_parent"><img src="https://camo.githubusercontent.com/52feade06f2fecbf006889a904d221e6a730c194/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg"></a>
-
-
-
-> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
-
-> Made with <span style="color: #e25555;">&hearts;</span> in Spain
@@ -1,123 +0,0 @@
---
-language: multilingual
-thumbnail:
---
-
-# [XLM](https://github.com/facebookresearch/XLM/) (multilingual version) fine-tuned for multilingual Q&A
-
-Released from `Facebook` together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau and fine-tuned on [XQuAD](https://github.com/deepmind/xquad) for multilingual (`11 different languages`) **Q&A** downstream task.
-
-## Details of the language model('xlm-mlm-100-1280')
-
-[Language model](https://github.com/facebookresearch/XLM/#ii-cross-lingual-language-model-pretraining-xlm)
-
-| Languages
-| --------- |
-| 100 |
-
-It includes the following languages:
-
-<details>
-en-es-fr-de-zh-ru-pt-it-ar-ja-id-tr-nl-pl-simple-fa-vi-sv-ko-he-ro-no-hi-uk-cs-fi-hu-th-da-ca-el-bg-sr-ms-bn-hr-sl-zh_yue-az-sk-eo-ta-sh-lt-et-ml-la-bs-sq-arz-af-ka-mr-eu-tl-ang-gl-nn-ur-kk-be-hy-te-lv-mk-zh_classical-als-is-wuu-my-sco-mn-ceb-ast-cy-kn-br-an-gu-bar-uz-lb-ne-si-war-jv-ga-zh_min_nan-oc-ku-sw-nds-ckb-ia-yi-fy-scn-gan-tt-am
-</details>
-
-## Details of the downstream task (multilingual Q&A) - Dataset
-
-Deepmind [XQuAD](https://github.com/deepmind/xquad)
-
-Languages covered:
-
- Arabic: `ar`
- German: `de`
- Greek: `el`
- English: `en`
- Spanish: `es`
- Hindi: `hi`
- Russian: `ru`
- Thai: `th`
- Turkish: `tr`
- Vietnamese: `vi`
- Chinese: `zh`
-
-As the dataset is based on SQuAD v1.1, there are no unanswerable questions in the data. We chose this
-setting so that models can focus on cross-lingual transfer.
-
-We show the average number of tokens per paragraph, question, and answer for each language in the
-table below. The statistics were obtained using [Jieba](https://github.com/fxsjy/jieba) for Chinese
-and the [Moses tokenizer](https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/tokenizer.perl)
-for the other languages.
-
-|           |  en   |  es   |  de   |  el   |  ru   |  tr   |  ar   |  vi   |  th   |  zh   |  hi   |
-| --------- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
-| Paragraph | 142.4 | 160.7 | 139.5 | 149.6 | 133.9 | 126.5 | 128.2 | 191.2 | 158.7 | 147.6 | 232.4 |
-| Question  | 11.5  | 13.4  | 11.0  | 11.7  | 10.0  |  9.8  | 10.7  | 14.8  | 11.5  | 10.5  | 18.7  |
-| Answer    |  3.1  |  3.6  |  3.0  |  3.3  |  3.1  |  3.1  |  3.1  |  4.5  |  4.1  |  3.5  |  5.6  |
-
-Citation:
-
-<details>
-
-```bibtex
-@article{Artetxe:etal:2019,
-      author    = {Mikel Artetxe and Sebastian Ruder and Dani Yogatama},
-      title     = {On the cross-lingual transferability of monolingual representations},
-      journal   = {CoRR},
-      volume    = {abs/1910.11856},
-      year      = {2019},
-      archivePrefix = {arXiv},
-      eprint    = {1910.11856}
-}
-```
-
-</details>
-
-As XQuAD is just an evaluation dataset, I used Data augmentation techniques (scraping, neural machine translation, etc) to obtain more samples and splited the dataset in order to have a train and test set. The test set was created in a way that contains the same number of samples for each language. Finally, I got:
-
-| Dataset     | # samples |
-| ----------- | --------- |
-| XQUAD train | 50 K      |
-| XQUAD test  | 8 K       |
-
-## Model training
-
-The model was trained on a Tesla P100 GPU and 25GB of RAM.
-The script for fine tuning can be found [here](https://github.com/huggingface/transformers/blob/master/examples/distillation/run_squad_w_distillation.py)
-
-
-## Model in action
-
-Fast usage with **pipelines**:
-
-```python
-from transformers import pipeline
-
-qa_pipeline = pipeline(
-    "question-answering",
-    model="mrm8488/bert-multi-uncased-finetuned-xquadv1",
-    tokenizer="bert-multi-uncased-finetuned-xquadv1"
-)
-
-# English
-qa_pipeline({
-    'context': "Manuel Romero has been working hardly in the repository hugginface/transformers lately",
-    'question': "Who has been working hard for hugginface/transformers lately?"
-})
-
-#Output: {'answer': 'Manuel', 'end': 6, 'score': 8.531880747878265e-05, 'start': 0}
-
-# Russian
-qa_pipeline({
-    'context': "Мануэль Ромеро в последнее время почти не работал в репозитории hugginface / transformers",
-    'question': "Кто в последнее время усердно работал над обнимашками / трансформерами?"
-    
-})
-
-#Output: {'answer': 'работал в репозитории hugginface /','end': 76, 'score': 0.00012340750456964894, 'start': 42}
-```
-Try it on a Colab:
-
-<a href="https://colab.research.google.com/github/mrm8488/shared_colab_notebooks/blob/master/Try_mrm8488_xquad_finetuned_uncased_model.ipynb" target="_parent"><img src="https://camo.githubusercontent.com/52feade06f2fecbf006889a904d221e6a730c194/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open In Colab" data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg"></a>
-
-> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
-
-> Made with <span style="color: #e25555;">&hearts;</span> in Spain
@@ -1,41 +0,0 @@
-This model is ALBERT base v2 trained on SQuAD v2 as:
-
-```
-python run_squad.py 
--model_type albert 
--model_name_or_path albert-base-v2 
--do_train --do_eval 
--do_lower_case 
--version_2_with_negative 
--train_file $SQUAD_DIR/train-v2.0.json 
--predict_file $SQUAD_DIR/dev-v2.0.json 
--per_gpu_train_batch_size 8 
--num_train_epochs 3 
--learning_rate 3e-5 
--max_seq_length 384 
--doc_stride 128 
--output_dir ./tmp/albert_base_fine/
-```
-
-Performance on a dev subset is close to the original paper:
-
-```
-Results: 
-{
-    'exact': 78.71010200723923, 
-    'f1': 81.89228117126069, 
-    'total': 6078, 
-    'HasAns_exact': 75.39518900343643, 
-    'HasAns_f1': 82.04167868004215, 
-    'HasAns_total': 2910, 
-    'NoAns_exact': 81.7550505050505, 
-    'NoAns_f1': 81.7550505050505, 
-    'NoAns_total': 3168, 
-    'best_exact': 78.72655478775913, 
-    'best_exact_thresh': 0.0, 
-    'best_f1': 81.90873395178066, 
-    'best_f1_thresh': 0.0
-}
-```
-
-We are hopeful this might save you time, energy, and compute. Cheers!
@@ -1,44 +0,0 @@
---
-language:
- chinese
---
-
-# albert_chinese_base
-
-This a albert_chinese_base model from [Google's github](https://github.com/google-research/ALBERT)  
-converted by huggingface's [script](https://github.com/huggingface/transformers/blob/master/src/transformers/convert_albert_original_tf_checkpoint_to_pytorch.py)
-
-## Attention (注意)
-
-Since sentencepiece is not used in albert_chinese_base model   
-you have to call BertTokenizer instead of AlbertTokenizer !!!    
-we can eval it using an example on MaskedLM   
-   
-由於 albert_chinese_base 模型沒有用 sentencepiece   
-用AlbertTokenizer會載不進詞表，因此需要改用BertTokenizer !!!   
-我們可以跑MaskedLM預測來驗證這個做法是否正確   
-   
-## Justify (驗證有效性)
-[colab trial](https://colab.research.google.com/drive/1Wjz48Uws6-VuSHv_-DcWLilv77-AaYgj)   
-```python
-from transformers import *
-import torch
-from torch.nn.functional import softmax
-
-pretrained = 'voidful/albert_chinese_base'
-tokenizer = BertTokenizer.from_pretrained(pretrained)
-model = AlbertForMaskedLM.from_pretrained(pretrained)
-
-inputtext = "今天[MASK]情很好"
-
-maskpos = tokenizer.encode(inputtext, add_special_tokens=True).index(103)
-
-input_ids = torch.tensor(tokenizer.encode(inputtext, add_special_tokens=True)).unsqueeze(0)  # Batch size 1
-outputs = model(input_ids, masked_lm_labels=input_ids)
-loss, prediction_scores = outputs[:2]
-logit_prob = softmax(prediction_scores[0, maskpos]).data.tolist()
-predicted_index = torch.argmax(prediction_scores[0, maskpos]).item()
-predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]
-print(predicted_token,logit_prob[predicted_index])
-```
-Result: `感 0.36333346366882324`   
@@ -1,44 +0,0 @@
---
-language:
- chinese
---
-
-# albert_chinese_large
-
-This a albert_chinese_large model from [Google's github](https://github.com/google-research/ALBERT)  
-converted by huggingface's [script](https://github.com/huggingface/transformers/blob/master/src/transformers/convert_albert_original_tf_checkpoint_to_pytorch.py)
-
-## Attention (注意)
-
-Since sentencepiece is not used in albert_chinese_large model   
-you have to call BertTokenizer instead of AlbertTokenizer !!!    
-we can eval it using an example on MaskedLM   
-   
-由於 albert_chinese_large 模型沒有用 sentencepiece   
-用AlbertTokenizer會載不進詞表，因此需要改用BertTokenizer !!!   
-我們可以跑MaskedLM預測來驗證這個做法是否正確   
-   
-## Justify (驗證有效性)
-[colab trial](https://colab.research.google.com/drive/1Wjz48Uws6-VuSHv_-DcWLilv77-AaYgj)   
-```python
-from transformers import *
-import torch
-from torch.nn.functional import softmax
-
-pretrained = 'voidful/albert_chinese_large'
-tokenizer = BertTokenizer.from_pretrained(pretrained)
-model = AlbertForMaskedLM.from_pretrained(pretrained)
-
-inputtext = "今天[MASK]情很好"
-
-maskpos = tokenizer.encode(inputtext, add_special_tokens=True).index(103)
-
-input_ids = torch.tensor(tokenizer.encode(inputtext, add_special_tokens=True)).unsqueeze(0)  # Batch size 1
-outputs = model(input_ids, masked_lm_labels=input_ids)
-loss, prediction_scores = outputs[:2]
-logit_prob = softmax(prediction_scores[0, maskpos]).data.tolist()
-predicted_index = torch.argmax(prediction_scores[0, maskpos]).item()
-predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]
-print(predicted_token,logit_prob[predicted_index])
-```
-Result: `心 0.9422469735145569`   
@@ -1,44 +0,0 @@
---
-language:
- chinese
---
-
-# albert_chinese_small
-
-This a albert_chinese_small model from [brightmart/albert_zh project](https://github.com/brightmart/albert_zh), albert_small_google_zh model    
-converted by huggingface's [script](https://github.com/huggingface/transformers/blob/master/src/transformers/convert_albert_original_tf_checkpoint_to_pytorch.py)
-
-## Attention (注意)
-
-Since sentencepiece is not used in albert_chinese_small model   
-you have to call BertTokenizer instead of AlbertTokenizer !!!    
-we can eval it using an example on MaskedLM   
-   
-由於 albert_chinese_small 模型沒有用 sentencepiece   
-用AlbertTokenizer會載不進詞表，因此需要改用BertTokenizer !!!   
-我們可以跑MaskedLM預測來驗證這個做法是否正確   
-   
-## Justify (驗證有效性)
-[colab trial](https://colab.research.google.com/drive/1Wjz48Uws6-VuSHv_-DcWLilv77-AaYgj)   
-```python
-from transformers import *
-import torch
-from torch.nn.functional import softmax
-
-pretrained = 'voidful/albert_chinese_small'
-tokenizer = BertTokenizer.from_pretrained(pretrained)
-model = AlbertForMaskedLM.from_pretrained(pretrained)
-
-inputtext = "今天[MASK]情很好"
-
-maskpos = tokenizer.encode(inputtext, add_special_tokens=True).index(103)
-
-input_ids = torch.tensor(tokenizer.encode(inputtext, add_special_tokens=True)).unsqueeze(0)  # Batch size 1
-outputs = model(input_ids, masked_lm_labels=input_ids)
-loss, prediction_scores = outputs[:2]
-logit_prob = softmax(prediction_scores[0, maskpos]).data.tolist()
-predicted_index = torch.argmax(prediction_scores[0, maskpos]).item()
-predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]
-print(predicted_token,logit_prob[predicted_index])
-```
-Result: `感 0.6390823125839233`   
@@ -1,44 +0,0 @@
---
-language:
- chinese
---
-
-# albert_chinese_tiny
-
-This a albert_chinese_tiny model from [brightmart/albert_zh project](https://github.com/brightmart/albert_zh), albert_tiny_google_zh model    
-converted by huggingface's [script](https://github.com/huggingface/transformers/blob/master/src/transformers/convert_albert_original_tf_checkpoint_to_pytorch.py)
-
-## Attention (注意)
-
-Since sentencepiece is not used in albert_chinese_tiny model   
-you have to call BertTokenizer instead of AlbertTokenizer !!!    
-we can eval it using an example on MaskedLM   
-   
-由於 albert_chinese_tiny 模型沒有用 sentencepiece   
-用AlbertTokenizer會載不進詞表，因此需要改用BertTokenizer !!!   
-我們可以跑MaskedLM預測來驗證這個做法是否正確   
-   
-## Justify (驗證有效性)
-[colab trial](https://colab.research.google.com/drive/1Wjz48Uws6-VuSHv_-DcWLilv77-AaYgj)   
-```python
-from transformers import *
-import torch
-from torch.nn.functional import softmax
-
-pretrained = 'voidful/albert_chinese_tiny'
-tokenizer = BertTokenizer.from_pretrained(pretrained)
-model = AlbertForMaskedLM.from_pretrained(pretrained)
-
-inputtext = "今天[MASK]情很好"
-
-maskpos = tokenizer.encode(inputtext, add_special_tokens=True).index(103)
-
-input_ids = torch.tensor(tokenizer.encode(inputtext, add_special_tokens=True)).unsqueeze(0)  # Batch size 1
-outputs = model(input_ids, masked_lm_labels=input_ids)
-loss, prediction_scores = outputs[:2]
-logit_prob = softmax(prediction_scores[0, maskpos]).data.tolist()
-predicted_index = torch.argmax(prediction_scores[0, maskpos]).item()
-predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]
-print(predicted_token,logit_prob[predicted_index])
-```
-Result: `感 0.40312355756759644`   
@@ -1,44 +0,0 @@
---
-language:
- chinese
---
-
-# albert_chinese_xlarge
-
-This a albert_chinese_xlarge model from [Google's github](https://github.com/google-research/ALBERT)  
-converted by huggingface's [script](https://github.com/huggingface/transformers/blob/master/src/transformers/convert_albert_original_tf_checkpoint_to_pytorch.py)
-
-## Attention (注意)
-
-Since sentencepiece is not used in albert_chinese_xlarge model   
-you have to call BertTokenizer instead of AlbertTokenizer !!!    
-we can eval it using an example on MaskedLM   
-   
-由於 albert_chinese_xlarge 模型沒有用 sentencepiece   
-用AlbertTokenizer會載不進詞表，因此需要改用BertTokenizer !!!   
-我們可以跑MaskedLM預測來驗證這個做法是否正確   
-   
-## Justify (驗證有效性)
-[colab trial](https://colab.research.google.com/drive/1Wjz48Uws6-VuSHv_-DcWLilv77-AaYgj)   
-```python
-from transformers import *
-import torch
-from torch.nn.functional import softmax
-
-pretrained = 'voidful/albert_chinese_xlarge'
-tokenizer = BertTokenizer.from_pretrained(pretrained)
-model = AlbertForMaskedLM.from_pretrained(pretrained)
-
-inputtext = "今天[MASK]情很好"
-
-maskpos = tokenizer.encode(inputtext, add_special_tokens=True).index(103)
-
-input_ids = torch.tensor(tokenizer.encode(inputtext, add_special_tokens=True)).unsqueeze(0)  # Batch size 1
-outputs = model(input_ids, masked_lm_labels=input_ids)
-loss, prediction_scores = outputs[:2]
-logit_prob = softmax(prediction_scores[0, maskpos]).data.tolist()
-predicted_index = torch.argmax(prediction_scores[0, maskpos]).item()
-predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]
-print(predicted_token,logit_prob[predicted_index])
-```
-Result: `心 0.9942440390586853`   
@@ -1,44 +0,0 @@
---
-language:
- chinese
---
-
-# albert_chinese_xxlarge
-
-This a albert_chinese_xxlarge model from [Google's github](https://github.com/google-research/ALBERT)  
-converted by huggingface's [script](https://github.com/huggingface/transformers/blob/master/src/transformers/convert_albert_original_tf_checkpoint_to_pytorch.py)
-
-## Attention (注意)
-
-Since sentencepiece is not used in albert_chinese_xxlarge model   
-you have to call BertTokenizer instead of AlbertTokenizer !!!    
-we can eval it using an example on MaskedLM   
-   
-由於 albert_chinese_xxlarge 模型沒有用 sentencepiece   
-用AlbertTokenizer會載不進詞表，因此需要改用BertTokenizer !!!   
-我們可以跑MaskedLM預測來驗證這個做法是否正確   
-   
-## Justify (驗證有效性)
-[colab trial](https://colab.research.google.com/drive/1Wjz48Uws6-VuSHv_-DcWLilv77-AaYgj)   
-```python
-from transformers import *
-import torch
-from torch.nn.functional import softmax
-
-pretrained = 'voidful/albert_chinese_xxlarge'
-tokenizer = BertTokenizer.from_pretrained(pretrained)
-model = AlbertForMaskedLM.from_pretrained(pretrained)
-
-inputtext = "今天[MASK]情很好"
-
-maskpos = tokenizer.encode(inputtext, add_special_tokens=True).index(103)
-
-input_ids = torch.tensor(tokenizer.encode(inputtext, add_special_tokens=True)).unsqueeze(0)  # Batch size 1
-outputs = model(input_ids, masked_lm_labels=input_ids)
-loss, prediction_scores = outputs[:2]
-logit_prob = softmax(prediction_scores[0, maskpos]).data.tolist()
-predicted_index = torch.argmax(prediction_scores[0, maskpos]).item()
-predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]
-print(predicted_token,logit_prob[predicted_index])
-```
-Result: `心 0.995713472366333`   
@@ -238,7 +238,7 @@
      "Tokens: ['This', 'is', 'an', 'input', 'example']\n",
      "Tokens id: [1188, 1110, 1126, 7758, 1859]\n",
      "Tokens PyTorch: tensor([[ 101, 1188, 1110, 1126, 7758, 1859,  102]])\n",
-      "Token wise output: torch.Size([1, 7, 768]), Pooled output: torch.Size([1, 768])\n"
+      "Tokenw   ise output: torch.Size([1, 7, 768]), Pooled output: torch.Size([1, 768])\n"
     ]
    }
   ],
@@ -261,7 +261,7 @@
    "\n",
    "# Now we're ready to go through BERT with out input\n",
    "outputs, pooled = model(tokens_pt)\n",
-    "print(\"Token wise output: {}, Pooled output: {}\".format(outputs.shape, pooled.shape))"
+    "print(\"Tokenw   ise output: {}, Pooled output: {}\".format(outputs.shape, pooled.shape))"
   ]
  },
  {
@@ -26,16 +26,13 @@ class UserCommands(BaseTransformersCLICommand):
        s3_parser = parser.add_parser("s3", help="{ls, rm} Commands to interact with the files you upload on S3.")
        s3_subparsers = s3_parser.add_subparsers(help="s3 related commands")
        ls_parser = s3_subparsers.add_parser("ls")
-        ls_parser.add_argument("--organization", type=str, help="Optional: organization namespace.")
        ls_parser.set_defaults(func=lambda args: ListObjsCommand(args))
        rm_parser = s3_subparsers.add_parser("rm")
        rm_parser.add_argument("filename", type=str, help="individual object filename to delete from S3.")
-        rm_parser.add_argument("--organization", type=str, help="Optional: organization namespace.")
        rm_parser.set_defaults(func=lambda args: DeleteObjCommand(args))
        # upload
        upload_parser = parser.add_parser("upload")
        upload_parser.add_argument("path", type=str, help="Local path of the folder or individual file to upload.")
-        upload_parser.add_argument("--organization", type=str, help="Optional: organization namespace.")
        upload_parser.add_argument(
            "--filename", type=str, default=None, help="Optional: override individual object filename on S3."
        )
@@ -94,10 +91,8 @@ class WhoamiCommand(BaseUserCommand):
            print("Not logged in")
            exit()
        try:
-            user, orgs = self._api.whoami(token)
+            user = self._api.whoami(token)
            print(user)
-            if orgs:
-                print(ANSI.bold("orgs: "), ",".join(orgs))
        except HTTPError as e:
            print(e)

@@ -135,7 +130,7 @@ class ListObjsCommand(BaseUserCommand):
            print("Not logged in")
            exit(1)
        try:
-            objs = self._api.list_objs(token, organization=self.args.organization)
+            objs = self._api.list_objs(token)
        except HTTPError as e:
            print(e)
            exit(1)
@@ -153,7 +148,7 @@ class DeleteObjCommand(BaseUserCommand):
            print("Not logged in")
            exit(1)
        try:
-            self._api.delete_obj(token, filename=self.args.filename, organization=self.args.organization)
+            self._api.delete_obj(token, filename=self.args.filename)
        except HTTPError as e:
            print(e)
            exit(1)
@@ -200,15 +195,8 @@ class UploadCommand(BaseUserCommand):
            )
            exit(1)

-        user, _ = self._api.whoami(token)
-        namespace = self.args.organization if self.args.organization is not None else user
-
        for filepath, filename in files:
-            print(
-                "About to upload file {} to S3 under filename {} and namespace {}".format(
-                    ANSI.bold(filepath), ANSI.bold(filename), ANSI.bold(namespace)
-                )
-            )
+            print("About to upload file {} to S3 under filename {}".format(ANSI.bold(filepath), ANSI.bold(filename)))

        choice = input("Proceed? [Y/n] ").lower()
        if not (choice == "" or choice == "y" or choice == "yes"):
@@ -216,8 +204,6 @@ class UploadCommand(BaseUserCommand):
            exit()
        print(ANSI.bold("Uploading... This might take a while if files are large"))
        for filepath, filename in files:
-            access_url = self._api.presign_and_upload(
-                token=token, filename=filename, filepath=filepath, organization=self.args.organization
-            )
+            access_url = self._api.presign_and_upload(token=token, filename=filename, filepath=filepath)
            print("Your file now lives at:")
            print(access_url)
@@ -40,9 +40,8 @@ class BartConfig(PretrainedConfig):
        self,
        activation_dropout=0.0,
        vocab_size=50265,
-        bos_token_id=0,
        pad_token_id=1,
-        eos_token_ids=[2],
+        eos_token_id=2,
        d_model=1024,
        encoder_ffn_dim=4096,
        encoder_layers=12,
@@ -59,7 +58,7 @@ class BartConfig(PretrainedConfig):
        classifier_dropout=0.0,
        output_past=False,
        num_labels=3,
-        is_encoder_decoder=True,
+        bos_token_id=0,
        **common_kwargs
    ):
        r"""
@@ -73,16 +72,14 @@ class BartConfig(PretrainedConfig):
            output_past=output_past,
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
-            eos_token_ids=eos_token_ids,
-            is_encoder_decoder=is_encoder_decoder,
            **common_kwargs,
        )
        self.vocab_size = vocab_size
        self.d_model = d_model  # encoder_embed_dim and decoder_embed_dim
+        self.eos_token_id = eos_token_id
        self.encoder_ffn_dim = encoder_ffn_dim
        self.encoder_layers = self.num_hidden_layers = encoder_layers
        self.encoder_attention_heads = encoder_attention_heads
-        self.eos_token_id = self.eos_token_ids[0]
        self.encoder_layerdrop = encoder_layerdrop
        self.decoder_layerdrop = decoder_layerdrop
        self.decoder_ffn_dim = decoder_ffn_dim
@@ -75,12 +75,9 @@ class T5Config(PretrainedConfig):
        dropout_rate=0.1,
        layer_norm_epsilon=1e-6,
        initializer_factor=1.0,
-        is_encoder_decoder=True,
        **kwargs
    ):
-        super().__init__(
-            is_encoder_decoder=is_encoder_decoder, **kwargs,
-        )
+        super().__init__(**kwargs)
        self.vocab_size = vocab_size
        self.n_positions = n_positions
        self.d_model = d_model
@@ -65,14 +65,11 @@ class PretrainedConfig(object):
        self.pruned_heads = kwargs.pop("pruned_heads", {})

        # Is decoder is used in encoder-decoder models to differentiate encoder from decoder
-        self.is_encoder_decoder = kwargs.pop("is_encoder_decoder", False)
        self.is_decoder = kwargs.pop("is_decoder", False)

        # Parameters for sequence generation
        self.max_length = kwargs.pop("max_length", 20)
-        self.min_length = kwargs.pop("min_length", 0)
        self.do_sample = kwargs.pop("do_sample", False)
-        self.early_stopping = kwargs.pop("early_stopping", False)
        self.num_beams = kwargs.pop("num_beams", 1)
        self.temperature = kwargs.pop("temperature", 1.0)
        self.top_k = kwargs.pop("top_k", 50)
@@ -82,7 +79,6 @@ class PretrainedConfig(object):
        self.pad_token_id = kwargs.pop("pad_token_id", None)
        self.eos_token_ids = kwargs.pop("eos_token_ids", None)
        self.length_penalty = kwargs.pop("length_penalty", 1.0)
-        self.no_repeat_ngram_size = kwargs.pop("no_repeat_ngram_size", 0)
        self.num_return_sequences = kwargs.pop("num_return_sequences", 1)

        # Fine-tuning task arguments
@@ -1,31 +0,0 @@
-import argparse
-import os
-
-import torch
-
-from transformers.file_utils import WEIGHTS_NAME
-
-
-DIALOGPT_MODELS = ["small", "medium", "large"]
-
-OLD_KEY = "lm_head.decoder.weight"
-NEW_KEY = "lm_head.weight"
-
-
-def convert_dialogpt_checkpoint(checkpoint_path: str, pytorch_dump_folder_path: str):
-    d = torch.load(checkpoint_path)
-    d[NEW_KEY] = d.pop(OLD_KEY)
-    os.makedirs(pytorch_dump_folder_path, exist_ok=True)
-    torch.save(d, os.path.join(pytorch_dump_folder_path, WEIGHTS_NAME))
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--dialogpt_path", default=".", type=str)
-    args = parser.parse_args()
-    for MODEL in DIALOGPT_MODELS:
-        checkpoint_path = os.path.join(args.dialogpt_path, f"{MODEL}_ft.pkl")
-        pytorch_dump_folder_path = f"./DialoGPT-{MODEL}"
-        convert_dialogpt_checkpoint(
-            checkpoint_path, pytorch_dump_folder_path,
-        )
@@ -17,7 +17,7 @@
 import io
 import os
 from os.path import expanduser
-from typing import Dict, List, Optional, Tuple
+from typing import List

 import requests
 from tqdm import tqdm
@@ -27,10 +27,6 @@ ENDPOINT = "https://huggingface.co"


 class S3Obj:
-    """
-    Data structure that represents a file belonging to the current user.
-    """
-
    def __init__(self, filename: str, LastModified: str, ETag: str, Size: int, **kwargs):
        self.filename = filename
        self.LastModified = LastModified
@@ -45,50 +41,6 @@ class PresignedUrl:
        self.type = type  # mime-type to send to S3.


