Hong Xu
501040fd30
Otherwise, if label is not specified, the following error occurs: Traceback (most recent call last): File "run_ner.py", line 303, in <module> main() File "run_ner.py", line 101, in main model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) File "/home/user/anaconda3/envs/bert/lib/python3.7/site-packages/transformers/hf_argparser.py", line 159, in parse_json_file obj = dtype(**inputs) TypeError: __init__() missing 1 required positional argument: 'labels'
Examples
Version 2.9 of 🤗 Transformers introduces a new Trainer class for PyTorch, and its equivalent TFTrainer for TF 2.
Running the examples requires PyTorch 1.3.1