-class S3Object:
-    """
-    Data structure that represents a public file accessible on our S3.
-    """
-
-    def __init__(
-        self,
-        key: str,  # S3 object key
-        etag: str,
-        lastModified: str,
-        size: int,
-        rfilename: str,  # filename relative to config.json
-        **kwargs
-    ):
-        self.key = key
-        self.etag = etag
-        self.lastModified = lastModified
-        self.size = size
-        self.rfilename = rfilename
-
-
-class ModelInfo:
-    """
-    Info about a public model accessible from our S3.
-    """
-
-    def __init__(
-        self,
-        modelId: str,  # id of model
-        key: str,  # S3 object key of config.json
-        author: Optional[str] = None,
-        downloads: Optional[int] = None,
-        tags: List[str] = [],
-        siblings: List[Dict] = [],  # list of files that constitute the model
-        **kwargs
-    ):
-        self.modelId = modelId
-        self.key = key
-        self.author = author
-        self.downloads = downloads
-        self.tags = tags
-        self.siblings = [S3Object(**x) for x in siblings]
-
-
 class HfApi:
    def __init__(self, endpoint=None):
        self.endpoint = endpoint if endpoint is not None else ENDPOINT
@@ -109,7 +61,7 @@ class HfApi:
        d = r.json()
        return d["token"]

-    def whoami(self, token: str) -> Tuple[str, List[str]]:
+    def whoami(self, token: str) -> str:
        """
        Call HF API to know "whoami"
        """
@@ -117,7 +69,7 @@ class HfApi:
        r = requests.get(path, headers={"authorization": "Bearer {}".format(token)})
        r.raise_for_status()
        d = r.json()
-        return d["user"], d["orgs"]
+        return d["user"]

    def logout(self, token: str) -> None:
        """
@@ -127,28 +79,24 @@ class HfApi:
        r = requests.post(path, headers={"authorization": "Bearer {}".format(token)})
        r.raise_for_status()

-    def presign(self, token: str, filename: str, organization: Optional[str] = None) -> PresignedUrl:
+    def presign(self, token: str, filename: str) -> PresignedUrl:
        """
        Call HF API to get a presigned url to upload `filename` to S3.
        """
        path = "{}/api/presign".format(self.endpoint)
-        r = requests.post(
-            path,
-            headers={"authorization": "Bearer {}".format(token)},
-            json={"filename": filename, "organization": organization},
-        )
+        r = requests.post(path, headers={"authorization": "Bearer {}".format(token)}, json={"filename": filename})
        r.raise_for_status()
        d = r.json()
        return PresignedUrl(**d)

-    def presign_and_upload(self, token: str, filename: str, filepath: str, organization: Optional[str] = None) -> str:
+    def presign_and_upload(self, token: str, filename: str, filepath: str) -> str:
        """
        Get a presigned url, then upload file to S3.

        Outputs:
            url: Read-only url for the stored file on S3.
        """
-        urls = self.presign(token, filename=filename, organization=organization)
+        urls = self.presign(token, filename=filename)
        # streaming upload:
        # https://2.python-requests.org/en/master/user/advanced/#streaming-uploads
        #
@@ -163,39 +111,24 @@ class HfApi:
            pf.close()
        return urls.access

-    def list_objs(self, token: str, organization: Optional[str] = None) -> List[S3Obj]:
+    def list_objs(self, token: str) -> List[S3Obj]:
        """
-        Call HF API to list all stored files for user (or one of their organizations).
+        Call HF API to list all stored files for user.
        """
        path = "{}/api/listObjs".format(self.endpoint)
-        params = {"organization": organization} if organization is not None else None
-        r = requests.get(path, params=params, headers={"authorization": "Bearer {}".format(token)})
+        r = requests.get(path, headers={"authorization": "Bearer {}".format(token)})
        r.raise_for_status()
        d = r.json()
        return [S3Obj(**x) for x in d]

-    def delete_obj(self, token: str, filename: str, organization: Optional[str] = None):
+    def delete_obj(self, token: str, filename: str):
        """
        Call HF API to delete a file stored by user
        """
        path = "{}/api/deleteObj".format(self.endpoint)
-        r = requests.delete(
-            path,
-            headers={"authorization": "Bearer {}".format(token)},
-            json={"filename": filename, "organization": organization},
-        )
+        r = requests.delete(path, headers={"authorization": "Bearer {}".format(token)}, json={"filename": filename})
        r.raise_for_status()

-    def model_list(self) -> List[ModelInfo]:
-        """
-        Get the public list of all the models on huggingface, including the community models
-        """
-        path = "{}/api/models".format(self.endpoint)
-        r = requests.get(path)
-        r.raise_for_status()
-        d = r.json()
-        return [ModelInfo(**x) for x in d]
-

 class TqdmProgressFileReader:
    """
@@ -78,7 +78,7 @@ from .modeling_distilbert import (
 )
 from .modeling_flaubert import (
    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
-    FlaubertForQuestionAnsweringSimple,
+    FlaubertForQuestionAnswering,
    FlaubertForSequenceClassification,
    FlaubertModel,
    FlaubertWithLMHeadModel,
@@ -97,7 +97,7 @@ from .modeling_t5 import T5_PRETRAINED_MODEL_ARCHIVE_MAP, T5Model, T5WithLMHeadM
 from .modeling_transfo_xl import TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP, TransfoXLLMHeadModel, TransfoXLModel
 from .modeling_xlm import (
    XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
-    XLMForQuestionAnsweringSimple,
+    XLMForQuestionAnswering,
    XLMForSequenceClassification,
    XLMModel,
    XLMWithLMHeadModel,
@@ -111,7 +111,7 @@ from .modeling_xlm_roberta import (
 )
 from .modeling_xlnet import (
    XLNET_PRETRAINED_MODEL_ARCHIVE_MAP,
-    XLNetForQuestionAnsweringSimple,
+    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetForTokenClassification,
    XLNetLMHeadModel,
@@ -225,9 +225,9 @@ MODEL_FOR_QUESTION_ANSWERING_MAPPING = OrderedDict(
        (AlbertConfig, AlbertForQuestionAnswering),
        (RobertaConfig, RobertaForQuestionAnswering),
        (BertConfig, BertForQuestionAnswering),
-        (XLNetConfig, XLNetForQuestionAnsweringSimple),
-        (FlaubertConfig, FlaubertForQuestionAnsweringSimple),
-        (XLMConfig, XLMForQuestionAnsweringSimple),
+        (XLNetConfig, XLNetForQuestionAnswering),
+        (FlaubertConfig, FlaubertForQuestionAnswering),
+        (XLMConfig, XLMForQuestionAnswering),
    ]
 )

@@ -65,7 +65,7 @@ BART_INPUTS_DOCSTRING = r"""
            If you want to change padding behavior, you should read :func:`~transformers.modeling_bart._prepare_decoder_inputs` and modify.
            See diagram 1 in the paper for more info on the default strategy
 """
-LARGE_NEGATIVE = -1e8
+LARGE_NEGATIVE = -1e4


 def _prepare_bart_decoder_inputs(
@@ -144,18 +144,18 @@ def _check_shapes(shape_1, shape2):
        raise AssertionError("shape mismatch: {} != {}".format(shape_1, shape2))


-def _combine_masks(key_padding_mask, causal_lm_mask, targ_size):
+def _combine_masks(key_padding_mask, attn_mask, targ_size):
    # targ_size = (bsz, tgt_len, src_len)
    a = torch.zeros(targ_size)
    b = torch.zeros(targ_size)
    if key_padding_mask is not None:  # (bsz, tgt_len) -> targ_size
        _check_shapes(key_padding_mask.shape, targ_size[:2])
        reshaped = key_padding_mask.unsqueeze(2).expand(*targ_size)
-        a[reshaped] = LARGE_NEGATIVE
+        a[reshaped] = 1e-8

-    if causal_lm_mask is not None:  # (tgt_len, src_len) -> targ_size
-        _check_shapes(causal_lm_mask.shape, targ_size[-2:])
-        b = causal_lm_mask.unsqueeze(0).expand(*targ_size)
+    if attn_mask is not None:  # (tgt_len, src_len) -> targ_size
+        _check_shapes(attn_mask.shape, targ_size[-2:])
+        b = attn_mask.unsqueeze(0).expand(*targ_size)
    return (a + b).unsqueeze(1).clamp(LARGE_NEGATIVE,)


@@ -271,12 +271,6 @@ class BartEncoder(nn.Module):
                - **all_attentions** (List[Tensor]): Attention weights for each layer.
                During training might not be of length n_layers because of layer dropout.
        """
-        # check attention mask and invert
-        if attention_mask is not None:
-            assert attention_mask.dim() == 2
-
-            attention_mask = (1.0 - attention_mask.long()) * -10000.0
-            assert attention_mask.max() <= 0
        inputs_embeds = self.embed_tokens(input_ids)
        embed_pos = self.embed_positions(input_ids)
        x = inputs_embeds + embed_pos
@@ -454,13 +448,6 @@ class BartDecoder(nn.Module):
                - hidden states
                - attentions
        """
-        # check attention mask and invert
-        if encoder_padding_mask is not None:
-            assert encoder_padding_mask.dim() == 2
-
-            encoder_padding_mask = (1.0 - encoder_padding_mask.long()) * -10000.0
-            assert encoder_padding_mask.max() <= 0
-
        # embed positions
        positions = self.embed_positions(input_ids, generation_mode=self.generation_mode)

@@ -821,6 +808,11 @@ class BartModel(PretrainedBartModel):
        decoder_attention_mask=None,
        decoder_cached_states=None,
    ):
+        if attention_mask is not None:
+            assert attention_mask.dim() == 2
+
+            attention_mask = (1.0 - attention_mask.long()) * -10000.0
+            assert attention_mask.max() <= 0

        # make masks if user doesn't supply
        if not self.decoder.generation_mode:
@@ -913,7 +905,7 @@ class BartForConditionalGeneration(PretrainedBartModel):

            # Mask filling only works for bart-large
            from transformers import BartTokenizer, BartForConditionalGeneration
-            tokenizer = BartTokenizer.from_pretrained('bart-large')
+            tokenizer = AutoTokenizer.from_pretrained('bart-large')
            TXT = "My friends are <mask> but they eat too many carbs."
            model = BartForConditionalGeneration.from_pretrained('bart-large')
            input_ids = tokenizer.batch_encode_plus([TXT], return_tensors='pt')['input_ids']
@@ -1031,7 +1023,8 @@ class BartForConditionalGeneration(PretrainedBartModel):
        Examples::
            from transformers import BartTokenizer, BartForConditionalGeneration, BartConfig
            # see ``examples/summarization/bart/evaluate_cnn.py`` for a longer example
-            model = BartForConditionalGeneration.from_pretrained('bart-large-cnn')
+            config = BartConfig(vocab_size=50264, output_past=True) # no mask_token_id
+            model = BartForConditionalGeneration.from_pretrained('bart-large-cnn', config=config)
            tokenizer = BartTokenizer.from_pretrained('bart-large-cnn')
            ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs."
            inputs = tokenizer.batch_encode_plus([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors='pt')
@@ -23,7 +23,7 @@ import tensorflow as tf
 from .configuration_albert import AlbertConfig
 from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
 from .modeling_tf_bert import ACT2FN, TFBertSelfAttention
-from .modeling_tf_utils import TFPreTrainedModel, get_initializer, keras_serializable, shape_list
+from .modeling_tf_utils import TFPreTrainedModel, get_initializer, shape_list


 logger = logging.getLogger(__name__)
@@ -478,10 +478,7 @@ class TFAlbertMLMHead(tf.keras.layers.Layer):
        return hidden_states


-@keras_serializable
 class TFAlbertMainLayer(tf.keras.layers.Layer):
-    config_class = AlbertConfig
-
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.num_hidden_layers = config.num_hidden_layers
@@ -23,7 +23,7 @@ import tensorflow as tf

 from .configuration_bert import BertConfig
 from .file_utils import MULTIPLE_CHOICE_DUMMY_INPUTS, add_start_docstrings, add_start_docstrings_to_callable
-from .modeling_tf_utils import TFPreTrainedModel, get_initializer, keras_serializable, shape_list
+from .modeling_tf_utils import TFPreTrainedModel, get_initializer, shape_list


 logger = logging.getLogger(__name__)
@@ -471,10 +471,7 @@ class TFBertNSPHead(tf.keras.layers.Layer):
        return seq_relationship_score


-@keras_serializable
 class TFBertMainLayer(tf.keras.layers.Layer):
-    config_class = BertConfig
-
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.num_hidden_layers = config.num_hidden_layers
@@ -23,7 +23,7 @@ import tensorflow as tf

 from .configuration_ctrl import CTRLConfig
 from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
-from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, keras_serializable, shape_list
+from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, shape_list


 logger = logging.getLogger(__name__)
@@ -164,10 +164,7 @@ class TFEncoderLayer(tf.keras.layers.Layer):
        return outputs


-@keras_serializable
 class TFCTRLMainLayer(tf.keras.layers.Layer):
-    config_class = CTRLConfig
-
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.output_hidden_states = config.output_hidden_states
@@ -29,7 +29,6 @@ from .modeling_tf_utils import (
    TFSequenceSummary,
    TFSharedEmbeddings,
    get_initializer,
-    keras_serializable,
    shape_list,
 )

@@ -197,10 +196,7 @@ class TFBlock(tf.keras.layers.Layer):
        return outputs  # x, present, (attentions)


-@keras_serializable
 class TFGPT2MainLayer(tf.keras.layers.Layer):
-    config_class = GPT2Config
-
    def __init__(self, config, *inputs, **kwargs):
        super().__init__(*inputs, **kwargs)
        self.output_hidden_states = config.output_hidden_states
@@ -24,7 +24,7 @@ import tensorflow as tf
 from .configuration_transfo_xl import TransfoXLConfig
 from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
 from .modeling_tf_transfo_xl_utilities import TFAdaptiveSoftmaxMask
-from .modeling_tf_utils import TFPreTrainedModel, get_initializer, keras_serializable, shape_list
+from .modeling_tf_utils import TFPreTrainedModel, get_initializer, shape_list


 logger = logging.getLogger(__name__)
@@ -378,10 +378,7 @@ class TFAdaptiveEmbedding(tf.keras.layers.Layer):
        return embed


-@keras_serializable
 class TFTransfoXLMainLayer(tf.keras.layers.Layer):
-    config_class = TransfoXLConfig
-
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.output_attentions = config.output_attentions
@@ -14,7 +14,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """TF general model utils."""
-import functools
+
+
 import logging
 import os

@@ -46,64 +47,6 @@ class TFModelUtilsMixin:
            return self.count_params()


-def keras_serializable(cls):
-    """
-    Decorate a Keras Layer class to support Keras serialization.
-
-    This is done by:
-    1. adding a `transformers_config` dict to the Keras config dictionary in `get_config` (called by Keras at
-       serialization time
-    2. wrapping `__init__` to accept that `transformers_config` dict (passed by Keras at deserialization time) and
-       convert it to a config object for the actual layer initializer
-    3. registering the class as a custom object in Keras (if the Tensorflow version supports this), so that it does
-       not need to be supplied in `custom_objects` in the call to `tf.keras.models.load_model`
-
-    :param cls: a tf.keras.layers.Layers subclass that accepts a `config` argument to its initializer (typically a
-                `TF*MainLayer` class in this project)
-    :return: the same class object, with modifications for Keras deserialization.
-    """
-    initializer = cls.__init__
-
-    config_class = getattr(cls, "config_class", None)
-    if config_class is None:
-        raise AttributeError("Must set `config_class` to use @keras_serializable")
-
-    @functools.wraps(initializer)
-    def wrapped_init(self, *args, **kwargs):
-        transformers_config = kwargs.pop("transformers_config", None)
-        config = args[0] if args and isinstance(args[0], PretrainedConfig) else kwargs.get("config", None)
-        if config is not None and transformers_config is not None:
-            raise ValueError("Must pass either `config` or `transformers_config`, not both")
-        elif config is not None:
-            # normal layer construction, call with unchanged args (config is already in there)
-            initializer(self, *args, **kwargs)
-        elif transformers_config is not None:
-            # Keras deserialization, convert dict to config
-            config = config_class.from_dict(transformers_config)
-            initializer(self, config, *args, **kwargs)
-        else:
-            raise ValueError("Must pass either `config` (PretrainedConfig) or `transformers_config` (dict)")
-        self._transformers_config = config
-
-    cls.__init__ = wrapped_init
-
-    if not hasattr(cls, "get_config"):
-        raise TypeError("Only use @keras_serializable on tf.keras.layers.Layer subclasses")
-    if hasattr(cls.get_config, "_is_default"):
-
-        def get_config(self):
-            cfg = super(cls, self).get_config()
-            cfg["transformers_config"] = self._transformers_config.to_dict()
-            return cfg
-
-        cls.get_config = get_config
-
-    cls._keras_serializable = True
-    if hasattr(tf.keras.utils, "register_keras_serializable"):
-        cls = tf.keras.utils.register_keras_serializable()(cls)
-    return cls
-
-
 class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
    r""" Base class for all TF models.

@@ -459,9 +402,7 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
        self,
        input_ids=None,
        max_length=None,
-        min_length=None,
        do_sample=True,
-        early_stopping=False,
        num_beams=None,
        temperature=None,
        top_k=None,
@@ -471,9 +412,7 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
        pad_token_id=None,
        eos_token_ids=None,
        length_penalty=None,
-        no_repeat_ngram_size=None,
        num_return_sequences=None,
-        attention_mask=None,
    ):
        r""" Generates sequences for models with a LM head. The method currently supports greedy or penalized greedy decoding, sampling with top-k or nucleus sampling
        and beam-search.
@@ -563,13 +502,11 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
        if self.get_output_embeddings() is None:
            raise AttributeError(
                "You tried to generate sequences with a model that does not have a LM Head."
-                "Please use another model class (e.g. `TFOpenAIGPTLMHeadModel`, `TFXLNetLMHeadModel`, `TFGPT2LMHeadModel`, `TFCTRLLMHeadModel`, `TFT5WithLMHeadModel`, `TFTransfoXLLMHeadModel`)"
+                "Please use another model class (e.g. `OpenAIGPTLMHeadModel`, `XLNetLMHeadModel`, `GPT2LMHeadModel`, `CTRLLMHeadModel`, `T5WithLMHeadModel`, `TransfoXLLMHeadModel`)"
            )

        max_length = max_length if max_length is not None else self.config.max_length
-        min_length = min_length if min_length is not None else self.config.min_length
        do_sample = do_sample if do_sample is not None else self.config.do_sample
-        early_stopping = early_stopping if early_stopping is not None else self.config.early_stopping
        num_beams = num_beams if num_beams is not None else self.config.num_beams
        temperature = temperature if temperature is not None else self.config.temperature
        top_k = top_k if top_k is not None else self.config.top_k
@@ -579,9 +516,6 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
        pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
        eos_token_ids = eos_token_ids if eos_token_ids is not None else self.config.eos_token_ids
        length_penalty = length_penalty if length_penalty is not None else self.config.length_penalty
-        no_repeat_ngram_size = (
-            no_repeat_ngram_size if no_repeat_ngram_size is not None else self.config.no_repeat_ngram_size
-        )
        num_return_sequences = (
            num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences
        )
@@ -594,9 +528,7 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
            eos_token_ids = [eos_token_ids]

        assert isinstance(max_length, int) and max_length > 0, "`max_length` should be a strictely positive integer."
-        assert isinstance(min_length, int) and min_length >= 0, "`min_length` should be a positive integer."
        assert isinstance(do_sample, bool), "`do_sample` should be a boolean."
-        assert isinstance(early_stopping, bool), "`early_stopping` should be a boolean."
        assert isinstance(num_beams, int) and num_beams > 0, "`num_beams` should be a strictely positive integer."
        assert temperature > 0, "`temperature` should be strictely positive."
        assert isinstance(top_k, int) and top_k >= 0, "`top_k` should be a positive integer."
@@ -625,7 +557,6 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
        else:
            assert len(shape_list(input_ids)) == 2, "Input prompt should be of shape (batch_size, sequence length)."

-        # not allow to duplicate outputs when greedy decoding
        if do_sample is False:
            if num_beams == 1:
                # no_beam_search greedy generation conditions
@@ -639,13 +570,6 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
                    num_beams >= num_return_sequences
                ), "Greedy beam search decoding cannot return more sequences than it has beams. Please set num_beams >= num_return_sequences"

-        # create attention mask if necessary
-        # TODO (PVP): this should later be handled by the forward fn() in each model in the future see PR 3140
-        if (attention_mask is None) and (pad_token_id is not None) and (pad_token_id in input_ids.numpy()):
-            attention_mask = tf.cast(tf.math.not_equal(input_ids, pad_token_id), dtype=tf.int32)
-        elif attention_mask is None:
-            attention_mask = tf.ones_like(input_ids)
-
        if pad_token_id is None and eos_token_ids is not None:
            logger.warning(
                "Setting `pad_token_id` to {} (first `eos_token_id`) to generate sequence".format(eos_token_ids[0])
@@ -656,69 +580,45 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
        cur_len = shape_list(input_ids)[1]
        vocab_size = self.config.vocab_size

-        # set effective batch size and effective batch multiplier according to do_sample
-        if do_sample:
+        if num_return_sequences != 1 and do_sample:
+            # Expand input to num return sequences
+            input_ids = tf.broadcast_to(tf.expand_dims(input_ids, 1), (batch_size, num_return_sequences, cur_len))
            effective_batch_size = batch_size * num_return_sequences
-            effective_batch_mult = num_return_sequences
+            input_ids = tf.reshape(input_ids, (effective_batch_size, cur_len))
        else:
            effective_batch_size = batch_size
-            effective_batch_mult = 1
-
-        # Expand input ids if num_beams > 1 or num_return_sequences > 1
-        if num_return_sequences > 1 or num_beams > 1:
-            input_ids_len = shape_list(input_ids)[-1]
-            input_ids = tf.broadcast_to(
-                tf.expand_dims(input_ids, 1), (batch_size, effective_batch_mult * num_beams, input_ids_len)
-            )
-            attention_mask = tf.broadcast_to(
-                tf.expand_dims(attention_mask, 1), (batch_size, effective_batch_mult * num_beams, input_ids_len)
-            )
-            input_ids = tf.reshape(
-                input_ids, (effective_batch_size * num_beams, input_ids_len)
-            )  # shape: (batch_size * num_return_sequences * num_beams, cur_len)
-            attention_mask = tf.reshape(
-                attention_mask, (effective_batch_size * num_beams, input_ids_len)
-            )  # shape: (batch_size * num_return_sequences * num_beams, cur_len)

        if num_beams > 1:
            output = self._generate_beam_search(
                input_ids,
-                cur_len=cur_len,
-                max_length=max_length,
-                min_length=min_length,
-                do_sample=do_sample,
-                early_stopping=early_stopping,
-                temperature=temperature,
-                top_k=top_k,
-                top_p=top_p,
-                repetition_penalty=repetition_penalty,
-                no_repeat_ngram_size=no_repeat_ngram_size,
-                pad_token_id=pad_token_id,
-                eos_token_ids=eos_token_ids,
-                batch_size=effective_batch_size,
-                num_return_sequences=num_return_sequences,
-                length_penalty=length_penalty,
-                num_beams=num_beams,
-                vocab_size=vocab_size,
-                attention_mask=attention_mask,
+                cur_len,
+                max_length,
+                do_sample,
+                temperature,
+                top_k,
+                top_p,
+                repetition_penalty,
+                pad_token_id,
+                eos_token_ids,
+                effective_batch_size,
+                num_return_sequences,
+                length_penalty,
+                num_beams,
+                vocab_size,
            )
        else:
            output = self._generate_no_beam_search(
                input_ids,
-                cur_len=cur_len,
-                max_length=max_length,
-                min_length=min_length,
-                do_sample=do_sample,
-                temperature=temperature,
-                top_k=top_k,
-                top_p=top_p,
-                repetition_penalty=repetition_penalty,
-                no_repeat_ngram_size=no_repeat_ngram_size,
-                pad_token_id=pad_token_id,
-                eos_token_ids=eos_token_ids,
-                batch_size=effective_batch_size,
-                vocab_size=vocab_size,
-                attention_mask=attention_mask,
+                cur_len,
+                max_length,
+                do_sample,
+                temperature,
+                top_k,
+                top_p,
+                repetition_penalty,
+                pad_token_id,
+                eos_token_ids,
+                effective_batch_size,
            )

        return output
@@ -728,18 +628,14 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
        input_ids,
        cur_len,
        max_length,
-        min_length,
        do_sample,
        temperature,
        top_k,
        top_p,
        repetition_penalty,
-        no_repeat_ngram_size,
        pad_token_id,
        eos_token_ids,
        batch_size,
-        vocab_size,
-        attention_mask,
    ):
        """ Generate sequences for each example without beam search (num_beams == 1).
            All returned sequence are generated independantly.
@@ -752,7 +648,7 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
        past = None

        while cur_len < max_length:
-            model_inputs = self.prepare_inputs_for_generation(input_ids, past=past, attention_mask=attention_mask)
+            model_inputs = self.prepare_inputs_for_generation(input_ids, past=past)
            outputs = self(**model_inputs)
            next_token_logits = outputs[0][:, -1, :]

@@ -767,33 +663,6 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
                )
                next_token_logits = tf.math.multiply(next_token_logits, next_token_logits_penalties)

-            if no_repeat_ngram_size > 0:
-                # calculate a list of banned tokens to prevent repetitively generating the same ngrams
-                # from fairseq: https://github.com/pytorch/fairseq/blob/a07cb6f40480928c9e0548b737aadd36ee66ac76/fairseq/sequence_generator.py#L345
-                banned_tokens = calc_banned_tokens(input_ids, batch_size, no_repeat_ngram_size, cur_len)
-                # create banned_tokens boolean mask
-                banned_tokens_indices_mask = []
-                for banned_tokens_slice in banned_tokens:
-                    banned_tokens_indices_mask.append(
-                        [True if token in banned_tokens_slice else False for token in range(vocab_size)]
-                    )
-
-                next_token_logits = set_tensor_by_indices_to_value(
-                    next_token_logits, tf.convert_to_tensor(banned_tokens_indices_mask, dtype=tf.bool), -float("inf")
-                )
-
-            # set eos token prob to zero if min_length is not reached
-            if eos_token_ids is not None and cur_len < min_length:
-                # create eos_token_ids boolean mask
-                is_token_logit_eos_token = tf.convert_to_tensor(
-                    [True if token in eos_token_ids else False for token in range(vocab_size)], dtype=tf.bool
-                )
-                eos_token_indices_mask = tf.broadcast_to(is_token_logit_eos_token, [batch_size, vocab_size])
-
-                next_token_logits = set_tensor_by_indices_to_value(
-                    next_token_logits, eos_token_indices_mask, -float("inf")
-                )
-
            if do_sample:
                # Temperature (higher temperature => more likely to sample low probability tokens)
                if temperature != 1.0:
@@ -832,12 +701,12 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
                    # unfinished_sents is set to zero if eos in sentence
                    unfinished_sents -= is_sents_unfinished_and_token_to_add_is_eos

+            cur_len = cur_len + 1
+
            # stop when there is a </s> in each sentence, or if we exceed the maximul length
            if tf.math.reduce_max(unfinished_sents) == 0:
                break

-            cur_len = cur_len + 1
-
        # if there are different sentences lengths in the batch, some batches have to be padded
        min_sent_length = tf.math.reduce_min(sent_lengths)
        max_sent_length = tf.math.reduce_max(sent_lengths)
@@ -865,14 +734,11 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
        input_ids,
        cur_len,
        max_length,
-        min_length,
        do_sample,
-        early_stopping,
        temperature,
        top_k,
        top_p,
        repetition_penalty,
-        no_repeat_ngram_size,
        pad_token_id,
        eos_token_ids,
        batch_size,
@@ -880,26 +746,29 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
        length_penalty,
        num_beams,
        vocab_size,
-        attention_mask,
    ):
        """ Generate sequences for each example with beam search.
        """

+        # Expand input to num beams
+        input_ids = tf.broadcast_to(tf.expand_dims(input_ids, 1), (batch_size, num_beams, cur_len))
+        input_ids = tf.reshape(input_ids, (batch_size * num_beams, cur_len))  # (batch_size * num_beams, cur_len)
+
        # generated hypotheses
        generated_hyps = [
-            BeamHypotheses(num_beams, max_length, length_penalty, early_stopping=early_stopping)
-            for _ in range(batch_size)
+            BeamHypotheses(num_beams, max_length, length_penalty, early_stopping=False) for _ in range(batch_size)
        ]

-        # for greedy decoding it is made sure that only tokens of the first beam are considered to avoid sampling the exact same tokens three times
+        # scores for each sentence in the beam
        if do_sample is False:
            beam_scores_begin = tf.zeros((batch_size, 1), dtype=tf.float32)
-            beam_scores_end = tf.ones((batch_size, num_beams - 1), dtype=tf.float32) * (-1e9)
+            beam_scores_end = tf.zeros((batch_size, num_beams - 1), dtype=tf.float32) * 1e-9
            beam_scores = tf.concat([beam_scores_begin, beam_scores_end], -1)
        else:
            beam_scores = tf.zeros((batch_size, num_beams), dtype=tf.float32)

        beam_scores = tf.reshape(beam_scores, (batch_size * num_beams,))
+
        # cache compute states
        past = None

@@ -907,7 +776,7 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
        done = [False for _ in range(batch_size)]

        while cur_len < max_length:
-            model_inputs = self.prepare_inputs_for_generation(input_ids, past=past, attention_mask=attention_mask)
+            model_inputs = self.prepare_inputs_for_generation(input_ids, past=past)
            outputs = self(**model_inputs)  # (batch_size * num_beams, cur_len, vocab_size)
            next_token_logits = outputs[0][:, -1, :]  # (batch_size * num_beams, vocab_size)

@@ -922,42 +791,12 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
                )
                next_token_logits = tf.math.multiply(next_token_logits, next_token_logits_penalties)

-            # Temperature (higher temperature => more likely to sample low probability tokens)
-            if temperature != 1.0:
-                next_token_logits = next_token_logits / temperature
-
-            #             calculate log softmax score
-            scores = tf.nn.log_softmax(next_token_logits, axis=-1)  # (batch_size * num_beams, vocab_size)
-
-            # set eos token prob to zero if min_length is not reached
-            if eos_token_ids is not None and cur_len < min_length:
-                # create eos_token_ids boolean mask
-                is_token_logit_eos_token = tf.convert_to_tensor(
-                    [True if token in eos_token_ids else False for token in range(vocab_size)], dtype=tf.bool
-                )
-                eos_token_indices_mask = tf.broadcast_to(is_token_logit_eos_token, [batch_size, vocab_size])
-
-                scores = set_tensor_by_indices_to_value(scores, eos_token_indices_mask, -float("inf"))
-
-            if no_repeat_ngram_size > 0:
-                # calculate a list of banned tokens to prevent repetitively generating the same ngrams
-                # from fairseq: https://github.com/pytorch/fairseq/blob/a07cb6f40480928c9e0548b737aadd36ee66ac76/fairseq/sequence_generator.py#L345
-                num_batch_hypotheses = batch_size * num_beams
-                banned_tokens = calc_banned_tokens(input_ids, num_batch_hypotheses, no_repeat_ngram_size, cur_len)
-                # create banned_tokens boolean mask
-                banned_tokens_indices_mask = []
-                for banned_tokens_slice in banned_tokens:
-                    banned_tokens_indices_mask.append(
-                        [True if token in banned_tokens_slice else False for token in range(vocab_size)]
-                    )
-
-                scores = set_tensor_by_indices_to_value(
-                    scores, tf.convert_to_tensor(banned_tokens_indices_mask, dtype=tf.bool), -float("inf")
-                )
-
-            assert shape_list(scores) == [batch_size * num_beams, vocab_size]
-
            if do_sample:
+                # Temperature (higher temperature => more likely to sample low probability tokens)
+                if temperature != 1.0:
+                    next_token_logits = next_token_logits / temperature
+
+                scores = tf.nn.log_softmax(next_token_logits, axis=-1)  # (batch_size * num_beams, vocab_size)
                _scores = scores + tf.broadcast_to(
                    beam_scores[:, None], (batch_size * num_beams, vocab_size)
                )  # (batch_size * num_beams, vocab_size)
@@ -974,12 +813,10 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
                )  # (batch_size, 2 * num_beams)
                # Compute next scores
                next_scores = tf.gather(_scores, next_tokens, batch_dims=1)  # (batch_size, 2 * num_beams)
-
-                # sort the sampled vector to make sure that the first num_beams samples are the best
-                next_scores_indices = tf.argsort(next_scores, direction="DESCENDING", axis=1)
-                next_scores = tf.gather(next_scores, next_scores_indices, batch_dims=1)  # (batch_size, num_beams * 2)
-                next_tokens = tf.gather(next_tokens, next_scores_indices, batch_dims=1)  # (batch_size, num_beams * 2)
            else:
+                # do greedy beam search
+                scores = tf.nn.log_softmax(next_token_logits, axis=-1)  # (batch_size * num_beams, vocab_size)
+                assert shape_list(scores) == [batch_size * num_beams, vocab_size]
                # Add the log prob of the new beams to the log prob of the beginning of the sequence (sum of logs == log of the product)
                next_scores = scores + tf.broadcast_to(
                    beam_scores[:, None], (batch_size * num_beams, vocab_size)
@@ -989,8 +826,7 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
                next_scores = tf.reshape(
                    next_scores, (batch_size, num_beams * vocab_size)
                )  # (batch_size, num_beams * vocab_size)
-
-                next_scores, next_tokens = tf.math.top_k(next_scores, k=2 * num_beams, sorted=True)
+                next_scores, next_tokens = tf.math.top_k(next_scores, 2 * num_beams, sorted=True)

            assert shape_list(next_scores) == shape_list(next_tokens) == [batch_size, 2 * num_beams]

@@ -1001,6 +837,10 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
            # for each sentence
            for batch_idx in range(batch_size):

+                # if we are done with this sentence
+                done[batch_idx] = done[batch_idx] or generated_hyps[batch_idx].is_done(
+                    tf.reduce_max(next_scores[batch_idx]).numpy()
+                )
                if done[batch_idx]:
                    assert (
                        len(generated_hyps[batch_idx]) >= num_beams
@@ -1015,46 +855,30 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
                next_sent_beam = []

                # next tokens for this sentence
-                for beam_token_rank, (beam_token_id, beam_token_score) in enumerate(
-                    zip(next_tokens[batch_idx], next_scores[batch_idx])
-                ):
+                for idx, score in zip(next_tokens[batch_idx], next_scores[batch_idx]):

                    # get beam and token IDs
-                    beam_id = beam_token_id // vocab_size
-                    token_id = beam_token_id % vocab_size
+                    beam_id = idx // vocab_size
+                    token_id = idx % vocab_size

-                    effective_beam_id = batch_idx * num_beams + beam_id
                    # add to generated hypotheses if end of sentence or last iteration
                    if eos_token_ids is not None and token_id.numpy() in eos_token_ids:
-                        # if beam_token does not belong to top num_beams tokens, it should not be added
-                        is_beam_token_worse_than_top_num_beams = beam_token_rank >= num_beams
-                        if is_beam_token_worse_than_top_num_beams:
-                            continue
                        generated_hyps[batch_idx].add(
-                            tf.identity(input_ids[effective_beam_id]), beam_token_score.numpy()
+                            tf.identity(input_ids[batch_idx * num_beams + beam_id, :cur_len]), score.numpy()
                        )
                    else:
                        # add next predicted token if it is not eos_token
-                        next_sent_beam.append((beam_token_score, token_id, effective_beam_id))
+                        next_sent_beam.append((score, token_id, batch_idx * num_beams + beam_id))

                    # the beam for next step is full
                    if len(next_sent_beam) == num_beams:
                        break

-                # if we are done with this sentence
-                done[batch_idx] = done[batch_idx] or generated_hyps[batch_idx].is_done(
-                    tf.reduce_max(next_scores[batch_idx]).numpy()
-                )
-
                # update next beam content
                assert len(next_sent_beam) == num_beams, "Beam should always be full"
                next_batch_beam.extend(next_sent_beam)
                assert len(next_batch_beam) == num_beams * (batch_idx + 1)

-            # stop when we are done with each sentence
-            if all(done):
-                break
-
            # sanity check / prepare next batch
            assert len(next_batch_beam) == batch_size * num_beams
            beam_scores = tf.convert_to_tensor([x[0] for x in next_batch_beam], dtype=tf.float32)
@@ -1064,6 +888,7 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
            # re-order batch
            input_ids = tf.stack([tf.identity(input_ids[x, :]) for x in beam_idx])
            input_ids = tf.concat([input_ids, tf.expand_dims(beam_tokens, 1)], axis=-1)
+
            # re-order internal states
            if past:
                past = self._reorder_cache(past, beam_idx)
@@ -1071,27 +896,21 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin):
            # update current length
            cur_len = cur_len + 1

-        # finalize all open beam hypotheses and end to generated hypotheses
+            # stop when we are done with each sentence
+            if all(done):
+                break
+
        for batch_idx in range(batch_size):
            # Add all open beam hypothesis to generated_hyps
-            if done[batch_idx]:
-                continue
-            # test that beam scores match previously calculated scores if not eos and batch_idx not done
-            if eos_token_ids is not None and all(
-                (token_id % vocab_size).numpy().item() not in eos_token_ids for token_id in next_tokens[batch_idx]
-            ):
-                assert tf.reduce_all(
-                    next_scores[batch_idx, :num_beams] == tf.reshape(beam_scores, (batch_size, num_beams))[batch_idx]
-                ), "If batch_idx is not done, final next scores: {} have to equal to accumulated beam_scores: {}".format(
-                    next_scores[:, :num_beams][batch_idx], tf.reshape(beam_scores, (batch_size, num_beams))[batch_idx]
-                )
+            if not done[batch_idx]:
+                for idx, score in zip(next_tokens[batch_idx], next_scores[batch_idx]):

-            # need to add best num_beams hypotheses to generated hyps
-            for beam_id in range(num_beams):
-                effective_beam_id = batch_idx * num_beams + beam_id
-                final_score = beam_scores[effective_beam_id].numpy().item()
-                final_tokens = input_ids[effective_beam_id]
-                generated_hyps[batch_idx].add(final_tokens, final_score)
+                    # get beam and token IDs
+                    beam_id = idx // vocab_size
+                    token_id = idx % vocab_size
+                    generated_hyps[batch_idx].add(
+                        tf.identity(input_ids[batch_idx * num_beams + beam_id, :cur_len]), score.numpy()
+                    )

        # depending on whether greedy generation is wanted or not define different output_batch_size and output_num_return_sequences_per_batch
        output_batch_size = batch_size if do_sample else batch_size * num_return_sequences
@@ -1161,37 +980,13 @@ def _create_next_token_logits_penalties(input_ids, logits, repetition_penalty):
    prev_input_ids = [np.unique(input_id) for input_id in input_ids.numpy()]
    for i, prev_input_id in enumerate(prev_input_ids):
        logit_penalized = logits[i].numpy()[prev_input_id]
-        logit_penalties = np.zeros(logit_penalized.shape)
        # if previous logit score is < 0 then multiply repetition penalty else divide
-        logit_penalties[logit_penalized < 0] = repetition_penalty
-        logit_penalties[logit_penalized > 0] = 1 / repetition_penalty
-        np.put(token_penalties[i], prev_input_id, logit_penalties)
+        logit_penalized[logit_penalized < 0] = repetition_penalty
+        logit_penalized[logit_penalized > 0] = 1 / repetition_penalty
+        np.put(token_penalties[i], prev_input_id, logit_penalized)
    return tf.convert_to_tensor(token_penalties, dtype=tf.float32)


-def calc_banned_tokens(prev_input_ids, num_hypos, no_repeat_ngram_size, cur_len):
-    # Copied from fairseq for no_repeat_ngram in beam_search"""
-    if cur_len + 1 < no_repeat_ngram_size:
-        # return no banned tokens if we haven't generated no_repeat_ngram_size tokens yet
-        return [[] for _ in range(num_hypos)]
-    generated_ngrams = [{} for _ in range(num_hypos)]
-    for idx in range(num_hypos):
-        gen_tokens = prev_input_ids[idx].numpy().tolist()
-        generated_ngram = generated_ngrams[idx]
-        for ngram in zip(*[gen_tokens[i:] for i in range(no_repeat_ngram_size)]):
-            prev_ngram_tuple = tuple(ngram[:-1])
-            generated_ngram[prev_ngram_tuple] = generated_ngram.get(prev_ngram_tuple, []) + [ngram[-1]]
-
-    def _get_generated_ngrams(hypo_idx):
-        # Before decoding the next token, prevent decoding of ngrams that have already appeared
-        start_idx = cur_len + 1 - no_repeat_ngram_size
-        ngram_idx = tuple(prev_input_ids[hypo_idx, start_idx:cur_len].numpy().tolist())
-        return generated_ngrams[hypo_idx].get(ngram_idx, [])
-
-    banned_tokens = [_get_generated_ngrams(hypo_idx) for hypo_idx in range(num_hypos)]
-    return banned_tokens
-
-
 def tf_top_k_top_p_filtering(logits, top_k=0, top_p=1.0, filter_value=-float("Inf"), min_tokens_to_keep=1):
    """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
        Args:
@@ -408,7 +408,7 @@ class TFXLMMainLayer(tf.keras.layers.Layer):
            inputs_embeds = self.embeddings(input_ids)

        tensor = inputs_embeds + self.position_embeddings(position_ids)
-        if langs is not None and self.use_lang_emb and self.n_langs > 1:
+        if langs is not None and self.use_lang_emb:
            tensor = tensor + self.lang_embeddings(langs)
        if token_type_ids is not None:
            tensor = tensor + self.embeddings(token_type_ids)
@@ -24,14 +24,7 @@ import tensorflow as tf

 from .configuration_xlnet import XLNetConfig
 from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
-from .modeling_tf_utils import (
-    TFPreTrainedModel,
-    TFSequenceSummary,
-    TFSharedEmbeddings,
-    get_initializer,
-    keras_serializable,
-    shape_list,
-)
+from .modeling_tf_utils import TFPreTrainedModel, TFSequenceSummary, TFSharedEmbeddings, get_initializer, shape_list


 logger = logging.getLogger(__name__)
@@ -349,10 +342,7 @@ class TFXLNetLMHead(tf.keras.layers.Layer):
        return hidden_states


-@keras_serializable
 class TFXLNetMainLayer(tf.keras.layers.Layer):
-    config_class = XLNetConfig
-
    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)
        self.output_attentions = config.output_attentions
@@ -173,7 +173,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
        if getattr(output_embeddings, "bias", None) is not None:
            output_embeddings.bias.data = torch.nn.functional.pad(
                output_embeddings.bias.data,
-                (0, output_embeddings.weight.shape[0] - output_embeddings.bias.shape[0],),
+                (0, output_embeddings.weight.shape[0] - output_embeddings.bias.shape[0]),
                "constant",
                0,
            )
@@ -411,8 +411,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
                else:
                    raise EnvironmentError(
                        "Error no file named {} found in directory {} or `from_tf` set to False".format(
-                            [WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME + ".index"],
-                            pretrained_model_name_or_path,
+                            [WEIGHTS_NAME, TF2_WEIGHTS_NAME, TF_WEIGHTS_NAME + ".index"], pretrained_model_name_or_path
                        )
                    )
            elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
@@ -426,7 +425,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
                archive_file = pretrained_model_name_or_path + ".index"
            else:
                archive_file = hf_bucket_url(
-                    pretrained_model_name_or_path, postfix=(TF2_WEIGHTS_NAME if from_tf else WEIGHTS_NAME),
+                    pretrained_model_name_or_path, postfix=(TF2_WEIGHTS_NAME if from_tf else WEIGHTS_NAME)
                )

            # redirect to the cache, if necessary
@@ -521,7 +520,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
            def load(module: nn.Module, prefix=""):
                local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
                module._load_from_state_dict(
-                    state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs,
+                    state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs
                )
                for name, child in module._modules.items():
                    if child is not None:
@@ -585,9 +584,6 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
    def prepare_inputs_for_generation(self, input_ids, **kwargs):
        return {"input_ids": input_ids}

-    def prepare_scores_for_generation(self, scores, **kwargs):
-        return scores
-
    def _do_output_past(self, outputs):
        """During generation, decide whether to pass the `past` variable to the next forward pass."""
        has_output_past = getattr(self.config, "output_past", False)
@@ -613,9 +609,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
        self,
        input_ids=None,
        max_length=None,
-        min_length=None,
        do_sample=True,
-        early_stopping=False,
        num_beams=None,
        temperature=None,
        top_k=None,
@@ -625,9 +619,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
        pad_token_id=None,
        eos_token_ids=None,
        length_penalty=None,
-        no_repeat_ngram_size=None,
        num_return_sequences=None,
-        attention_mask=None,
    ):
        r""" Generates sequences for models with a LM head. The method currently supports greedy or penalized greedy decoding, sampling with top-k or nucleus sampling
        and beam-search.
@@ -721,9 +713,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
            )

        max_length = max_length if max_length is not None else self.config.max_length
-        min_length = min_length if min_length is not None else self.config.min_length
        do_sample = do_sample if do_sample is not None else self.config.do_sample
-        early_stopping = early_stopping if early_stopping is not None else self.config.early_stopping
        num_beams = num_beams if num_beams is not None else self.config.num_beams
        temperature = temperature if temperature is not None else self.config.temperature
        top_k = top_k if top_k is not None else self.config.top_k
@@ -733,9 +723,6 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
        pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
        eos_token_ids = eos_token_ids if eos_token_ids is not None else self.config.eos_token_ids
        length_penalty = length_penalty if length_penalty is not None else self.config.length_penalty
-        no_repeat_ngram_size = (
-            no_repeat_ngram_size if no_repeat_ngram_size is not None else self.config.no_repeat_ngram_size
-        )
        num_return_sequences = (
            num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences
        )
@@ -748,9 +735,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
            eos_token_ids = [eos_token_ids]

        assert isinstance(max_length, int) and max_length > 0, "`max_length` should be a strictly positive integer."
-        assert isinstance(min_length, int) and min_length >= 0, "`min_length` should be a positive integer."
        assert isinstance(do_sample, bool), "`do_sample` should be a boolean."
-        assert isinstance(early_stopping, bool), "`early_stopping` should be a boolean."
        assert isinstance(num_beams, int) and num_beams > 0, "`num_beams` should be a strictly positive integer."
        assert temperature > 0, "`temperature` should be strictly positive."
        assert isinstance(top_k, int) and top_k >= 0, "`top_k` should be a positive integer."
@@ -766,9 +751,6 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
            isinstance(eos_token_ids, (list, tuple)) and ((isinstance(e, int) and e >= 0) for e in eos_token_ids)
        ), "`eos_token_ids` should be a positive integer or a list/tuple of positive integers."
        assert length_penalty > 0, "`length_penalty` should be strictly positive."
-        assert (
-            isinstance(no_repeat_ngram_size, int) and no_repeat_ngram_size >= 0
-        ), "`no_repeat_ngram_size` should be a positive integer."
        assert (
            isinstance(num_return_sequences, int) and num_return_sequences > 0
        ), "`num_return_sequences` should be a strictly positive integer."
@@ -779,7 +761,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
                "or a `bos_token_id` (integer >= 0) as a first token to start the generation."
            )
            input_ids = torch.full(
-                (batch_size, 1), bos_token_id, dtype=torch.long, device=next(self.parameters()).device,
+                (batch_size, 1), bos_token_id, dtype=torch.long, device=next(self.parameters()).device
            )
        else:
            assert input_ids.dim() == 2, "Input prompt should be of shape (batch_size, sequence length)."
@@ -798,15 +780,6 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
                    num_beams >= num_return_sequences
                ), "Greedy beam search decoding cannot return more sequences than it has beams. Please set num_beams >= num_return_sequences"

-        # create attention mask if necessary
-        # TODO (PVP): this should later be handled by the forward fn() in each model in the future see PR 3140
-        if (attention_mask is None) and (pad_token_id is not None) and (pad_token_id in input_ids):
-            attention_mask = input_ids.ne(pad_token_id).long()
-        elif attention_mask is None:
-            attention_mask = input_ids.new_ones(input_ids.shape)
-
-        # set pad_token_id to eos_token_ids if not set. Important that this is done after
-        # attention_mask is created
        if pad_token_id is None and eos_token_ids is not None:
            logger.warning(
                "Setting `pad_token_id` to {} (first `eos_token_id`) to generate sequence".format(eos_token_ids[0])
@@ -814,6 +787,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
            pad_token_id = eos_token_ids[0]

        # current position and vocab size
+        cur_len = input_ids.shape[1]
        vocab_size = self.config.vocab_size

        # set effective batch size and effective batch multiplier according to do_sample
@@ -828,77 +802,41 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
        if num_return_sequences > 1 or num_beams > 1:
            input_ids_len = input_ids.shape[-1]
            input_ids = input_ids.unsqueeze(1).expand(batch_size, effective_batch_mult * num_beams, input_ids_len)
-            attention_mask = attention_mask.unsqueeze(1).expand(
-                batch_size, effective_batch_mult * num_beams, input_ids_len
-            )
-
            input_ids = input_ids.contiguous().view(
                effective_batch_size * num_beams, input_ids_len
            )  # shape: (batch_size * num_return_sequences * num_beams, cur_len)
-            attention_mask = attention_mask.contiguous().view(
-                effective_batch_size * num_beams, input_ids_len
-            )  # shape: (batch_size * num_return_sequences * num_beams, cur_len)
-
-        if self.config.is_encoder_decoder:
-            assert bos_token_id is not None, "Encoder Decoder Models need to have a bos_token_id"
-            # encoder decoder need to start with empty input_ids and copy the input_ids to encoder_inputs
-            encoder_inputs = input_ids
-            input_ids = torch.full(
-                (effective_batch_size * num_beams, 1),
-                bos_token_id,
-                dtype=torch.long,
-                device=next(self.parameters()).device,
-            )
-            cur_len = 1
-
-            # put model in generation mode if it has one
-            if hasattr(self.model, "decoder") and hasattr(self.model.decoder, "generation_mode"):
-                self.model.decoder.generation_mode = True
-        else:
-            encoder_inputs = None
-            cur_len = input_ids.shape[-1]

        if num_beams > 1:
            output = self._generate_beam_search(
                input_ids,
-                cur_len=cur_len,
-                max_length=max_length,
-                min_length=min_length,
-                do_sample=do_sample,
-                early_stopping=early_stopping,
-                temperature=temperature,
-                top_k=top_k,
-                top_p=top_p,
-                repetition_penalty=repetition_penalty,
-                no_repeat_ngram_size=no_repeat_ngram_size,
-                bos_token_id=bos_token_id,
-                pad_token_id=pad_token_id,
-                eos_token_ids=eos_token_ids,
-                batch_size=effective_batch_size,
-                num_return_sequences=num_return_sequences,
-                length_penalty=length_penalty,
-                num_beams=num_beams,
-                vocab_size=vocab_size,
-                encoder_inputs=encoder_inputs,
-                attention_mask=attention_mask,
+                cur_len,
+                max_length,
+                do_sample,
+                temperature,
+                top_k,
+                top_p,
+                repetition_penalty,
+                pad_token_id,
+                eos_token_ids,
+                effective_batch_size,
+                num_return_sequences,
+                length_penalty,
+                num_beams,
+                vocab_size,
            )
        else:
            output = self._generate_no_beam_search(
                input_ids,
-                cur_len=cur_len,
-                max_length=max_length,
-                min_length=min_length,
-                do_sample=do_sample,
-                temperature=temperature,
-                top_k=top_k,
-                top_p=top_p,
-                repetition_penalty=repetition_penalty,
-                no_repeat_ngram_size=no_repeat_ngram_size,
-                pad_token_id=pad_token_id,
-                eos_token_ids=eos_token_ids,
-                batch_size=effective_batch_size,
-                encoder_inputs=encoder_inputs,
-                attention_mask=attention_mask,
+                cur_len,
+                max_length,
+                do_sample,
+                temperature,
+                top_k,
+                top_p,
+                repetition_penalty,
+                pad_token_id,
+                eos_token_ids,
+                effective_batch_size,
            )

        return output
@@ -908,18 +846,14 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
        input_ids,
        cur_len,
        max_length,
-        min_length,
        do_sample,
        temperature,
        top_k,
        top_p,
        repetition_penalty,
-        no_repeat_ngram_size,
        pad_token_id,
        eos_token_ids,
        batch_size,
-        encoder_inputs,
-        attention_mask,
    ):
        """ Generate sequences for each example without beam search (num_beams == 1).
            All returned sequence are generated independantly.
@@ -930,9 +864,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):

        past = None
        while cur_len < max_length:
-            model_inputs = self.prepare_inputs_for_generation(
-                input_ids, past=past, encoder_inputs=encoder_inputs, attention_mask=attention_mask
-            )
+            model_inputs = self.prepare_inputs_for_generation(input_ids, past=past)

            outputs = self(**model_inputs)
            next_token_logits = outputs[0][:, -1, :]
@@ -945,18 +877,6 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
            if repetition_penalty != 1.0:
                self.enforce_repetition_penalty_(next_token_logits, batch_size, 1, input_ids, repetition_penalty)

-            if no_repeat_ngram_size > 0:
-                # calculate a list of banned tokens to prevent repetitively generating the same ngrams
-                # from fairseq: https://github.com/pytorch/fairseq/blob/a07cb6f40480928c9e0548b737aadd36ee66ac76/fairseq/sequence_generator.py#L345
-                banned_tokens = calc_banned_tokens(input_ids, batch_size, no_repeat_ngram_size, cur_len)
-                for batch_idx in range(batch_size):
-                    next_token_logits[batch_idx, banned_tokens[batch_idx]] = -float("inf")
-
-            # set eos token prob to zero if min_length is not reached
-            if eos_token_ids is not None and cur_len < min_length:
-                for eos_token_id in eos_token_ids:
-                    next_token_logits[:, eos_token_id] = -float("inf")
-
            if do_sample:
                # Temperature (higher temperature => more likely to sample low probability tokens)
                if temperature != 1.0:
@@ -964,8 +884,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
                # Top-p/top-k filtering
                next_token_logits = top_k_top_p_filtering(next_token_logits, top_k=top_k, top_p=top_p)
                # Sample
-                probs = F.softmax(next_token_logits, dim=-1)
-                next_token = torch.multinomial(probs, num_samples=1).squeeze(1)
+                next_token = torch.multinomial(F.softmax(next_token_logits, dim=-1), num_samples=1).squeeze(1)
            else:
                # Greedy decoding
                next_token = torch.argmax(next_token_logits, dim=-1)
@@ -992,12 +911,6 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
            if unfinished_sents.max() == 0:
                break

-            # extend attention_mask for new generated input if only decoder
-            if self.config.is_encoder_decoder is False:
-                attention_mask = torch.cat(
-                    [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
-                )
-
            cur_len = cur_len + 1

        # if there are different sentences lengths in the batch, some batches have to be padded
@@ -1018,15 +931,11 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
        input_ids,
        cur_len,
        max_length,
-        min_length,
        do_sample,
-        early_stopping,
        temperature,
        top_k,
        top_p,
        repetition_penalty,
-        no_repeat_ngram_size,
-        bos_token_id,
        pad_token_id,
        eos_token_ids,
        batch_size,
@@ -1034,22 +943,18 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
        length_penalty,
        num_beams,
        vocab_size,
-        encoder_inputs,
-        attention_mask,
    ):
        """ Generate sequences for each example with beam search.
        """

        # generated hypotheses
        generated_hyps = [
-            BeamHypotheses(num_beams, max_length, length_penalty, early_stopping=early_stopping)
-            for _ in range(batch_size)
+            BeamHypotheses(num_beams, max_length, length_penalty, early_stopping=False) for _ in range(batch_size)
        ]

        # scores for each sentence in the beam
        beam_scores = torch.zeros((batch_size, num_beams), dtype=torch.float, device=input_ids.device)
-
-        # for greedy decoding it is made sure that only tokens of the first beam are considered to avoid sampling the exact same tokens three times
+        # Greedy decoding it is made sure that only tokens of the first beam are considered to avoid sampling the exact same tokens three times
        if do_sample is False:
            beam_scores[:, 1:] = -1e9
        beam_scores = beam_scores.view(-1)  # shape (batch_size * num_beams,)
@@ -1061,9 +966,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
        done = [False for _ in range(batch_size)]

        while cur_len < max_length:
-            model_inputs = self.prepare_inputs_for_generation(
-                input_ids, past=past, encoder_inputs=encoder_inputs, attention_mask=attention_mask
-            )
+            model_inputs = self.prepare_inputs_for_generation(input_ids, past=past)
            outputs = self(**model_inputs)  # (batch_size * num_beams, cur_len, vocab_size)
            next_token_logits = outputs[0][:, -1, :]  # (batch_size * num_beams, vocab_size)

@@ -1074,59 +977,44 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
            # repetition penalty (from CTRL paper https://arxiv.org/abs/1909.05858)
            if repetition_penalty != 1.0:
                self.enforce_repetition_penalty_(
-                    next_token_logits, batch_size, num_beams, input_ids, repetition_penalty,
+                    next_token_logits, batch_size, num_beams, input_ids, repetition_penalty
                )

-            if temperature != 1.0:
-                next_token_logits = next_token_logits / temperature
-
-            scores = F.log_softmax(next_token_logits, dim=-1)  # (batch_size * num_beams, vocab_size)
-            if self.config.is_encoder_decoder and do_sample is False:
-                # TODO(PVP) to be refactored later - do we need this boolean flag here? Also Only add for beam_search or also for no_beam_search? The prepare scores fn is ugly here
-                scores = self.prepare_scores_for_generation(scores, cur_len, max_length)
-
-            # set eos token prob to zero if min_length is not reached
-            if eos_token_ids is not None and cur_len < min_length:
-                for eos_token_id in eos_token_ids:
-                    scores[:, eos_token_id] = -float("inf")
-
-            if no_repeat_ngram_size > 0:
-                # calculate a list of banned tokens to prevent repetitively generating the same ngrams
-                num_batch_hypotheses = batch_size * num_beams
-                # from fairseq: https://github.com/pytorch/fairseq/blob/a07cb6f40480928c9e0548b737aadd36ee66ac76/fairseq/sequence_generator.py#L345
-                banned_batch_tokens = calc_banned_tokens(
-                    input_ids, num_batch_hypotheses, no_repeat_ngram_size, cur_len
-                )
-                for i, banned_tokens in enumerate(banned_batch_tokens):
-                    scores[i, banned_tokens] = -float("inf")
-
-            assert scores.shape == (batch_size * num_beams, vocab_size), "Shapes of scores: {} != {}".format(
-                scores.shape, (batch_size * num_beams, vocab_size)
-            )
-
            if do_sample:
+                # Temperature (higher temperature => more likely to sample low probability tokens)
+                if temperature != 1.0:
+                    next_token_logits = next_token_logits / temperature
+
+                scores = F.log_softmax(next_token_logits, dim=-1)  # (batch_size * num_beams, vocab_size)
                _scores = scores + beam_scores[:, None].expand_as(scores)  # (batch_size * num_beams, vocab_size)
+
                # Top-p/top-k filtering
                _scores = top_k_top_p_filtering(
                    _scores, top_k=top_k, top_p=top_p, min_tokens_to_keep=2
                )  # (batch_size * num_beams, vocab_size)
+
                # re-organize to group the beam together to sample from all beam_idxs
                _scores = _scores.contiguous().view(
                    batch_size, num_beams * vocab_size
                )  # (batch_size, num_beams * vocab_size)

                # Sample 2 next tokens for each beam (so we have some spare tokens and match output of greedy beam search)
-                probs = F.softmax(_scores, dim=-1)
-                next_tokens = torch.multinomial(probs, num_samples=2 * num_beams)  # (batch_size, num_beams * 2)
+                next_tokens = torch.multinomial(
+                    F.softmax(_scores, dim=-1), num_samples=2 * num_beams
+                )  # (batch_size, num_beams * 2)
+
                # Compute next scores
                next_scores = torch.gather(_scores, -1, next_tokens)  # (batch_size, num_beams * 2)
+
                # sort the sampled vector to make sure that the first num_beams samples are the best
                next_scores, next_scores_indices = torch.sort(next_scores, descending=True, dim=1)
                next_tokens = torch.gather(next_tokens, -1, next_scores_indices)  # (batch_size, num_beams * 2)
-
            else:
+                # do greedy beam search
+                scores = F.log_softmax(next_token_logits, dim=-1)  # (batch_size * num_beams, vocab_size)
+                assert scores.size() == (batch_size * num_beams, vocab_size)
+                # Add the log prob of the new beams to the log prob of the beginning of the sequence (sum of logs == log of the product)
                next_scores = scores + beam_scores[:, None].expand_as(scores)  # (batch_size * num_beams, vocab_size)
-
                # re-organize to group the beam together (we are keeping top hypothesis accross beams)
                next_scores = next_scores.view(
                    batch_size, num_beams * vocab_size
@@ -1137,12 +1025,16 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
            assert next_scores.size() == next_tokens.size() == (batch_size, 2 * num_beams)

            # next batch beam content
+            # list of (batch_size * num_beams) tuple(next hypothesis score, next word, current position in the batch)
            next_batch_beam = []

            # for each sentence
            for batch_idx in range(batch_size):

                # if we are done with this sentence
+                done[batch_idx] = done[batch_idx] or generated_hyps[batch_idx].is_done(
+                    next_scores[batch_idx].max().item()
+                )
                if done[batch_idx]:
                    assert (
                        len(generated_hyps[batch_idx]) >= num_beams
@@ -1157,46 +1049,31 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
                next_sent_beam = []

                # next tokens for this sentence
-                for beam_token_rank, (beam_token_id, beam_token_score) in enumerate(
-                    zip(next_tokens[batch_idx], next_scores[batch_idx])
-                ):
+                for idx, score in zip(next_tokens[batch_idx], next_scores[batch_idx]):
+
                    # get beam and word IDs
-                    beam_id = beam_token_id // vocab_size
-                    token_id = beam_token_id % vocab_size
+                    beam_id = idx // vocab_size
+                    token_id = idx % vocab_size

                    effective_beam_id = batch_idx * num_beams + beam_id
-
                    # add to generated hypotheses if end of sentence
-                    if (eos_token_ids is not None) and (token_id.item() in eos_token_ids):
-                        # if beam_token does not belong to top num_beams tokens, it should not be added
-                        is_beam_token_worse_than_top_num_beams = beam_token_rank >= num_beams
-                        if is_beam_token_worse_than_top_num_beams:
-                            continue
+                    if eos_token_ids is not None and token_id.item() in eos_token_ids:
                        generated_hyps[batch_idx].add(
-                            input_ids[effective_beam_id].clone(), beam_token_score.item(),
+                            input_ids[effective_beam_id].clone(), score.item(),
                        )
                    else:
                        # add next predicted word if it is not eos_token
-                        next_sent_beam.append((beam_token_score, token_id, effective_beam_id))
+                        next_sent_beam.append((score, token_id, effective_beam_id))

                    # the beam for next step is full
                    if len(next_sent_beam) == num_beams:
                        break

-                # Check if were done so that we can save a pad step if all(done)
-                done[batch_idx] = done[batch_idx] or generated_hyps[batch_idx].is_done(
-                    next_scores[batch_idx].max().item(), cur_len=cur_len
-                )
-
                # update next beam content
                assert len(next_sent_beam) == num_beams, "Beam should always be full"
                next_batch_beam.extend(next_sent_beam)
                assert len(next_batch_beam) == num_beams * (batch_idx + 1)

-            # stop when we are done with each sentence
-            if all(done):
-                break
-
            # sanity check / prepare next batch
            assert len(next_batch_beam) == batch_size * num_beams
            beam_scores = beam_scores.new([x[0] for x in next_batch_beam])
@@ -1211,11 +1088,9 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
            if past:
                past = self._reorder_cache(past, beam_idx)

-            # extend attention_mask for new generated input if only decoder
-            if self.config.is_encoder_decoder is False:
-                attention_mask = torch.cat(
-                    [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
-                )
+            # stop when we are done with each sentence
+            if all(done):
+                break

            # update current length
            cur_len = cur_len + 1
@@ -1232,7 +1107,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
                assert torch.all(
                    next_scores[batch_idx, :num_beams] == beam_scores.view(batch_size, num_beams)[batch_idx]
                ), "If batch_idx is not done, final next scores: {} have to equal to accumulated beam_scores: {}".format(
-                    next_scores[:, :num_beams][batch_idx], beam_scores.view(batch_size, num_beams)[batch_idx],
+                    next_scores[:, :num_beams][batch_idx], beam_scores.view(batch_size, num_beams)[batch_idx]
                )

            # need to add best num_beams hypotheses to generated hyps
@@ -1275,23 +1150,8 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
            assert (len(hypo) == max_length for hypo in best)
            decoded = torch.stack(best).type(torch.long).to(next(self.parameters()).device)

-        if self.config.is_encoder_decoder:
-            # do not return first <EOS> token
-            return decoded[:, 1:]
        return decoded

-    # force one of token_ids to be generated by setting prob of all other tokens to 0.
-    def _force_token_ids_generation(self, scores, token_ids):
-        if isinstance(token_ids, int):
-            token_ids = [token_ids]
-        all_but_token_ids_mask = torch.tensor(
-            [x for x in range(self.config.vocab_size) if x not in token_ids],
-            dtype=torch.long,
-            device=next(self.parameters()).device,
-        )
-        assert len(scores.shape) == 2, "scores should be of rank 2 with shape: [batch_size, vocab_size]"
-        scores[:, all_but_token_ids_mask] = -float("inf")
-
    @staticmethod
    def _reorder_cache(past, beam_idx):
        reordered_past = []
@@ -1307,29 +1167,6 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
        return past


-def calc_banned_tokens(prev_input_ids, num_hypos, no_repeat_ngram_size, cur_len):
-    # Copied from fairseq for no_repeat_ngram in beam_search"""
-    if cur_len + 1 < no_repeat_ngram_size:
-        # return no banned tokens if we haven't generated no_repeat_ngram_size tokens yet
-        return [[] for _ in range(num_hypos)]
-    generated_ngrams = [{} for _ in range(num_hypos)]
-    for idx in range(num_hypos):
-        gen_tokens = prev_input_ids[idx].tolist()
-        generated_ngram = generated_ngrams[idx]
-        for ngram in zip(*[gen_tokens[i:] for i in range(no_repeat_ngram_size)]):
-            prev_ngram_tuple = tuple(ngram[:-1])
-            generated_ngram[prev_ngram_tuple] = generated_ngram.get(prev_ngram_tuple, []) + [ngram[-1]]
-
-    def _get_generated_ngrams(hypo_idx):
-        # Before decoding the next token, prevent decoding of ngrams that have already appeared
-        start_idx = cur_len + 1 - no_repeat_ngram_size
-        ngram_idx = tuple(prev_input_ids[hypo_idx, start_idx:cur_len].tolist())
-        return generated_ngrams[hypo_idx].get(ngram_idx, [])
-
-    banned_tokens = [_get_generated_ngrams(hypo_idx) for hypo_idx in range(num_hypos)]
-    return banned_tokens
-
-
 def top_k_top_p_filtering(logits, top_k=0, top_p=1.0, filter_value=-float("Inf"), min_tokens_to_keep=1):
    """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
        Args:
@@ -1603,7 +1440,7 @@ class SQuADHead(nn.Module):
        self.answer_class = PoolerAnswerClass(config)

    def forward(
-        self, hidden_states, start_positions=None, end_positions=None, cls_index=None, is_impossible=None, p_mask=None,
+        self, hidden_states, start_positions=None, end_positions=None, cls_index=None, is_impossible=None, p_mask=None
    ):
        outputs = ()

@@ -1662,7 +1499,7 @@ class SQuADHead(nn.Module):
            start_states = torch.einsum("blh,bl->bh", hidden_states, start_log_probs)
            cls_logits = self.answer_class(hidden_states, start_states=start_states, cls_index=cls_index)

-            outputs = (start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits,) + outputs
+            outputs = (start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits) + outputs

        # return start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits
        # or (if labels are provided) (total_loss,)
@@ -1731,7 +1568,7 @@ class SequenceSummary(nn.Module):
            output = hidden_states.mean(dim=1)
        elif self.summary_type == "cls_index":
            if cls_index is None:
-                cls_index = torch.full_like(hidden_states[..., :1, :], hidden_states.shape[-2] - 1, dtype=torch.long,)
+                cls_index = torch.full_like(hidden_states[..., :1, :], hidden_states.shape[-2] - 1, dtype=torch.long)
            else:
                cls_index = cls_index.unsqueeze(-1).unsqueeze(-1)
                cls_index = cls_index.expand((-1,) * (cls_index.dim() - 1) + (hidden_states.size(-1),))
@@ -296,13 +296,19 @@ class Pipeline(_ScikitCompat):
    pickle format.

    Arguments:
-        model (:obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`):
-            The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
+        model (:obj:`str` or :obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`, `optional`, defaults to :obj:`None`):
+            The model that will be used by the pipeline to make predictions. This can be :obj:`None`, a string
+            checkpoint identifier or an actual pre-trained model inheriting from
            :class:`~transformers.PreTrainedModel` for PyTorch and :class:`~transformers.TFPreTrainedModel` for
            TensorFlow.
-        tokenizer (:obj:`~transformers.PreTrainedTokenizer`):
-            The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
+
+            If :obj:`None`, the default of the pipeline will be loaded.
+        tokenizer (:obj:`str` or :obj:`~transformers.PreTrainedTokenizer`, `optional`, defaults to :obj:`None`):
+            The tokenizer that will be used by the pipeline to encode data for the model. This can be :obj:`None`,
+            a string checkpoint identifier or an actual pre-trained tokenizer inheriting from
            :class:`~transformers.PreTrainedTokenizer`.
+
+            If :obj:`None`, the default of the pipeline will be loaded.
        modelcard (:obj:`str` or :class:`~transformers.ModelCard`, `optional`, defaults to :obj:`None`):
            Model card attributed to the model for this pipeline.
        framework (:obj:`str`, `optional`, defaults to :obj:`None`):
@@ -328,11 +334,12 @@ class Pipeline(_ScikitCompat):
    """

    default_input_names = None
+    task = None

    def __init__(
        self,
-        model: Union["PreTrainedModel", "TFPreTrainedModel"],
-        tokenizer: PreTrainedTokenizer,
+        model: Optional = None,
+        tokenizer: PreTrainedTokenizer = None,
        modelcard: Optional[ModelCard] = None,
        framework: Optional[str] = None,
        args_parser: ArgumentHandler = None,
@@ -343,6 +350,8 @@ class Pipeline(_ScikitCompat):
        if framework is None:
            framework = get_framework()

+        model, tokenizer = self.get_defaults(model, tokenizer, framework)
+
        self.model = model
        self.tokenizer = tokenizer
        self.modelcard = modelcard
@@ -474,6 +483,26 @@ class Pipeline(_ScikitCompat):
        else:
            return predictions.numpy()

+    def get_defaults(self, model, tokenizer, framework):
+        task_defaults = SUPPORTED_TASKS[self.task]
+        if model is None:
+            if framework == "tf":
+                model = task_defaults["tf"].from_pretrained(task_defaults["default"]["model"]["tf"])
+            elif framework == "pt":
+                model = task_defaults["pt"].from_pretrained(task_defaults["default"]["model"]["pt"])
+            else:
+                raise ValueError("Provided framework should be either 'tf' for TensorFlow or 'pt' for PyTorch.")
+
+        if tokenizer is None:
+            default_tokenizer = task_defaults["default"]["tokenizer"]
+            if isinstance(default_tokenizer, tuple):
+                # For tuple we have (tokenizer name, {kwargs})
+                tokenizer = AutoTokenizer.from_pretrained(default_tokenizer[0], **default_tokenizer[1])
+            else:
+                tokenizer = AutoTokenizer.from_pretrained(default_tokenizer)
+
+        return model, tokenizer
+

 class FeatureExtractionPipeline(Pipeline):
    """
@@ -489,13 +518,19 @@ class FeatureExtractionPipeline(Pipeline):
    `huggingface.co/models <https://huggingface.co/models>`__.

    Arguments:
-        model (:obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`):
-            The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
+        model (:obj:`str` or :obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`, `optional`, defaults to :obj:`None`):
+            The model that will be used by the pipeline to make predictions. This can be :obj:`None`, a string
+            checkpoint identifier or an actual pre-trained model inheriting from
            :class:`~transformers.PreTrainedModel` for PyTorch and :class:`~transformers.TFPreTrainedModel` for
            TensorFlow.
-        tokenizer (:obj:`~transformers.PreTrainedTokenizer`):
-            The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
+
+            If :obj:`None`, the default of the pipeline will be loaded.
+        tokenizer (:obj:`str` or :obj:`~transformers.PreTrainedTokenizer`, `optional`, defaults to :obj:`None`):
+            The tokenizer that will be used by the pipeline to encode data for the model. This can be :obj:`None`,
+            a string checkpoint identifier or an actual pre-trained tokenizer inheriting from
            :class:`~transformers.PreTrainedTokenizer`.
+
+            If :obj:`None`, the default of the pipeline will be loaded.
        modelcard (:obj:`str` or :class:`~transformers.ModelCard`, `optional`, defaults to :obj:`None`):
            Model card attributed to the model for this pipeline.
        framework (:obj:`str`, `optional`, defaults to :obj:`None`):
@@ -511,10 +546,12 @@ class FeatureExtractionPipeline(Pipeline):
            on the associated CUDA device id.
    """

+    task = "feature-extraction"
+
    def __init__(
        self,
-        model: Union["PreTrainedModel", "TFPreTrainedModel"],
-        tokenizer: PreTrainedTokenizer,
+        model: Optional = None,
+        tokenizer: PreTrainedTokenizer = None,
        modelcard: Optional[ModelCard] = None,
        framework: Optional[str] = None,
        args_parser: ArgumentHandler = None,
@@ -549,13 +586,19 @@ class TextClassificationPipeline(Pipeline):
    `huggingface.co/models <https://huggingface.co/models?search=&filter=text-classification>`__.

    Arguments:
-        model (:obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`):
-            The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
+        model (:obj:`str` or :obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`, `optional`, defaults to :obj:`None`):
+            The model that will be used by the pipeline to make predictions. This can be :obj:`None`, a string
+            checkpoint identifier or an actual pre-trained model inheriting from
            :class:`~transformers.PreTrainedModel` for PyTorch and :class:`~transformers.TFPreTrainedModel` for
            TensorFlow.
-        tokenizer (:obj:`~transformers.PreTrainedTokenizer`):
-            The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
+
+            If :obj:`None`, the default of the pipeline will be loaded.
+        tokenizer (:obj:`str` or :obj:`~transformers.PreTrainedTokenizer`, `optional`, defaults to :obj:`None`):
+            The tokenizer that will be used by the pipeline to encode data for the model. This can be :obj:`None`,
+            a string checkpoint identifier or an actual pre-trained tokenizer inheriting from
            :class:`~transformers.PreTrainedTokenizer`.
+
+            If :obj:`None`, the default of the pipeline will be loaded.
        modelcard (:obj:`str` or :class:`~transformers.ModelCard`, `optional`, defaults to :obj:`None`):
            Model card attributed to the model for this pipeline.
        framework (:obj:`str`, `optional`, defaults to :obj:`None`):
@@ -571,6 +614,8 @@ class TextClassificationPipeline(Pipeline):
            on the associated CUDA device id.
    """

+    task = "sentiment-analysis"
+
    def __call__(self, *args, **kwargs):
        outputs = super().__call__(*args, **kwargs)
        scores = np.exp(outputs) / np.exp(outputs).sum(-1)
@@ -593,13 +638,19 @@ class FillMaskPipeline(Pipeline):
    `huggingface.co/models <https://huggingface.co/models?search=&filter=lm-head>`__.

    Arguments:
-        model (:obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`):
-            The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
+        model (:obj:`str` or :obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`, `optional`, defaults to :obj:`None`):
+            The model that will be used by the pipeline to make predictions. This can be :obj:`None`, a string
+            checkpoint identifier or an actual pre-trained model inheriting from
            :class:`~transformers.PreTrainedModel` for PyTorch and :class:`~transformers.TFPreTrainedModel` for
            TensorFlow.
-        tokenizer (:obj:`~transformers.PreTrainedTokenizer`):
-            The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
+
+            If :obj:`None`, the default of the pipeline will be loaded.
+        tokenizer (:obj:`str` or :obj:`~transformers.PreTrainedTokenizer`, `optional`, defaults to :obj:`None`):
+            The tokenizer that will be used by the pipeline to encode data for the model. This can be :obj:`None`,
+            a string checkpoint identifier or an actual pre-trained tokenizer inheriting from
            :class:`~transformers.PreTrainedTokenizer`.
+
+            If :obj:`None`, the default of the pipeline will be loaded.
        modelcard (:obj:`str` or :class:`~transformers.ModelCard`, `optional`, defaults to :obj:`None`):
            Model card attributed to the model for this pipeline.
        framework (:obj:`str`, `optional`, defaults to :obj:`None`):
@@ -615,10 +666,12 @@ class FillMaskPipeline(Pipeline):
            on the associated CUDA device id.
    """

+    task = "fill-mask"
+
    def __init__(
        self,
-        model: Union["PreTrainedModel", "TFPreTrainedModel"],
-        tokenizer: PreTrainedTokenizer,
+        model: Optional = None,
+        tokenizer: PreTrainedTokenizer = None,
        modelcard: Optional[ModelCard] = None,
        framework: Optional[str] = None,
        args_parser: ArgumentHandler = None,
@@ -690,13 +743,19 @@ class NerPipeline(Pipeline):
    `huggingface.co/models <https://huggingface.co/models?search=&filter=token-classification>`__.

    Arguments:
-        model (:obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`):
-            The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
+        model (:obj:`str` or :obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`, `optional`, defaults to :obj:`None`):
+            The model that will be used by the pipeline to make predictions. This can be :obj:`None`, a string
+            checkpoint identifier or an actual pre-trained model inheriting from
            :class:`~transformers.PreTrainedModel` for PyTorch and :class:`~transformers.TFPreTrainedModel` for
            TensorFlow.
-        tokenizer (:obj:`~transformers.PreTrainedTokenizer`):
-            The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
+
+            If :obj:`None`, the default of the pipeline will be loaded.
+        tokenizer (:obj:`str` or :obj:`~transformers.PreTrainedTokenizer`, `optional`, defaults to :obj:`None`):
+            The tokenizer that will be used by the pipeline to encode data for the model. This can be :obj:`None`,
+            a string checkpoint identifier or an actual pre-trained tokenizer inheriting from
            :class:`~transformers.PreTrainedTokenizer`.
+
+            If :obj:`None`, the default of the pipeline will be loaded.
        modelcard (:obj:`str` or :class:`~transformers.ModelCard`, `optional`, defaults to :obj:`None`):
            Model card attributed to the model for this pipeline.
        framework (:obj:`str`, `optional`, defaults to :obj:`None`):
@@ -710,14 +769,19 @@ class NerPipeline(Pipeline):
        device (:obj:`int`, `optional`, defaults to :obj:`-1`):
            Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, >=0 will run the model
            on the associated CUDA device id.
+
+    Example::
+
+        from transformers import pi
    """

    default_input_names = "sequences"
+    task = "ner"

    def __init__(
        self,
-        model: Union["PreTrainedModel", "TFPreTrainedModel"],
-        tokenizer: PreTrainedTokenizer,
+        model: Optional = None,
+        tokenizer: PreTrainedTokenizer = None,
        modelcard: Optional[ModelCard] = None,
        framework: Optional[str] = None,
        args_parser: ArgumentHandler = None,
@@ -864,13 +928,19 @@ class QuestionAnsweringPipeline(Pipeline):
    `huggingface.co/models <https://huggingface.co/models?search=&filter=question-answering>`__.

    Arguments:
-        model (:obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`):
-            The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
+        model (:obj:`str` or :obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`, `optional`, defaults to :obj:`None`):
+            The model that will be used by the pipeline to make predictions. This can be :obj:`None`, a string
+            checkpoint identifier or an actual pre-trained model inheriting from
            :class:`~transformers.PreTrainedModel` for PyTorch and :class:`~transformers.TFPreTrainedModel` for
            TensorFlow.
-        tokenizer (:obj:`~transformers.PreTrainedTokenizer`):
-            The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
+
+            If :obj:`None`, the default of the pipeline will be loaded.
+        tokenizer (:obj:`str` or :obj:`~transformers.PreTrainedTokenizer`, `optional`, defaults to :obj:`None`):
+            The tokenizer that will be used by the pipeline to encode data for the model. This can be :obj:`None`,
+            a string checkpoint identifier or an actual pre-trained tokenizer inheriting from
            :class:`~transformers.PreTrainedTokenizer`.
+
+            If :obj:`None`, the default of the pipeline will be loaded.
        modelcard (:obj:`str` or :class:`~transformers.ModelCard`, `optional`, defaults to :obj:`None`):
            Model card attributed to the model for this pipeline.
        framework (:obj:`str`, `optional`, defaults to :obj:`None`):
@@ -887,11 +957,12 @@ class QuestionAnsweringPipeline(Pipeline):
    """

    default_input_names = "question,context"
+    task = "question-answering"

    def __init__(
        self,
-        model: Union["PreTrainedModel", "TFPreTrainedModel"],
-        tokenizer: PreTrainedTokenizer,
+        model: Optional = None,
+        tokenizer: Optional[PreTrainedTokenizer] = None,
        modelcard: Optional[ModelCard] = None,
        framework: Optional[str] = None,
        device: int = -1,
@@ -69,7 +69,6 @@ class DistilBertTokenizer(BertTokenizer):
    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
-    model_input_names = ["attention_mask"]


 class DistilBertTokenizerFast(BertTokenizerFast):
@@ -77,4 +76,3 @@ class DistilBertTokenizerFast(BertTokenizerFast):
    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
-    model_input_names = ["attention_mask"]
@@ -119,7 +119,6 @@ class RobertaTokenizer(GPT2Tokenizer):
    vocab_files_names = VOCAB_FILES_NAMES
    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["attention_mask"]

    def __init__(
        self,
@@ -245,7 +244,6 @@ class RobertaTokenizerFast(GPT2TokenizerFast):
    vocab_files_names = VOCAB_FILES_NAMES
    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["attention_mask"]

    def __init__(
        self,
@@ -22,7 +22,6 @@ import os
 import re
 from collections import defaultdict
 from contextlib import contextmanager
-from typing import List, Optional, Tuple, Union

 from tokenizers.implementations import BaseTokenizer

@@ -139,7 +138,6 @@ class PreTrainedTokenizer(object):
    pretrained_vocab_files_map = {}
    pretrained_init_configuration = {}
    max_model_input_sizes = {}
-    model_input_names = ["token_type_ids", "attention_mask"]

    SPECIAL_TOKENS_ATTRIBUTES = [
        "bos_token",
@@ -318,7 +316,6 @@ class PreTrainedTokenizer(object):

        # Padding side is right by default and over-riden in subclasses. If specified in the kwargs, it is changed.
        self.padding_side = kwargs.pop("padding_side", self.padding_side)
-        self.model_input_names = kwargs.pop("model_input_names", self.model_input_names)

        # Added tokens
        self.added_tokens_encoder = {}
@@ -852,14 +849,14 @@ class PreTrainedTokenizer(object):

    def encode(
        self,
-        text: str,
-        text_pair: Optional[str] = None,
-        add_special_tokens: bool = True,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        truncation_strategy: str = "longest_first",
-        pad_to_max_length: bool = False,
-        return_tensors: Optional[str] = None,
+        text,
+        text_pair=None,
+        add_special_tokens=True,
+        max_length=None,
+        stride=0,
+        truncation_strategy="longest_first",
+        pad_to_max_length=False,
+        return_tensors=None,
        **kwargs
    ):
        """
@@ -868,43 +865,34 @@ class PreTrainedTokenizer(object):
        Same as doing ``self.convert_tokens_to_ids(self.tokenize(text))``.

        Args:
-            text (:obj:`str` or :obj:`List[str]`):
-                The first sequence to be encoded. This can be a string, a list of strings (tokenized string using
+            text: The first sequence to be encoded. This can be a string, a list of strings (tokenized string using
                the `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids`
                method)
-            text_pair (:obj:`str` or :obj:`List[str]`, `optional`, defaults to :obj:`None`):
-                Optional second sequence to be encoded. This can be a string, a list of strings (tokenized
+            text_pair: Optional second sequence to be encoded. This can be a string, a list of strings (tokenized
                string using the `tokenize` method) or a list of integers (tokenized string ids using the
                `convert_tokens_to_ids` method)
-            add_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`True`):
-                If set to ``True``, the sequences will be encoded with the special tokens relative
+            add_special_tokens: if set to ``True``, the sequences will be encoded with the special tokens relative
                to their model.
-            max_length (:obj:`int`, `optional`, defaults to :obj:`None`):
-                If set to a number, will limit the total sequence returned so that it has a maximum length.
+            max_length: if set to a number, will limit the total sequence returned so that it has a maximum length.
                If there are overflowing tokens, those will be added to the returned dictionary
-            stride (:obj:`int`, `optional`, defaults to ``0``):
-                If set to a number along with max_length, the overflowing tokens returned will contain some tokens
+            stride: if set to a number along with max_length, the overflowing tokens returned will contain some tokens
                from the main sequence returned. The value of this argument defines the number of additional tokens.
-            truncation_strategy (:obj:`str`, `optional`, defaults to `longest_first`):
-                String selected in the following options:
-
+            truncation_strategy: string selected in the following options:
                - 'longest_first' (default) Iteratively reduce the inputs sequence until the input is under max_length
-                  starting from the longest one at each token (when there is a pair of input sequences)
+                    starting from the longest one at each token (when there is a pair of input sequences)
                - 'only_first': Only truncate the first sequence
                - 'only_second': Only truncate the second sequence
                - 'do_not_truncate': Does not truncate (raise an error if the input sequence is longer than max_length)
-            pad_to_max_length (:obj:`bool`, `optional`, defaults to :obj:`False`):
-                If set to True, the returned sequences will be padded according to the model's padding side and
-                padding index, up to their max length. If no max length is specified, the padding is done up to the
-                model's max length. The tokenizer padding sides are handled by the class attribute `padding_side`
-                which can be set to the following strings:
-
+            pad_to_max_length: if set to True, the returned sequences will be padded according to the model's padding side and
+                padding index, up to their max length. If no max length is specified, the padding is done up to the model's max length.
+                The tokenizer padding sides are handled by the class attribute `padding_side` which can be set to the following strings:
                - 'left': pads on the left of the sequences
                - 'right': pads on the right of the sequences
                Defaults to False: no padding.
-            return_tensors (:obj:`str`, `optional`, defaults to :obj:`None`):
-                Can be set to 'tf' or 'pt' to return respectively TensorFlow :obj:`tf.constant`
-                or PyTorch :obj:`torch.Tensor` instead of a list of python integers.
+            return_tensors: (optional) can be set to 'tf' or 'pt' to return respectively TensorFlow tf.constant
+                or PyTorch torch.Tensor instead of a list of python integers.
+            add_prefix_space: Only applies to GPT-2 and RoBERTa tokenizers. When `True`, this ensures that the sequence
+                begins with an empty space. False by default except for when using RoBERTa with `add_special_tokens=True`.
            **kwargs: passed to the `self.tokenize()` method
        """
        encoded_inputs = self.encode_plus(
@@ -923,79 +911,59 @@ class PreTrainedTokenizer(object):

    def encode_plus(
        self,
-        text: str,
-        text_pair: Optional[str] = None,
-        add_special_tokens: bool = True,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        truncation_strategy: str = "longest_first",
-        pad_to_max_length: bool = False,
-        return_tensors: Optional[str] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
+        text,
+        text_pair=None,
+        add_special_tokens=True,
+        max_length=None,
+        stride=0,
+        truncation_strategy="longest_first",
+        pad_to_max_length=False,
+        return_tensors=None,
+        return_token_type_ids=True,
+        return_attention_mask=True,
+        return_overflowing_tokens=False,
+        return_special_tokens_mask=False,
+        return_offsets_mapping=False,
        **kwargs
    ):
        """
-        Returns a dictionary containing the encoded sequence or sequence pair and additional information:
+        Returns a dictionary containing the encoded sequence or sequence pair and additional informations:
        the mask for sequence classification and the overflowing elements if a ``max_length`` is specified.

        Args:
-            text (:obj:`str` or :obj:`List[str]`):
-                The first sequence to be encoded. This can be a string, a list of strings (tokenized string using
+            text: The first sequence to be encoded. This can be a string, a list of strings (tokenized string using
                the `tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids`
                method)
-            text_pair (:obj:`str` or :obj:`List[str]`, `optional`, defaults to :obj:`None`):
-                Optional second sequence to be encoded. This can be a string, a list of strings (tokenized
+            text_pair: Optional second sequence to be encoded. This can be a string, a list of strings (tokenized
                string using the `tokenize` method) or a list of integers (tokenized string ids using the
                `convert_tokens_to_ids` method)
-            add_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`True`):
-                If set to ``True``, the sequences will be encoded with the special tokens relative
+            add_special_tokens: if set to ``True``, the sequences will be encoded with the special tokens relative
                to their model.
-            max_length (:obj:`int`, `optional`, defaults to :obj:`None`):
-                If set to a number, will limit the total sequence returned so that it has a maximum length.
+            max_length: if set to a number, will limit the total sequence returned so that it has a maximum length.
                If there are overflowing tokens, those will be added to the returned dictionary
-            stride (:obj:`int`, `optional`, defaults to ``0``):
-                If set to a number along with max_length, the overflowing tokens returned will contain some tokens
+            stride: if set to a number along with max_length, the overflowing tokens returned will contain some tokens
                from the main sequence returned. The value of this argument defines the number of additional tokens.
-            truncation_strategy (:obj:`str`, `optional`, defaults to `longest_first`):
-                String selected in the following options:
-
+            truncation_strategy: string selected in the following options:
                - 'longest_first' (default) Iteratively reduce the inputs sequence until the input is under max_length
-                  starting from the longest one at each token (when there is a pair of input sequences)
+                    starting from the longest one at each token (when there is a pair of input sequences)
                - 'only_first': Only truncate the first sequence
                - 'only_second': Only truncate the second sequence
                - 'do_not_truncate': Does not truncate (raise an error if the input sequence is longer than max_length)
-            pad_to_max_length (:obj:`bool`, `optional`, defaults to :obj:`False`):
-                If set to True, the returned sequences will be padded according to the model's padding side and
-                padding index, up to their max length. If no max length is specified, the padding is done up to the
-                model's max length. The tokenizer padding sides are handled by the class attribute `padding_side`
-                which can be set to the following strings:
-
+            pad_to_max_length: if set to True, the returned sequences will be padded according to the model's padding side and
+                padding index, up to their max length. If no max length is specified, the padding is done up to the model's max length.
+                The tokenizer padding sides are handled by the class attribute `padding_side` which can be set to the following strings:
                - 'left': pads on the left of the sequences
                - 'right': pads on the right of the sequences
                Defaults to False: no padding.
-            return_tensors (:obj:`str`, `optional`, defaults to :obj:`None`):
-                Can be set to 'tf' or 'pt' to return respectively TensorFlow :obj:`tf.constant`
-                or PyTorch :obj:`torch.Tensor` instead of a list of python integers.
-            return_token_type_ids (:obj:`bool`, `optional`, defaults to :obj:`None`):
-                Whether to return token type IDs. If left to the default, will return the token type IDs according
-                to the specific tokenizer's default, defined by the :obj:`return_outputs` attribute.
-
-                `What are token type IDs? <../glossary.html#token-type-ids>`_
-            return_attention_mask (:obj:`bool`, `optional`, defaults to :obj:`none`):
-                Whether to return the attention mask. If left to the default, will return the attention mask according
-                to the specific tokenizer's default, defined by the :obj:`return_outputs` attribute.
-
-                `What are attention masks? <../glossary.html#attention-mask>`__
-            return_overflowing_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
-                Set to True to return overflowing token information (default False).
-            return_special_tokens_mask (:obj:`bool`, `optional`, defaults to :obj:`False`):
-                Set to True to return special tokens mask information (default False).
-            return_offsets_mapping (:obj:`bool`, `optional`, defaults to :obj:`False`):
-                Set to True to return (char_start, char_end) for each token (default False).
+            return_tensors: (optional) can be set to 'tf' or 'pt' to return respectively TensorFlow tf.constant
+                or PyTorch torch.Tensor instead of a list of python integers.
+            add_prefix_space: Only applies to GPT-2 and RoBERTa tokenizers. When `True`, this ensures that the sequence
+                begins with an empty space. False by default except for when using RoBERTa with `add_special_tokens=True`.
+            return_token_type_ids: (optional) Set to False to avoid returning token_type_ids (default True).
+            return_attention_mask: (optional) Set to False to avoid returning attention mask (default True)
+            return_overflowing_tokens: (optional) Set to True to return overflowing token information (default False).
+            return_special_tokens_mask: (optional) Set to True to return special tokens mask information (default False).
+            return_offsets_mapping: (optional) Set to True to return (char_start, char_end) for each token (default False).
                If using Python's tokenizer, this method will raise NotImplementedError. This one is only available on
                Rust-based tokenizers inheriting from PreTrainedTokenizerFast.
            **kwargs: passed to the `self.tokenize()` method
@@ -1013,14 +981,13 @@ class PreTrainedTokenizer(object):
                }

            With the fields:
-
-            - ``input_ids``: list of token ids to be fed to a model
-            - ``token_type_ids``: list of token type ids to be fed to a model
-            - ``attention_mask``: list of indices specifying which tokens should be attended to by the model
-            - ``overflowing_tokens``: list of overflowing tokens if a max length is specified.
-            - ``num_truncated_tokens``: number of overflowing tokens a ``max_length`` is specified
-            - ``special_tokens_mask``: if adding special tokens, this is a list of [0, 1], with 0 specifying special added
-              tokens and 1 specifying sequence tokens.
+                ``input_ids``: list of token ids to be fed to a model
+                ``token_type_ids``: list of token type ids to be fed to a model
+                ``attention_mask``: list of indices specifying which tokens should be attended to by the model
+                ``overflowing_tokens``: list of overflowing tokens if a max length is specified.
+                ``num_truncated_tokens``: number of overflowing tokens a ``max_length`` is specified
+                ``special_tokens_mask``: if adding special tokens, this is a list of [0, 1], with 0 specifying special added
+                tokens and 1 specifying sequence tokens.
        """

        def get_input_ids(text):
@@ -1071,19 +1038,19 @@ class PreTrainedTokenizer(object):

    def batch_encode_plus(
        self,
-        batch_text_or_text_pairs: Union[str, List[str]],
-        add_special_tokens: bool = True,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        truncation_strategy: str = "longest_first",
-        pad_to_max_length: bool = False,
-        return_tensors: Optional[str] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_masks: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_masks: bool = False,
-        return_offsets_mapping: bool = False,
-        return_input_lengths: bool = False,
+        batch_text_or_text_pairs=None,
+        add_special_tokens=True,
+        max_length=None,
+        stride=0,
+        truncation_strategy="longest_first",
+        pad_to_max_length=False,
+        return_tensors=None,
+        return_token_type_ids=True,
+        return_attention_masks=True,
+        return_overflowing_tokens=False,
+        return_special_tokens_masks=False,
+        return_offsets_mapping=False,
+        return_input_lengths=False,
        **kwargs
    ):
        """
@@ -1091,59 +1058,32 @@ class PreTrainedTokenizer(object):
        the mask for sequence classification and the overflowing elements if a ``max_length`` is specified.

        Args:
-            batch_text_or_text_pairs (:obj:`List[str]` or :obj:`List[List[str]]`):
-                Batch of sequences or pair of sequences to be encoded.
+            batch_text_or_text_pairs: Batch of sequences or pair of sequences to be encoded.
                This can be a list of string/string-sequences/int-sequences or a list of pair of
                string/string-sequences/int-sequence (see details in encode_plus)
-            add_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`True`):
-                If set to ``True``, the sequences will be encoded with the special tokens relative
+            add_special_tokens: if set to ``True``, the sequences will be encoded with the special tokens relative
                to their model.
-            max_length (:obj:`int`, `optional`, defaults to :obj:`None`):
-                If set to a number, will limit the total sequence returned so that it has a maximum length.
-                If there are overflowing tokens, those will be added to the returned dictionary
-            stride (:obj:`int`, `optional`, defaults to ``0``):
-                If set to a number along with max_length, the overflowing tokens returned will contain some tokens
+            max_length: if set to a number, will limit the total sequence returned so that it has a maximum length.
+                If there are overflowing tokens, those will be added to the returned dictionary`
+            stride: if set to a number along with max_length, the overflowing tokens returned will contain some tokens
                from the main sequence returned. The value of this argument defines the number of additional tokens.
-            truncation_strategy (:obj:`str`, `optional`, defaults to `longest_first`):
-                String selected in the following options:
-
+            truncation_strategy: string selected in the following options:
                - 'longest_first' (default) Iteratively reduce the inputs sequence until the input is under max_length
-                  starting from the longest one at each token (when there is a pair of input sequences)
+                    starting from the longest one at each token (when there is a pair of input sequences)
                - 'only_first': Only truncate the first sequence
                - 'only_second': Only truncate the second sequence
                - 'do_not_truncate': Does not truncate (raise an error if the input sequence is longer than max_length)
-            pad_to_max_length (:obj:`bool`, `optional`, defaults to :obj:`False`):
-                If set to True, the returned sequences will be padded according to the model's padding side and
-                padding index, up to their max length. If no max length is specified, the padding is done up to the
-                model's max length. The tokenizer padding sides are handled by the class attribute `padding_side`
-                which can be set to the following strings:
-
+            pad_to_max_length: if set to True, the returned sequences will be padded according to the model's padding side and
+                padding index, up to their max length. If no max length is specified, the padding is done up to the model's max length.
+                The tokenizer padding sides are handled by the class attribute `padding_side` which can be set to the following strings:
                - 'left': pads on the left of the sequences
                - 'right': pads on the right of the sequences
                Defaults to False: no padding.
-            return_tensors (:obj:`str`, `optional`, defaults to :obj:`None`):
-                Can be set to 'tf' or 'pt' to return respectively TensorFlow :obj:`tf.constant`
-                or PyTorch :obj:`torch.Tensor` instead of a list of python integers.
-            return_token_type_ids (:obj:`bool`, `optional`, defaults to :obj:`None`):
-                Whether to return token type IDs. If left to the default, will return the token type IDs according
-                to the specific tokenizer's default, defined by the :obj:`return_outputs` attribute.
-
-                `What are token type IDs? <../glossary.html#token-type-ids>`_
-            return_attention_masks (:obj:`bool`, `optional`, defaults to :obj:`none`):
-                Whether to return the attention mask. If left to the default, will return the attention mask according
-                to the specific tokenizer's default, defined by the :obj:`return_outputs` attribute.
-
-                `What are attention masks? <../glossary.html#attention-mask>`__
-            return_overflowing_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
-                Set to True to return overflowing token information (default False).
-            return_special_tokens_masks (:obj:`bool`, `optional`, defaults to :obj:`False`):
-                Set to True to return special tokens mask information (default False).
-            return_offsets_mapping (:obj:`bool`, `optional`, defaults to :obj:`False`):
-                Set to True to return (char_start, char_end) for each token (default False).
-                If using Python's tokenizer, this method will raise NotImplementedError. This one is only available on
-                Rust-based tokenizers inheriting from PreTrainedTokenizerFast.
-            return_input_lengths (:obj:`bool`, `optional`, defaults to :obj:`False`):
-                If set the resulting dictionary will include the length of each sample
+            return_tensors: (optional) can be set to 'tf' or 'pt' to return respectively TensorFlow tf.constant
+                or PyTorch torch.Tensor instead of a list of python integers.
+            return_input_lengths: (optional) If set the resulting dictionary will include the length of each sample
+            return_attention_masks: (optional) Set to True to return the attention mask (default False)
+            return_offsets_mapping: (optional) Not available, should be set to False or it will throw NotImplementError
            **kwargs: passed to the `self.tokenize()` method

        Return:
@@ -1159,14 +1099,13 @@ class PreTrainedTokenizer(object):
                }

            With the fields:
-
-            - ``input_ids``: list of token ids to be fed to a model
-            - ``token_type_ids``: list of token type ids to be fed to a model
-            - ``attention_mask``: list of indices specifying which tokens should be attended to by the model
-            - ``overflowing_tokens``: list of overflowing tokens if a max length is specified.
-            - ``num_truncated_tokens``: number of overflowing tokens a ``max_length`` is specified
-            - ``special_tokens_mask``: if adding special tokens, this is a list of [0, 1], with 0 specifying special added
-              tokens and 1 specifying sequence tokens.
+                ``input_ids``: list of token ids to be fed to a model
+                ``token_type_ids``: list of token type ids to be fed to a model
+                ``attention_mask``: list of indices specifying which tokens should be attended to by the model
+                ``overflowing_tokens``: list of overflowing tokens if a max length is specified.
+                ``num_truncated_tokens``: number of overflowing tokens a ``max_length`` is specified
+                ``special_tokens_mask``: if adding special tokens, this is a list of [0, 1], with 0 specifying special added
+                tokens and 1 specifying sequence tokens.
        """

        def get_input_ids(text):
@@ -1281,18 +1220,18 @@ class PreTrainedTokenizer(object):

    def prepare_for_model(
        self,
-        ids: List[int],
-        pair_ids: Optional[List[int]] = None,
-        max_length: Optional[int] = None,
-        add_special_tokens: bool = True,
-        stride: int = 0,
-        truncation_strategy: str = "longest_first",
-        pad_to_max_length: bool = False,
-        return_tensors: Optional[str] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
+        ids,
+        pair_ids=None,
+        max_length=None,
+        add_special_tokens=True,
+        stride=0,
+        truncation_strategy="longest_first",
+        pad_to_max_length=False,
+        return_tensors=None,
+        return_token_type_ids=True,
+        return_attention_mask=True,
+        return_overflowing_tokens=False,
+        return_special_tokens_mask=False,
    ):
        """
        Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model.
@@ -1353,11 +1292,6 @@ class PreTrainedTokenizer(object):
        len_ids = len(ids)
        len_pair_ids = len(pair_ids) if pair else 0

-        if return_token_type_ids is None:
-            return_token_type_ids = "token_type_ids" in self.model_input_names
-        if return_attention_mask is None:
-            return_attention_mask = "attention_mask" in self.model_input_names
-
        encoded_inputs = {}

        # Handle max sequence length
@@ -1683,9 +1617,6 @@ class PreTrainedTokenizer(object):


 class PreTrainedTokenizerFast(PreTrainedTokenizer):
-
-    model_input_names = ["token_type_ids", "attention_mask"]
-
    def __init__(self, tokenizer: BaseTokenizer, **kwargs):
        if tokenizer is None:
            raise ValueError("Provided tokenizer cannot be None")
@@ -1754,21 +1685,16 @@ class PreTrainedTokenizerFast(PreTrainedTokenizer):
        if self._tokenizer is not None:
            self._tokenizer.add_special_tokens(self.all_special_tokens)

+    @staticmethod
    def _convert_encoding(
-        self,
        encoding,
        return_tensors=None,
-        return_token_type_ids=None,
-        return_attention_mask=None,
+        return_token_type_ids=True,
+        return_attention_mask=True,
        return_overflowing_tokens=False,
        return_special_tokens_mask=False,
        return_offsets_mapping=False,
    ):
-        if return_token_type_ids is None:
-            return_token_type_ids = "token_type_ids" in self.model_input_names
-        if return_attention_mask is None:
-            return_attention_mask = "attention_mask" in self.model_input_names
-
        if return_overflowing_tokens and encoding.overflowing is not None:
            encodings = [encoding] + encoding.overflowing
        else:
@@ -1848,18 +1774,18 @@ class PreTrainedTokenizerFast(PreTrainedTokenizer):

    def batch_encode_plus(
        self,
-        batch_text_or_text_pairs: Optional[Union[List[str], List[Tuple[str]]]] = None,
-        add_special_tokens: bool = True,
-        max_length: Optional[int] = None,
-        stride: int = 0,
-        truncation_strategy: str = "longest_first",
-        pad_to_max_length: bool = False,
-        return_tensors: Optional[str] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
+        batch_text_or_text_pairs=None,
+        add_special_tokens=True,
+        max_length=None,
+        stride=0,
+        truncation_strategy="longest_first",
+        pad_to_max_length=False,
+        return_tensors=None,
+        return_token_type_ids=True,
+        return_attention_mask=True,
+        return_overflowing_tokens=False,
+        return_special_tokens_mask=False,
+        return_offsets_mapping=False,
        **kwargs
    ):
        if not add_special_tokens:
@@ -1926,8 +1852,8 @@ class PreTrainedTokenizerFast(PreTrainedTokenizer):
                stack = tf.stack(stack, axis=0)
            elif return_tensors == "pt":
                stack = torch.stack(stack, dim=0)
-            elif not return_tensors and len(stack) == 1:
-                stack = stack[0]
+            # elif not return_tensors and len(stack) == 1:
+            #     stack = stack[0]

            sanitized[key] = stack

@@ -1942,19 +1868,19 @@ class PreTrainedTokenizerFast(PreTrainedTokenizer):

    def encode_plus(
        self,
-        text: str,
-        text_pair: Optional[str] = None,
-        add_special_tokens: bool = False,
-        max_length: Optional[int] = None,
-        pad_to_max_length: bool = False,
-        stride: int = 0,
-        truncation_strategy: str = "longest_first",
-        return_tensors: Optional[bool] = None,
-        return_token_type_ids: Optional[bool] = None,
-        return_attention_mask: Optional[bool] = None,
-        return_overflowing_tokens: bool = False,
-        return_special_tokens_mask: bool = False,
-        return_offsets_mapping: bool = False,
+        text,
+        text_pair=None,
+        add_special_tokens=False,
+        max_length=None,
+        pad_to_max_length=False,
+        stride=0,
+        truncation_strategy="longest_first",
+        return_tensors=None,
+        return_token_type_ids=True,
+        return_attention_mask=True,
+        return_overflowing_tokens=False,
+        return_special_tokens_mask=False,
+        return_offsets_mapping=False,
        **kwargs
    ):
        batched_input = [(text, text_pair)] if text_pair else [text]
@@ -1976,7 +1902,10 @@ class PreTrainedTokenizerFast(PreTrainedTokenizer):

        # Return tensor is None, then we can remove the leading batch axis
        if not return_tensors:
-            return {key: value[0] if isinstance(value[0], list) else value for key, value in batched_output.items()}
+            return {
+                key: value[0] if len(value) > 0 and isinstance(value[0], list) else value
+                for key, value in batched_output.items()
+            }
        else:
            return batched_output

@@ -21,7 +21,7 @@ import unittest
 import requests
 from requests.exceptions import HTTPError

-from transformers.hf_api import HfApi, HfFolder, ModelInfo, PresignedUrl, S3Obj
+from transformers.hf_api import HfApi, HfFolder, PresignedUrl, S3Obj


 USER = "__DUMMY_TRANSFORMERS_USER__"
@@ -36,11 +36,10 @@ FILES = [
        os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures/empty.txt"),
    ),
 ]
-ENDPOINT_STAGING = "https://moon-staging.huggingface.co"


 class HfApiCommonTest(unittest.TestCase):
-    _api = HfApi(endpoint=ENDPOINT_STAGING)
+    _api = HfApi(endpoint="https://moon-staging.huggingface.co")


 class HfApiLoginTest(HfApiCommonTest):
@@ -67,17 +66,8 @@ class HfApiEndpointsTest(HfApiCommonTest):
            cls._api.delete_obj(token=cls._token, filename=FILE_KEY)

    def test_whoami(self):
-        user, orgs = self._api.whoami(token=self._token)
+        user = self._api.whoami(token=self._token)
        self.assertEqual(user, USER)
-        self.assertIsInstance(orgs, list)
-
-    def test_presign_invalid_org(self):
-        with self.assertRaises(HTTPError):
-            _ = self._api.presign(token=self._token, filename="fake_org.txt", organization="fake")
-
-    def test_presign_valid_org(self):
-        urls = self._api.presign(token=self._token, filename="valid_org.txt", organization="valid_org")
-        self.assertIsInstance(urls, PresignedUrl)

    def test_presign(self):
        for FILE_KEY, FILE_PATH in FILES:
@@ -102,18 +92,6 @@ class HfApiEndpointsTest(HfApiCommonTest):
            self.assertIsInstance(o, S3Obj)


-class HfApiPublicTest(unittest.TestCase):
-    def test_staging_model_list(self):
-        _api = HfApi(endpoint=ENDPOINT_STAGING)
-        _ = _api.model_list()
-
-    def test_model_list(self):
-        _api = HfApi()
-        models = _api.model_list()
-        self.assertGreater(len(models), 100)
-        self.assertIsInstance(models[0], ModelInfo)
-
-
 class HfFolderTest(unittest.TestCase):
    def test_token_workflow(self):
        """
@@ -37,7 +37,6 @@ if is_torch_available():
        BART_PRETRAINED_MODEL_ARCHIVE_MAP,
        shift_tokens_right,
        _prepare_bart_decoder_inputs,
-        LARGE_NEGATIVE,
    )
    from transformers.tokenization_bart import BartTokenizer

@@ -304,38 +303,6 @@ class BartHeadTests(unittest.TestCase):
        lm_model = BartForConditionalGeneration(config).eval().to(torch_device).half()
        lm_model.generate(input_ids, attention_mask)

-    def test_prepare_bart_decoder_inputs(self):
-        config, *_ = self._get_config_and_data(output_past=False)
-        input_ids = _long_tensor(([4, 4, 2]))  # only used for .device if decoder_input_ids is passed
-        decoder_input_ids = _long_tensor([[26388, 2, config.pad_token_id]])
-        ignore = LARGE_NEGATIVE
-        decoder_input_ids, decoder_attn_mask = _prepare_bart_decoder_inputs(config, input_ids, decoder_input_ids)
-        expected_mask = torch.tensor(
-            [
-                [0, ignore, ignore],
-                [0, 0, ignore],
-                [ignore, ignore, ignore],  # never attend to the final token, because its pad
-            ]
-        ).to(input_ids.device)
-        self.assertEqual(decoder_attn_mask.size(), (1, 1, 3, 3))
-        self.assertTrue(torch.eq(expected_mask, decoder_attn_mask).all())
-
-        # Test no causal mask
-        config, *_ = self._get_config_and_data(output_past=True)
-        expected_just_padding_mask = torch.tensor(
-            [[0, 0, 0], [0, 0, 0], [ignore, ignore, ignore]]  # never attend to the final token, because its pad
-        ).to(input_ids.device)
-        _, decoder_attn_mask_no_causal_mask = _prepare_bart_decoder_inputs(config, input_ids, decoder_input_ids)
-        self.assertEqual(decoder_attn_mask_no_causal_mask.size(), (1, 1, 3, 3))
-        self.assertTrue(torch.eq(expected_just_padding_mask, decoder_attn_mask_no_causal_mask).all())
-
-        decoder_input_ids = _long_tensor([[0, 26388, 4133, 2]])
-        # Attend to everything if no pad tokens and no causal mask
-        _, decoder_attn_mask_no_padding_no_causal_mask = _prepare_bart_decoder_inputs(
-            config, input_ids, decoder_input_ids
-        )
-        self.assertTrue(torch.eq(decoder_attn_mask_no_padding_no_causal_mask, 0).all())
-

 def _assert_tensors_equal(a, b, atol=1e-12, prefix=""):
    """If tensors not close, or a and b arent both tensors, raise a nice Assertion error."""
@@ -460,30 +427,3 @@ class BartModelIntegrationTest(unittest.TestCase):
        )
        # TODO(SS): run fairseq again with num_beams=2, min_len=20.
        # TODO(SS): add test case that hits max_length
-
-    @slow
-    def test_cnn_rouge_regression(self):
-        ARTICLE_9 = ' (CNN)If you\'ve been following the news lately, there are certain things you doubtless know about Mohammad Javad Zarif. He is, of course, the Iranian foreign minister. He has been U.S. Secretary of State John Kerry\'s opposite number in securing a breakthrough in nuclear discussions that could lead to an end to sanctions against Iran -- if the details can be worked out in the coming weeks. And he received a hero\'s welcome as he arrived in Iran on a sunny Friday morning. "Long live Zarif," crowds chanted as his car rolled slowly down the packed street. You may well have read that he is "polished" and, unusually for one burdened with such weighty issues, "jovial." An Internet search for "Mohammad Javad Zarif" and "jovial" yields thousands of results. He certainly has gone a long way to bring Iran in from the cold and allow it to rejoin the international community. But there are some facts about Zarif that are less well-known. Here are six: . In September 2013, Zarif tweeted "Happy Rosh Hashanah," referring to the Jewish New Year. That prompted Christine Pelosi, the daughter of House Minority Leader Nancy Pelosi, to respond with a tweet of her own: "Thanks. The New Year would be even sweeter if you would end Iran\'s Holocaust denial, sir." And, perhaps to her surprise, Pelosi got a response. "Iran never denied it," Zarif tweeted back. "The man who was perceived to be denying it is now gone. Happy New Year." The reference was likely to former Iranian President Mahmoud Ahmadinejad, who had left office the previous month. Zarif was nominated to be foreign minister by Ahmadinejad\'s successor, Hassan Rouhami. His foreign ministry notes, perhaps defensively, that "due to the political and security conditions of the time, he decided to continue his education in the United States." That is another way of saying that he was outside the country during the demonstrations against the Shah of Iran, which began in 1977, and during the Iranian Revolution, which drove the shah from power in 1979. Zarif left the country in 1977, received his undergraduate degree from San Francisco State University in 1981, his master\'s in international relations from the University of Denver in 1984 and his doctorate from the University of Denver in 1988. Both of his children were born in the United States. The website of the Iranian Foreign Ministry, which Zarif runs, cannot even agree with itself on when he was born. The first sentence of his official biography, perhaps in a nod to the powers that be in Tehran, says Zarif was "born to a religious traditional family in Tehran in 1959." Later on the same page, however, his date of birth is listed as January 8, 1960. And the Iranian Diplomacy website says he was born in in 1961 . So he is 54, 55 or maybe even 56. Whichever, he is still considerably younger than his opposite number, Kerry, who is 71. The feds investigated him over his alleged role in controlling the Alavi Foundation, a charitable organization. The U.S. Justice Department said the organization was secretly run on behalf of the Iranian government to launder money and get around U.S. sanctions. But last year, a settlement in the case, under which the foundation agreed to give a 36-story building in Manhattan along with other properties to the U.S. government, did not mention Zarif\'s name. Early in the Iranian Revolution, Zarif was among the students who took over the Iranian Consulate in San Francisco. The aim, says the website Iranian.com -- which cites Zarif\'s memoirs, titled "Mr. Ambassador" -- was to expel from the consulate people who were not sufficiently Islamic. Later, the website says, Zarif went to make a similar protest at the Iranian mission to the United Nations. In response, the Iranian ambassador to the United Nations offered him a job. In fact, he has now spent more time with Kerry than any other foreign minister in the world. And that amount of quality time will only increase as the two men, with help from other foreign ministers as well, try to meet a June 30 deadline for nailing down the details of the agreement they managed to outline this week in Switzerland.'
-        EXPECTED_SUMMARY_9 = """Mohammad Javad Zarif is the Iranian foreign minister. He has been John Kerry's opposite number in securing a breakthrough in nuclear discussions. He received a hero's welcome as he arrived in Iran on a sunny Friday morning. But there are some facts about Zarif that are less well-known."""
-        hf = BartForConditionalGeneration.from_pretrained("bart-large-cnn", output_past=True,).to(torch_device)
-        tok = BartTokenizer.from_pretrained("bart-large")
-        dct = tok.batch_encode_plus([ARTICLE_9], max_length=1024, pad_to_max_length=True, return_tensors="pt",)
-
-        self.assertEqual(1024, dct["input_ids"].shape[1])
-        hypotheses_batch = hf.generate(
-            input_ids=dct["input_ids"].to(torch_device),
-            attention_mask=dct["attention_mask"].to(torch_device),
-            num_beams=4,
-            length_penalty=2.0,
-            max_length=140,
-            min_len=55,
-            no_repeat_ngram_size=3,
-            # do_sample=False,
-            # early_stopping=True,
-        )
-
-        decoded = [
-            tok.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in hypotheses_batch
-        ]
-
-        self.assertEqual(decoded[0], EXPECTED_SUMMARY_9)
@@ -628,9 +628,6 @@ class ModelTesterMixin:
            "input_ids", None
        )  # TODO (PVP): ugly workaround to make code work for t5 for the moment - has to changed when t5 is fixed.

-        if self.is_encoder_decoder:
-            config.output_past = True  # needed for Bart TODO: might have to update for other encoder-decoder models
-
        for model_class in self.all_generative_model_classes:
            model = model_class(config)
            model.to(torch_device)
@@ -219,31 +219,30 @@ class CTRLModelLanguageGenerationTest(unittest.TestCase):
    @slow
    def test_lm_generate_ctrl(self):
        model = CTRLLMHeadModel.from_pretrained("ctrl")
-        input_ids = torch.tensor(
-            [[11859, 0, 1611, 8]], dtype=torch.long, device=torch_device
-        )  # Legal the president is
+        input_ids = torch.Tensor([[11859, 586, 20984, 8]]).long()  # Legal My neighbor is
        expected_output_ids = [
            11859,
-            0,
-            1611,
+            586,
+            20984,
            8,
-            5,
-            150,
-            26449,
-            2,
-            19,
-            348,
-            469,
+            13391,
            3,
-            2595,
-            48,
-            20740,
-            246533,
-            246533,
-            19,
-            30,
-            5,
-        ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
+            980,
+            8258,
+            72,
+            327,
+            148,
+            2,
+            53,
+            29,
+            226,
+            3,
+            780,
+            49,
+            3,
+            980,
+        ]  # Legal My neighbor is refusing to pay rent after 2 years and we are having to force him to pay
+        torch.manual_seed(0)

-        output_ids = model.generate(input_ids, do_sample=False)
+        output_ids = model.generate(input_ids)
        self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
@@ -223,7 +223,7 @@ class GPT2ModelTest(ModelTesterMixin, unittest.TestCase):
            # append to next input_ids and attn_mask
            next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
            attn_mask = torch.cat(
-                [attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)], dim=1,
+                [attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)], dim=1
            )

            # get two different outputs
@@ -343,36 +343,39 @@ class GPT2ModelLanguageGenerationTest(unittest.TestCase):
    @slow
    def test_lm_generate_gpt2(self):
        model = GPT2LMHeadModel.from_pretrained("gpt2")
-        input_ids = torch.tensor([[464, 3290]], dtype=torch.long, device=torch_device)  # The dog
+        input_ids = torch.Tensor([[464, 3290, 318, 13779]]).long()  # The dog is cute
        expected_output_ids = [
            464,
            3290,
-            373,
-            1043,
-            287,
-            257,
-            2214,
-            1474,
-            262,
-            16246,
-            286,
-            2688,
-            290,
-            2688,
-            27262,
+            318,
+            13779,
+            1165,
            13,
-            198,
-            198,
-            464,
+            632,
+            7832,
+            284,
+            6437,
+            319,
+            502,
+            290,
+            318,
+            922,
+            329,
+            502,
+            357,
+            1169,
            3290,
-        ]  # The dog was found in a field near the intersection of West and West Streets.\n\nThe dog
-        output_ids = model.generate(input_ids, do_sample=False)
+        ]  # The dog is cute too. It likes to rub on me and is good for me (the dog
+        torch.manual_seed(0)
+
+        output_ids = model.generate(input_ids)
+
        self.assertListEqual(output_ids[0].tolist(), expected_output_ids)

    @slow
    def test_lm_generate_distilgpt2(self):
        model = GPT2LMHeadModel.from_pretrained("distilgpt2")
-        input_ids = torch.tensor([[464, 1893]], dtype=torch.long, device=torch_device)  # The president
+        input_ids = torch.Tensor([[464, 1893]]).long()  # The president
        expected_output_ids = [
            464,
            1893,
@@ -123,15 +123,7 @@ class OpenAIGPTModelTest(ModelTesterMixin, unittest.TestCase):

            head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)

-            return (
-                config,
-                input_ids,
-                head_mask,
-                token_type_ids,
-                sequence_labels,
-                token_labels,
-                choice_labels,
-            )
+            return config, input_ids, head_mask, token_type_ids, sequence_labels, token_labels, choice_labels

        def check_loss_output(self, result):
            self.parent.assertListEqual(list(result["loss"].size()), [])
@@ -147,7 +139,7 @@ class OpenAIGPTModelTest(ModelTesterMixin, unittest.TestCase):

            result = {"sequence_output": sequence_output}
            self.parent.assertListEqual(
-                list(result["sequence_output"].size()), [self.batch_size, self.seq_length, self.hidden_size],
+                list(result["sequence_output"].size()), [self.batch_size, self.seq_length, self.hidden_size]
            )

        def create_and_check_lm_head_model(self, config, input_ids, head_mask, token_type_ids, *args):
@@ -161,7 +153,7 @@ class OpenAIGPTModelTest(ModelTesterMixin, unittest.TestCase):

            self.parent.assertListEqual(list(result["loss"].size()), [])
            self.parent.assertListEqual(
-                list(result["lm_logits"].size()), [self.batch_size, self.seq_length, self.vocab_size],
+                list(result["lm_logits"].size()), [self.batch_size, self.seq_length, self.vocab_size]
            )

        def create_and_check_double_lm_head_model(self, config, input_ids, head_mask, token_type_ids, *args):
@@ -175,7 +167,7 @@ class OpenAIGPTModelTest(ModelTesterMixin, unittest.TestCase):

            self.parent.assertListEqual(list(result["loss"].size()), [])
            self.parent.assertListEqual(
-                list(result["lm_logits"].size()), [self.batch_size, self.seq_length, self.vocab_size],
+                list(result["lm_logits"].size()), [self.batch_size, self.seq_length, self.vocab_size]
            )

        def prepare_config_and_inputs_for_common(self):
@@ -189,11 +181,7 @@ class OpenAIGPTModelTest(ModelTesterMixin, unittest.TestCase):
                token_labels,
                choice_labels,
            ) = config_and_inputs
-            inputs_dict = {
-                "input_ids": input_ids,
-                "token_type_ids": token_type_ids,
-                "head_mask": head_mask,
-            }
+            inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "head_mask": head_mask}

            return config, inputs_dict

@@ -227,29 +215,30 @@ class OPENAIGPTModelLanguageGenerationTest(unittest.TestCase):
    @slow
    def test_lm_generate_openai_gpt(self):
        model = OpenAIGPTLMHeadModel.from_pretrained("openai-gpt")
-        input_ids = torch.tensor([[481, 4735, 544]], dtype=torch.long, device=torch_device)  # the president is
+        input_ids = torch.Tensor([[481, 2585, 544, 4957]]).long()  # The dog is cute
        expected_output_ids = [
            481,
-            4735,
+            2585,
            544,
-            246,
-            963,
-            870,
-            762,
-            239,
-            244,
-            40477,
-            244,
-            249,
-            719,
-            881,
+            4957,
+            669,
+            512,
+            761,
+            5990,
+            271,
+            645,
            487,
-            544,
+            535,
+            976,
+            2479,
            240,
-            244,
-            603,
-            481,
-        ]  # the president is a very good man. " \n " i\'m sure he is, " said the
+            487,
+            804,
+            1296,
+            2891,
+            512,
+        ]  # the dog is cute when you're annoyed : if he's really stupid, he 'll stop fighting you
+        torch.manual_seed(0)

-        output_ids = model.generate(input_ids, do_sample=False)
+        output_ids = model.generate(input_ids)
        self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
@@ -19,7 +19,6 @@ import os
 import random
 import tempfile
 import unittest
-from importlib import import_module

 from transformers import is_tf_available, is_torch_available

@@ -90,49 +89,13 @@ class TFModelTesterMixin:
                model = model_class.from_pretrained(tmpdirname)
                after_outputs = model(inputs_dict)

-                self.assert_outputs_same(after_outputs, outputs)
-
-    def test_keras_save_load(self):
-        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
-
-        tf_main_layer_classes = set(
-            module_member
-            for model_class in self.all_model_classes
-            for module in (import_module(model_class.__module__),)
-            for module_member_name in dir(module)
-            if module_member_name.endswith("MainLayer")
-            for module_member in (getattr(module, module_member_name),)
-            if isinstance(module_member, type)
-            and tf.keras.layers.Layer in module_member.__bases__
-            and getattr(module_member, "_keras_serializable", False)
-        )
-        for main_layer_class in tf_main_layer_classes:
-            main_layer = main_layer_class(config)
-            symbolic_inputs = {
-                name: tf.keras.Input(tensor.shape[1:], dtype=tensor.dtype) for name, tensor in inputs_dict.items()
-            }
-            model = tf.keras.Model(symbolic_inputs, outputs=main_layer(symbolic_inputs))
-            outputs = model(inputs_dict)
-
-            with tempfile.TemporaryDirectory() as tmpdirname:
-                filepath = os.path.join(tmpdirname, "keras_model.h5")
-                model.save(filepath)
-                model = tf.keras.models.load_model(
-                    filepath, custom_objects={main_layer_class.__name__: main_layer_class}
-                )
-                assert isinstance(model, tf.keras.Model)
-                after_outputs = model(inputs_dict)
-                self.assert_outputs_same(after_outputs, outputs)
-
-    def assert_outputs_same(self, after_outputs, outputs):
-        # Make sure we don't have nans
-        out_1 = after_outputs[0].numpy()
-        out_2 = outputs[0].numpy()
-        self.assertEqual(out_1.shape, out_2.shape)
-        out_1 = out_1[~np.isnan(out_1)]
-        out_2 = out_2[~np.isnan(out_2)]
-        max_diff = np.amax(np.abs(out_1 - out_2))
-        self.assertLessEqual(max_diff, 1e-5)
+                # Make sure we don't have nans
+                out_1 = after_outputs[0].numpy()
+                out_2 = outputs[0].numpy()
+                out_1 = out_1[~np.isnan(out_1)]
+                out_2 = out_2[~np.isnan(out_2)]
+                max_diff = np.amax(np.abs(out_1 - out_2))
+                self.assertLessEqual(max_diff, 1e-5)

    def test_pt_tf_model_equivalence(self):
        if not is_torch_available():
@@ -24,7 +24,6 @@ from .utils import CACHE_DIR, require_tf, slow


 if is_tf_available():
-    import tensorflow as tf
    from transformers.modeling_tf_ctrl import TFCTRLModel, TFCTRLLMHeadModel, TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP


@@ -203,35 +202,3 @@ class TFCTRLModelTest(TFModelTesterMixin, unittest.TestCase):
        for model_name in list(TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
            model = TFCTRLModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
            self.assertIsNotNone(model)
-
-
-class TFCTRLModelLanguageGenerationTest(unittest.TestCase):
-    @slow
-    def test_lm_generate_ctrl(self):
-        model = TFCTRLLMHeadModel.from_pretrained("ctrl")
-        input_ids = tf.convert_to_tensor([[11859, 0, 1611, 8]], dtype=tf.int32)  # Legal the president is
-        expected_output_ids = [
-            11859,
-            0,
-            1611,
-            8,
-            5,
-            150,
-            26449,
-            2,
-            19,
-            348,
-            469,
-            3,
-            2595,
-            48,
-            20740,
-            246533,
-            246533,
-            19,
-            30,
-            5,
-        ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
-
-        output_ids = model.generate(input_ids, do_sample=False)
-        self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)
@@ -328,35 +328,13 @@ class TFGPT2ModelTest(TFModelTesterMixin, unittest.TestCase):
            self.assertIsNotNone(model)


+def prepare_generation_special_tokens():
+    return {"bos_token_id": 50256, "eos_token_id": 50256}
+
+
 class TFGPT2ModelLanguageGenerationTest(unittest.TestCase):
-    @slow
-    def test_lm_generate_gpt2(self):
-        model = TFGPT2LMHeadModel.from_pretrained("gpt2")
-        input_ids = tf.convert_to_tensor([[464, 3290]], dtype=tf.int32)  # The dog
-        expected_output_ids = [
-            464,
-            3290,
-            373,
-            1043,
-            287,
-            257,
-            2214,
-            1474,
-            262,
-            16246,
-            286,
-            2688,
-            290,
-            2688,
-            27262,
-            13,
-            198,
-            198,
-            464,
-            3290,
-        ]  # The dog was found in a field near the intersection of West and West Streets.\n\nThe dog
-        output_ids = model.generate(input_ids, do_sample=False)
-        self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)
+
+    special_tokens = prepare_generation_special_tokens()

    @slow
    def test_lm_generate_distilgpt2(self):
@@ -385,5 +363,11 @@ class TFGPT2ModelLanguageGenerationTest(unittest.TestCase):
            2635,
        ]  # The president of the United States, and the president of the United Kingdom, have been in the White

-        output_ids = model.generate(input_ids, do_sample=False)
+        output_ids = model.generate(
+            input_ids,
+            do_sample=False,
+            bos_token_id=self.special_tokens["bos_token_id"],
+            eos_token_ids=self.special_tokens["eos_token_id"],
+        )
+
        self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)
@@ -238,35 +238,3 @@ class TFOpenAIGPTModelTest(TFModelTesterMixin, unittest.TestCase):
        for model_name in list(TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
            model = TFOpenAIGPTModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
            self.assertIsNotNone(model)
-
-
-class TFOPENAIGPTModelLanguageGenerationTest(unittest.TestCase):
-    @slow
-    def test_lm_generate_openai_gpt(self):
-        model = TFOpenAIGPTLMHeadModel.from_pretrained("openai-gpt")
-        input_ids = tf.convert_to_tensor([[481, 4735, 544]], dtype=tf.int32)  # the president is
-        expected_output_ids = [
-            481,
-            4735,
-            544,
-            246,
-            963,
-            870,
-            762,
-            239,
-            244,
-            40477,
-            244,
-            249,
-            719,
-            881,
-            487,
-            544,
-            240,
-            244,
-            603,
-            481,
-        ]  # the president is a very good man. " \n " i\'m sure he is, " said the
-
-        output_ids = model.generate(input_ids, do_sample=False)
-        self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)
@@ -212,366 +212,3 @@ class TFTransfoXLModelTest(TFModelTesterMixin, unittest.TestCase):
        for model_name in list(TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
            model = TFTransfoXLModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
            self.assertIsNotNone(model)
-
-
-class TFTransfoXLModelLanguageGenerationTest(unittest.TestCase):
-    @slow
-    def test_lm_generate_transfo_xl_wt103(self):
-        model = TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103")
-        input_ids = tf.convert_to_tensor(
-            [
-                [
-                    33,
-                    1297,
-                    2,
-                    1,
-                    1009,
-                    4,
-                    1109,
-                    11739,
-                    4762,
-                    358,
-                    5,
-                    25,
-                    245,
-                    22,
-                    1706,
-                    17,
-                    20098,
-                    5,
-                    3215,
-                    21,
-                    37,
-                    1110,
-                    3,
-                    13,
-                    1041,
-                    4,
-                    24,
-                    603,
-                    490,
-                    2,
-                    71477,
-                    20098,
-                    104447,
-                    2,
-                    20961,
-                    1,
-                    2604,
-                    4,
-                    1,
-                    329,
-                    3,
-                    6224,
-                    831,
-                    16002,
-                    2,
-                    8,
-                    603,
-                    78967,
-                    29546,
-                    23,
-                    803,
-                    20,
-                    25,
-                    416,
-                    5,
-                    8,
-                    232,
-                    4,
-                    277,
-                    6,
-                    1855,
-                    4601,
-                    3,
-                    29546,
-                    54,
-                    8,
-                    3609,
-                    5,
-                    57211,
-                    49,
-                    4,
-                    1,
-                    277,
-                    18,
-                    8,
-                    1755,
-                    15691,
-                    3,
-                    341,
-                    25,
-                    416,
-                    693,
-                    42573,
-                    71,
-                    17,
-                    401,
-                    94,
-                    31,
-                    17919,
-                    2,
-                    29546,
-                    7873,
-                    18,
-                    1,
-                    435,
-                    23,
-                    11011,
-                    755,
-                    5,
-                    5167,
-                    3,
-                    7983,
-                    98,
-                    84,
-                    2,
-                    29546,
-                    3267,
-                    8,
-                    3609,
-                    4,
-                    1,
-                    4865,
-                    1075,
-                    2,
-                    6087,
-                    71,
-                    6,
-                    346,
-                    8,
-                    5854,
-                    3,
-                    29546,
-                    824,
-                    1400,
-                    1868,
-                    2,
-                    19,
-                    160,
-                    2,
-                    311,
-                    8,
-                    5496,
-                    2,
-                    20920,
-                    17,
-                    25,
-                    15097,
-                    3,
-                    24,
-                    24,
-                    0,
-                ]
-            ],
-            dtype=tf.int31,
-        )
-        #  In 1991 , the remains of Russian Tsar Nicholas II and his family
-        #  ( except for Alexei and Maria ) are discovered .
-        #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
-        #  remainder of the story . 1883 Western Siberia ,
-        #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
-        #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
-        #  father initially slaps him for making such an accusation , Rasputin watches as the
-        #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
-        #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
-        #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>
-
-        expected_output_ids = [
-            33,
-            1297,
-            2,
-            1,
-            1009,
-            4,
-            1109,
-            11739,
-            4762,
-            358,
-            5,
-            25,
-            245,
-            22,
-            1706,
-            17,
-            20098,
-            5,
-            3215,
-            21,
-            37,
-            1110,
-            3,
-            13,
-            1041,
-            4,
-            24,
-            603,
-            490,
-            2,
-            71477,
-            20098,
-            104447,
-            2,
-            20961,
-            1,
-            2604,
-            4,
-            1,
-            329,
-            3,
-            6224,
-            831,
-            16002,
-            2,
-            8,
-            603,
-            78967,
-            29546,
-            23,
-            803,
-            20,
-            25,
-            416,
-            5,
-            8,
-            232,
-            4,
-            277,
-            6,
-            1855,
-            4601,
-            3,
-            29546,
-            54,
-            8,
-            3609,
-            5,
-            57211,
-            49,
-            4,
-            1,
-            277,
-            18,
-            8,
-            1755,
-            15691,
-            3,
-            341,
-            25,
-            416,
-            693,
-            42573,
-            71,
-            17,
-            401,
-            94,
-            31,
-            17919,
-            2,
-            29546,
-            7873,
-            18,
-            1,
-            435,
-            23,
-            11011,
-            755,
-            5,
-            5167,
-            3,
-            7983,
-            98,
-            84,
-            2,
-            29546,
-            3267,
-            8,
-            3609,
-            4,
-            1,
-            4865,
-            1075,
-            2,
-            6087,
-            71,
-            6,
-            346,
-            8,
-            5854,
-            3,
-            29546,
-            824,
-            1400,
-            1868,
-            2,
-            19,
-            160,
-            2,
-            311,
-            8,
-            5496,
-            2,
-            20920,
-            17,
-            25,
-            15097,
-            3,
-            24,
-            24,
-            0,
-            33,
-            1,
-            1857,
-            2,
-            1,
-            1009,
-            4,
-            1109,
-            11739,
-            4762,
-            358,
-            5,
-            25,
-            245,
-            28,
-            1110,
-            3,
-            13,
-            1041,
-            4,
-            24,
-            603,
-            490,
-            2,
-            71477,
-            20098,
-            104447,
-            2,
-            20961,
-            1,
-            2604,
-            4,
-            1,
-            329,
-            3,
-            0,
-        ]
-        #  In 1991, the remains of Russian Tsar Nicholas II and his family (
-        #  except for Alexei and Maria ) are discovered. The voice of young son,
-        #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
-        #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
-        #  and a group of men to perform magic. Rasputin has a vision and
-        #  denounces one of the men as a horse thief. Although his father initially
-        #  slaps him for making such an accusation, Rasputin watches as the man
-        #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
-        #  of the Virgin Mary, prompting him to become a priest.
-        #  Rasputin quickly becomes famous, with people, even a bishop, begging for
-        #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
-        # Nicholas II and his family were discovered. The voice of <unk> young son,
-        # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>
-
-        # TODO: add this test when trasnfo-xl-lmhead is implemented
-        with self.assertRaises(NotImplementedError):
-            model.generate(input_ids, max_length=200, do_sample=False)
-            print(expected_output_ids)
-        # self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids) TODO: (PVP) to add when transfo-xl is implemented
@@ -311,35 +311,3 @@ class TFXLMModelTest(TFModelTesterMixin, unittest.TestCase):
        for model_name in list(TF_XLM_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
            model = TFXLMModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
            self.assertIsNotNone(model)
-
-
-class TFXLMModelLanguageGenerationTest(unittest.TestCase):
-    @slow
-    def test_lm_generate_xlm_mlm_en_2048(self):
-        model = TFXLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048")
-        input_ids = tf.convert_to_tensor([[14, 447]], dtype=tf.int32)  # the president
-        expected_output_ids = [
-            14,
-            447,
-            14,
-            447,
-            14,
-            447,
-            14,
-            447,
-            14,
-            447,
-            14,
-            447,
-            14,
-            447,
-            14,
-            447,
-            14,
-            447,
-            14,
-            447,
-        ]  # the president the president the president the president the president the president the president the president the president the president
-        # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
-        output_ids = model.generate(input_ids, do_sample=False)
-        self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)
@@ -413,405 +413,3 @@ class TFXLNetModelTest(TFModelTesterMixin, unittest.TestCase):
        for model_name in list(TF_XLNET_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
            model = TFXLNetModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
            self.assertIsNotNone(model)
-
-
-class TFXLNetModelLanguageGenerationTest(unittest.TestCase):
-    @slow
-    def test_lm_generate_xlnet_base_cased(self):
-        model = TFXLNetLMHeadModel.from_pretrained("xlnet-base-cased")
-        input_ids = tf.convert_to_tensor(
-            [
-                [
-                    67,
-                    2840,
-                    19,
-                    18,
-                    1484,
-                    20,
-                    965,
-                    29077,
-                    8719,
-                    1273,
-                    21,
-                    45,
-                    273,
-                    17,
-                    10,
-                    15048,
-                    28,
-                    27511,
-                    21,
-                    4185,
-                    11,
-                    41,
-                    2444,
-                    9,
-                    32,
-                    1025,
-                    20,
-                    8719,
-                    26,
-                    23,
-                    673,
-                    966,
-                    19,
-                    29077,
-                    20643,
-                    27511,
-                    20822,
-                    20643,
-                    19,
-                    17,
-                    6616,
-                    17511,
-                    18,
-                    8978,
-                    20,
-                    18,
-                    777,
-                    9,
-                    19233,
-                    1527,
-                    17669,
-                    19,
-                    24,
-                    673,
-                    17,
-                    28756,
-                    150,
-                    12943,
-                    4354,
-                    153,
-                    27,
-                    442,
-                    37,
-                    45,
-                    668,
-                    21,
-                    24,
-                    256,
-                    20,
-                    416,
-                    22,
-                    2771,
-                    4901,
-                    9,
-                    12943,
-                    4354,
-                    153,
-                    51,
-                    24,
-                    3004,
-                    21,
-                    28142,
-                    23,
-                    65,
-                    20,
-                    18,
-                    416,
-                    34,
-                    24,
-                    2958,
-                    22947,
-                    9,
-                    1177,
-                    45,
-                    668,
-                    3097,
-                    13768,
-                    23,
-                    103,
-                    28,
-                    441,
-                    148,
-                    48,
-                    20522,
-                    19,
-                    12943,
-                    4354,
-                    153,
-                    12860,
-                    34,
-                    18,
-                    326,
-                    27,
-                    17492,
-                    684,
-                    21,
-                    6709,
-                    9,
-                    8585,
-                    123,
-                    266,
-                    19,
-                    12943,
-                    4354,
-                    153,
-                    6872,
-                    24,
-                    3004,
-                    20,
-                    18,
-                    9225,
-                    2198,
-                    19,
-                    12717,
-                    103,
-                    22,
-                    401,
-                    24,
-                    6348,
-                    9,
-                    12943,
-                    4354,
-                    153,
-                    1068,
-                    2768,
-                    2286,
-                    19,
-                    33,
-                    104,
-                    19,
-                    176,
-                    24,
-                    9313,
-                    19,
-                    20086,
-                    28,
-                    45,
-                    10292,
-                    9,
-                    4,
-                    3,
-                ]
-            ],
-            dtype=tf.int32,
-        )
-        #  In 1991, the remains of Russian Tsar Nicholas II and his family
-        #  (except for Alexei and Maria) are discovered.
-        #  The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
-        #  remainder of the story. 1883 Western Siberia,
-        #  a young Grigori Rasputin is asked by his father and a group of men to perform magic.
-        #  Rasputin has a vision and denounces one of the men as a horse thief. Although his
-        #  father initially slaps him for making such an accusation, Rasputin watches as the
-        #  man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
-        #  the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
-        #  with people, even a bishop, begging for his blessing. """
-
-        expected_output_ids = [
-            67,
-            2840,
-            19,
-            18,
-            1484,
-            20,
-            965,
-            29077,
-            8719,
-            1273,
-            21,
-            45,
-            273,
-            17,
-            10,
-            15048,
-            28,
-            27511,
-            21,
-            4185,
-            11,
-            41,
-            2444,
-            9,
-            32,
-            1025,
-            20,
-            8719,
-            26,
-            23,
-            673,
-            966,
-            19,
-            29077,
-            20643,
-            27511,
-            20822,
-            20643,
-            19,
-            17,
-            6616,
-            17511,
-            18,
-            8978,
-            20,
-            18,
-            777,
-            9,
-            19233,
-            1527,
-            17669,
-            19,
-            24,
-            673,
-            17,
-            28756,
-            150,
-            12943,
-            4354,
-            153,
-            27,
-            442,
-            37,
-            45,
-            668,
-            21,
-            24,
-            256,
-            20,
-            416,
-            22,
-            2771,
-            4901,
-            9,
-            12943,
-            4354,
-            153,
-            51,
-            24,
-            3004,
-            21,
-            28142,
-            23,
-            65,
-            20,
-            18,
-            416,
-            34,
-            24,
-            2958,
-            22947,
-            9,
-            1177,
-            45,
-            668,
-            3097,
-            13768,
-            23,
-            103,
-            28,
-            441,
-            148,
-            48,
-            20522,
-            19,
-            12943,
-            4354,
-            153,
-            12860,
-            34,
-            18,
-            326,
-            27,
-            17492,
-            684,
-            21,
-            6709,
-            9,
-            8585,
-            123,
-            266,
-            19,
-            12943,
-            4354,
-            153,
-            6872,
-            24,
-            3004,
-            20,
-            18,
-            9225,
-            2198,
-            19,
-            12717,
-            103,
-            22,
-            401,
-            24,
-            6348,
-            9,
-            12943,
-            4354,
-            153,
-            1068,
-            2768,
-            2286,
-            19,
-            33,
-            104,
-            19,
-            176,
-            24,
-            9313,
-            19,
-            20086,
-            28,
-            45,
-            10292,
-            9,
-            4,
-            3,
-            19,
-            12943,
-            4354,
-            153,
-            27,
-            442,
-            22,
-            2771,
-            4901,
-            9,
-            69,
-            27,
-            50,
-            551,
-            22,
-            2771,
-            4901,
-            19,
-            21,
-            45,
-            668,
-            21,
-            18,
-            416,
-            41,
-            1499,
-            22,
-            755,
-            18,
-            14285,
-            9,
-            12943,
-            4354,
-            153,
-            27,
-            1499,
-            22,
-            642,
-            22,
-        ]
-        #  In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria)
-        #  are discovered. The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich,
-        #  narrates the remainder of the story. 1883 Western Siberia, a young Grigori Rasputin
-        #  is asked by his father and a group of men to perform magic. Rasputin has a vision and
-        #  denounces one of the men as a horse thief. Although his father initially slaps
-        #  him for making such an accusation, Rasputin watches as the man is chased outside and beaten.
-        #  Twenty years later, Rasputin sees a vision of the Virgin Mary, prompting him to become a priest.
-        #  Rasputin quickly becomes famous, with people, even a bishop, begging for his blessing.
-        #  <sep><cls>, Rasputin is asked to perform magic.
-        #  He is not able to perform magic, and his father and
-        # the men are forced to leave the monastery. Rasputin is forced to return to
-
-        output_ids = model.generate(input_ids, max_length=200, do_sample=False)
-
-        self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)
@@ -218,7 +218,7 @@ class TransfoXLModelLanguageGenerationTest(unittest.TestCase):
    @slow
    def test_lm_generate_transfo_xl_wt103(self):
        model = TransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103")
-        input_ids = torch.tensor(
+        input_ids = torch.Tensor(
            [
                [
                    33,
@@ -363,10 +363,8 @@ class TransfoXLModelLanguageGenerationTest(unittest.TestCase):
                    24,
                    0,
                ]
-            ],
-            dtype=torch.long,
-            device=torch_device,
-        )
+            ]
+        ).long()
        #  In 1991 , the remains of Russian Tsar Nicholas II and his family
        #  ( except for Alexei and Maria ) are discovered .
        #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
@@ -376,7 +374,6 @@ class TransfoXLModelLanguageGenerationTest(unittest.TestCase):
        #  father initially slaps him for making such an accusation , Rasputin watches as the
        #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
        #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
-
        #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

        expected_output_ids = [
@@ -521,10 +518,20 @@ class TransfoXLModelLanguageGenerationTest(unittest.TestCase):
            24,
            24,
            0,
-            33,
-            1,
-            1857,
+            29546,
+            40,
+            1092,
+            18,
+            8,
+            5854,
+            7,
+            1143,
            2,
+            7,
+            1,
+            159,
+            99,
+            16,
            1,
            1009,
            4,
@@ -538,23 +545,14 @@ class TransfoXLModelLanguageGenerationTest(unittest.TestCase):
            28,
            1110,
            3,
-            13,
-            1041,
-            4,
-            24,
-            603,
-            490,
-            2,
-            71477,
-            20098,
-            104447,
-            2,
-            20961,
-            1,
-            2604,
-            4,
-            1,
-            329,
+            57,
+            629,
+            38,
+            3493,
+            47,
+            1094,
+            7,
+            1297,
            3,
            0,
        ]
@@ -568,9 +566,10 @@ class TransfoXLModelLanguageGenerationTest(unittest.TestCase):
        #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
        #  of the Virgin Mary, prompting him to become a priest.
        #  Rasputin quickly becomes famous, with people, even a bishop, begging for
-        #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
-        # Nicholas II and his family were discovered. The voice of <unk> young son,
-        # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>
+        #  his blessing. Rasputin first appears as a priest in 1996, in the same year
+        #  that the remains of Russian Tsar Nicholas II and his family were discovered. H

-        output_ids = model.generate(input_ids, max_length=200, do_sample=False)
+        torch.manual_seed(0)
+
+        output_ids = model.generate(input_ids, max_length=200)
        self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
@@ -403,29 +403,31 @@ class XLMModelLanguageGenerationTest(unittest.TestCase):
    @slow
    def test_lm_generate_xlm_mlm_en_2048(self):
        model = XLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048")
-        input_ids = torch.tensor([[14, 447]], dtype=torch.long, device=torch_device)  # the president
+        input_ids = torch.Tensor([[1, 14, 2232, 26, 1]]).long()  # The dog is cute
        expected_output_ids = [
+            1,
            14,
-            447,
-            14,
-            447,
-            14,
-            447,
-            14,
-            447,
-            14,
-            447,
-            14,
-            447,
-            14,
-            447,
-            14,
-            447,
-            14,
-            447,
-            14,
-            447,
-        ]  # the president the president the president the president the president the president the president the president the president the president
-        # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
-        output_ids = model.generate(input_ids, do_sample=False)
-        self.assertListEqual(output_ids[0].numpy().tolist(), expected_output_ids)
+            2232,
+            26,
+            1,
+            567,
+            26,
+            32,
+            149,
+            149,
+            149,
+            149,
+            149,
+            149,
+            149,
+            149,
+            149,
+            149,
+            149,
+            149,
+        ]  # The dog is nothing is it!!!!!!!!!!!! TODO (PVP): this sentence (and others I tried) does not make much sense, there seems to be a problem with xlm language generation.
+        torch.manual_seed(0)
+
+        output_ids = model.generate(input_ids)
+
+        self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
@@ -119,11 +119,11 @@ class XLNetModelTest(ModelTesterMixin, unittest.TestCase):

            input_ids_q = ids_tensor([self.batch_size, self.seq_length + 1], self.vocab_size)
            perm_mask = torch.zeros(
-                self.batch_size, self.seq_length + 1, self.seq_length + 1, dtype=torch.float, device=torch_device,
+                self.batch_size, self.seq_length + 1, self.seq_length + 1, dtype=torch.float, device=torch_device
            )
            perm_mask[:, :, -1] = 1.0  # Previous tokens don't see last token
            target_mapping = torch.zeros(
-                self.batch_size, 1, self.seq_length + 1, dtype=torch.float, device=torch_device,
+                self.batch_size, 1, self.seq_length + 1, dtype=torch.float, device=torch_device
            )
            target_mapping[:, 0, -1] = 1.0  # predict last token

@@ -212,7 +212,7 @@ class XLNetModelTest(ModelTesterMixin, unittest.TestCase):
            self.parent.assertEqual(len(no_mems_outputs), 1)

            self.parent.assertListEqual(
-                list(result["outputs"].size()), [self.batch_size, self.seq_length, self.hidden_size],
+                list(result["outputs"].size()), [self.batch_size, self.seq_length, self.hidden_size]
            )
            self.parent.assertListEqual(
                list(list(mem.size()) for mem in result["mems_1"]),
@@ -283,7 +283,7 @@ class XLNetModelTest(ModelTesterMixin, unittest.TestCase):

            self.parent.assertListEqual(list(result["loss_1"].size()), [])
            self.parent.assertListEqual(
-                list(result["all_logits_1"].size()), [self.batch_size, self.seq_length, self.vocab_size],
+                list(result["all_logits_1"].size()), [self.batch_size, self.seq_length, self.vocab_size]
            )
            self.parent.assertListEqual(
                list(list(mem.size()) for mem in result["mems_1"]),
@@ -292,7 +292,7 @@ class XLNetModelTest(ModelTesterMixin, unittest.TestCase):

            self.parent.assertListEqual(list(result["loss_2"].size()), [])
            self.parent.assertListEqual(
-                list(result["all_logits_2"].size()), [self.batch_size, self.seq_length, self.vocab_size],
+                list(result["all_logits_2"].size()), [self.batch_size, self.seq_length, self.vocab_size]
            )
            self.parent.assertListEqual(
                list(list(mem.size()) for mem in result["mems_2"]),
@@ -319,7 +319,7 @@ class XLNetModelTest(ModelTesterMixin, unittest.TestCase):
            model.eval()

            outputs = model(input_ids_1)
-            (start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits, mems,) = outputs
+            start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits, mems = outputs

            outputs = model(
                input_ids_1,
@@ -340,7 +340,7 @@ class XLNetModelTest(ModelTesterMixin, unittest.TestCase):

            total_loss, mems = outputs

-            outputs = model(input_ids_1, start_positions=sequence_labels, end_positions=sequence_labels,)
+            outputs = model(input_ids_1, start_positions=sequence_labels, end_positions=sequence_labels)

            total_loss, mems = outputs

@@ -356,10 +356,10 @@ class XLNetModelTest(ModelTesterMixin, unittest.TestCase):

            self.parent.assertListEqual(list(result["loss"].size()), [])
            self.parent.assertListEqual(
-                list(result["start_top_log_probs"].size()), [self.batch_size, model.config.start_n_top],
+                list(result["start_top_log_probs"].size()), [self.batch_size, model.config.start_n_top]
            )
            self.parent.assertListEqual(
-                list(result["start_top_index"].size()), [self.batch_size, model.config.start_n_top],
+                list(result["start_top_index"].size()), [self.batch_size, model.config.start_n_top]
            )
            self.parent.assertListEqual(
                list(result["end_top_log_probs"].size()),
@@ -405,7 +405,7 @@ class XLNetModelTest(ModelTesterMixin, unittest.TestCase):

            self.parent.assertListEqual(list(result["loss"].size()), [])
            self.parent.assertListEqual(
-                list(result["logits"].size()), [self.batch_size, self.seq_length, self.type_sequence_label_size],
+                list(result["logits"].size()), [self.batch_size, self.seq_length, self.type_sequence_label_size]
            )
            self.parent.assertListEqual(
                list(list(mem.size()) for mem in result["mems_1"]),
@@ -442,7 +442,7 @@ class XLNetModelTest(ModelTesterMixin, unittest.TestCase):

            self.parent.assertListEqual(list(result["loss"].size()), [])
            self.parent.assertListEqual(
-                list(result["logits"].size()), [self.batch_size, self.type_sequence_label_size],
+                list(result["logits"].size()), [self.batch_size, self.type_sequence_label_size]
            )
            self.parent.assertListEqual(
                list(list(mem.size()) for mem in result["mems_1"]),
@@ -517,7 +517,7 @@ class XLNetModelLanguageGenerationTest(unittest.TestCase):
    @slow
    def test_lm_generate_xlnet_base_cased(self):
        model = XLNetLMHeadModel.from_pretrained("xlnet-base-cased")
-        input_ids = torch.tensor(
+        input_ids = torch.Tensor(
            [
                [
                    67,
@@ -682,10 +682,8 @@ class XLNetModelLanguageGenerationTest(unittest.TestCase):
                    4,
                    3,
                ]
-            ],
-            dtype=torch.long,
-            device=torch_device,
-        )
+            ]
+        ).long()
        #  In 1991, the remains of Russian Tsar Nicholas II and his family
        #  (except for Alexei and Maria) are discovered.
        #  The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
@@ -859,45 +857,45 @@ class XLNetModelLanguageGenerationTest(unittest.TestCase):
            9,
            4,
            3,
+            1722,
+            19,
+            24,
+            6348,
+            61,
+            977,
+            176,
+            1772,
+            33,
+            45,
+            970,
+            19,
+            4185,
            19,
-            12943,
-            4354,
-            153,
            27,
            442,
            22,
            2771,
            4901,
+            25,
+            18,
+            2059,
+            20,
+            24,
+            303,
+            1775,
+            691,
            9,
-            69,
-            27,
-            50,
-            551,
-            22,
-            2771,
-            4901,
+            1147,
            19,
-            21,
-            45,
-            668,
-            21,
-            18,
-            416,
-            41,
-            1499,
-            22,
-            755,
-            18,
-            14285,
-            9,
-            12943,
-            4354,
-            153,
-            27,
-            1499,
-            22,
-            642,
-            22,
+            634,
+            19,
+            43,
+            51,
+            54,
+            6157,
+            2999,
+            33,
+            4185,
        ]
        #  In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria)
        #  are discovered. The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich,
@@ -907,9 +905,11 @@ class XLNetModelLanguageGenerationTest(unittest.TestCase):
        #  him for making such an accusation, Rasputin watches as the man is chased outside and beaten.
        #  Twenty years later, Rasputin sees a vision of the Virgin Mary, prompting him to become a priest.
        #  Rasputin quickly becomes famous, with people, even a bishop, begging for his blessing.
-        #  <sep><cls>, Rasputin is asked to perform magic.
-        #  He is not able to perform magic, and his father and
-        # the men are forced to leave the monastery. Rasputin is forced to return to
+        #  1990, a priest who cannot even walk with his wife, Maria, is asked to perform magic
+        #  in the presence of a local religious leader.
+        #  Since, however, he has had difficulty walking with Maria
+
+        torch.manual_seed(0)
+        output_ids = model.generate(input_ids, max_length=200)

-        output_ids = model.generate(input_ids, max_length=200, do_sample=False)
        self.assertListEqual(output_ids[0].tolist(), expected_output_ids)
@@ -291,7 +291,7 @@ class MultiColumnInputTestCase(unittest.TestCase):
            self._test_multicolumn_pipeline(nlp, valid_samples, invalid_samples, mandatory_output_keys)

    @require_tf
-    @slow
+    @unittest.skip("This test is failing intermittently. Skipping it until we resolve.")
    def test_tf_question_answering(self):
        mandatory_output_keys = {"score", "answer", "start", "end"}
        valid_samples = [
@@ -48,7 +48,7 @@ class TokenizerTesterMixin:
        # to the concatenated encode_plus format: [{'input_ids': [...], ...}, {'input_ids': [...], ...}]
        return [
            {value: batch_encode_plus_sequences[value][i] for value in batch_encode_plus_sequences.keys()}
-            for i in range(len(batch_encode_plus_sequences["input_ids"]))
+            for i in range(len(batch_encode_plus_sequences))
        ]

    def test_tokenizers_common_properties(self):
@@ -261,10 +261,7 @@ class TokenizerTesterMixin:
    def test_mask_output(self):
        tokenizer = self.get_tokenizer()

-        if (
-            tokenizer.build_inputs_with_special_tokens.__qualname__.split(".")[0] != "PreTrainedTokenizer"
-            and "token_type_ids" in tokenizer.model_input_names
-        ):
+        if tokenizer.build_inputs_with_special_tokens.__qualname__.split(".")[0] != "PreTrainedTokenizer":
            seq_0 = "Test this method."
            seq_1 = "With these inputs."
            information = tokenizer.encode_plus(seq_0, seq_1, add_special_tokens=True)
@@ -507,58 +504,51 @@ class TokenizerTesterMixin:

        encoded_sequence = tokenizer.encode_plus(sequence, return_special_tokens_mask=True)
        input_ids = encoded_sequence["input_ids"]
+        token_type_ids = encoded_sequence["token_type_ids"]
+        attention_mask = encoded_sequence["attention_mask"]
        special_tokens_mask = encoded_sequence["special_tokens_mask"]
        sequence_length = len(input_ids)

        # Test right padding
        tokenizer.padding_side = "right"

-        right_padded_sequence = tokenizer.encode_plus(
+        padded_sequence = tokenizer.encode_plus(
            sequence,
            max_length=sequence_length + padding_size,
            pad_to_max_length=True,
            return_special_tokens_mask=True,
        )
-        right_padded_input_ids = right_padded_sequence["input_ids"]
+        padded_input_ids = padded_sequence["input_ids"]
+        padded_token_type_ids = padded_sequence["token_type_ids"]
+        padded_attention_mask = padded_sequence["attention_mask"]
+        padded_special_tokens_mask = padded_sequence["special_tokens_mask"]
+        padded_sequence_length = len(padded_input_ids)

-        right_padded_special_tokens_mask = right_padded_sequence["special_tokens_mask"]
-        right_padded_sequence_length = len(right_padded_input_ids)
-
-        assert sequence_length + padding_size == right_padded_sequence_length
-        assert input_ids + [padding_idx] * padding_size == right_padded_input_ids
-        assert special_tokens_mask + [1] * padding_size == right_padded_special_tokens_mask
+        assert sequence_length + padding_size == padded_sequence_length
+        assert input_ids + [padding_idx] * padding_size == padded_input_ids
+        assert token_type_ids + [token_type_padding_idx] * padding_size == padded_token_type_ids
+        assert attention_mask + [0] * padding_size == padded_attention_mask
+        assert special_tokens_mask + [1] * padding_size == padded_special_tokens_mask

        # Test left padding
        tokenizer.padding_side = "left"
-        left_padded_sequence = tokenizer.encode_plus(
+        padded_sequence = tokenizer.encode_plus(
            sequence,
            max_length=sequence_length + padding_size,
            pad_to_max_length=True,
            return_special_tokens_mask=True,
        )
-        left_padded_input_ids = left_padded_sequence["input_ids"]
-        left_padded_special_tokens_mask = left_padded_sequence["special_tokens_mask"]
-        left_padded_sequence_length = len(left_padded_input_ids)
+        padded_input_ids = padded_sequence["input_ids"]
+        padded_token_type_ids = padded_sequence["token_type_ids"]
+        padded_attention_mask = padded_sequence["attention_mask"]
+        padded_special_tokens_mask = padded_sequence["special_tokens_mask"]
+        padded_sequence_length = len(padded_input_ids)

-        assert sequence_length + padding_size == left_padded_sequence_length
-        assert [padding_idx] * padding_size + input_ids == left_padded_input_ids
-        assert [1] * padding_size + special_tokens_mask == left_padded_special_tokens_mask
-
-        if "token_type_ids" in tokenizer.model_input_names:
-            token_type_ids = encoded_sequence["token_type_ids"]
-            left_padded_token_type_ids = left_padded_sequence["token_type_ids"]
-            right_padded_token_type_ids = right_padded_sequence["token_type_ids"]
-
-            assert token_type_ids + [token_type_padding_idx] * padding_size == right_padded_token_type_ids
-            assert [token_type_padding_idx] * padding_size + token_type_ids == left_padded_token_type_ids
-
-        if "attention_mask" in tokenizer.model_input_names:
-            attention_mask = encoded_sequence["attention_mask"]
-            right_padded_attention_mask = right_padded_sequence["attention_mask"]
-            left_padded_attention_mask = left_padded_sequence["attention_mask"]
-
-            assert attention_mask + [0] * padding_size == right_padded_attention_mask
-            assert [0] * padding_size + attention_mask == left_padded_attention_mask
+        assert sequence_length + padding_size == padded_sequence_length
+        assert [padding_idx] * padding_size + input_ids == padded_input_ids
+        assert [token_type_padding_idx] * padding_size + token_type_ids == padded_token_type_ids
+        assert [0] * padding_size + attention_mask == padded_attention_mask
+        assert [1] * padding_size + special_tokens_mask == padded_special_tokens_mask

    def test_separate_tokenizers(self):
        # This tests that tokenizers don't impact others. Unfortunately the case where it fails is when
@@ -272,6 +272,30 @@ class FastTokenizerMatchingTest(unittest.TestCase):
            # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
            # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

+    def assert_empty_output_no_special_tokens(self, ru_class, py_class, model):
+        tokenizer_r = ru_class.from_pretrained(model, add_special_tokens=False)
+        tokenizer_p = py_class.from_pretrained(model)
+
+        # add_special_tokens=False makes nothing for now.
+        self.assertEqual(
+            tokenizer_p.tokenize(" ", add_special_tokens=False), tokenizer_r.tokenize(" ", add_special_tokens=False)
+        )
+
+        self.assertEqual(
+            tokenizer_p.encode_plus(" ", add_special_tokens=False),
+            tokenizer_r.encode_plus(" ", add_special_tokens=False),
+        )
+
+        self.assertEqual(
+            tokenizer_p.encode_plus(" ", add_special_tokens=False),
+            tokenizer_r.encode_plus(" ", add_special_tokens=False),
+        )
+
+        self.assertEqual(
+            tokenizer_p.batch_encode_plus([" "], add_special_tokens=False),
+            tokenizer_r.batch_encode_plus([" "], add_special_tokens=False),
+        )
+
    def test_bert(self):
        for tokenizer_name in BertTokenizer.pretrained_vocab_files_map["vocab_file"].keys():
            tokenizer_p = BertTokenizer.from_pretrained(tokenizer_name)
@@ -313,6 +337,9 @@ class FastTokenizerMatchingTest(unittest.TestCase):
            # Check for padding
            self.assert_padding(tokenizer_r, tokenizer_p)

+            # Check for space-only input
+            self.assert_empty_output_no_special_tokens(tokenizer_r.__class__, tokenizer_p.__class__, tokenizer_name)
+
    @require_torch
    def test_transfoxl(self):
        for tokenizer_name in TransfoXLTokenizer.pretrained_vocab_files_map["pretrained_vocab_file"].keys():
@@ -369,6 +396,9 @@ class FastTokenizerMatchingTest(unittest.TestCase):
            # self.assertIsNotNone(tokenizer_p.__class__.from_pretrained('./'))
            self.assertIsNotNone(tokenizer_r.__class__.from_pretrained("./"))

+            # Check for space-only input
+            self.assert_empty_output_no_special_tokens(tokenizer_r.__class__, tokenizer_p.__class__, tokenizer_name)
+
    def test_distilbert(self):
        for tokenizer_name in DistilBertTokenizer.pretrained_vocab_files_map["vocab_file"].keys():
            tokenizer_p = DistilBertTokenizer.from_pretrained(tokenizer_name)
@@ -411,6 +441,9 @@ class FastTokenizerMatchingTest(unittest.TestCase):
            # Check for padding
            self.assert_padding(tokenizer_r, tokenizer_p)

+            # Check for space-only input
+            self.assert_empty_output_no_special_tokens(tokenizer_r.__class__, tokenizer_p.__class__, tokenizer_name)
+
    def test_gpt2(self):
        for tokenizer_name in GPT2Tokenizer.pretrained_vocab_files_map["vocab_file"].keys():
            tokenizer_p = GPT2Tokenizer.from_pretrained(tokenizer_name)
@@ -452,6 +485,9 @@ class FastTokenizerMatchingTest(unittest.TestCase):
            # Check for padding
            self.assertRaises(ValueError, self.assert_padding, tokenizer_r, tokenizer_p)

+            # Check for space-only input
+            self.assert_empty_output_no_special_tokens(tokenizer_r.__class__, tokenizer_p.__class__, tokenizer_name)
+
    def test_roberta(self):
        for tokenizer_name in RobertaTokenizer.pretrained_vocab_files_map["vocab_file"].keys():
            tokenizer_p = RobertaTokenizer.from_pretrained(tokenizer_name)
@@ -494,6 +530,9 @@ class FastTokenizerMatchingTest(unittest.TestCase):
            # TODO: Re-enable this test as soon as Roberta align with the python tokenizer.
            # self.assert_padding(tokenizer_r, tokenizer_p)

+            # Check for space-only input
+            self.assert_empty_output_no_special_tokens(tokenizer_r.__class__, tokenizer_p.__class__, tokenizer_name)
+
    def test_openai(self):
        for tokenizer_name in OpenAIGPTTokenizer.pretrained_vocab_files_map["vocab_file"].keys():
            tokenizer_p = OpenAIGPTTokenizer.from_pretrained(tokenizer_name)
@@ -536,3 +575,6 @@ class FastTokenizerMatchingTest(unittest.TestCase):

            # Check the number of returned files for save_vocabulary
            self.assert_save_pretrained(tokenizer_r, tokenizer_p)
+
+            # Check for space-only input
+            self.assert_empty_output_no_special_tokens(tokenizer_r.__class__, tokenizer_p.__class__, tokenizer_name)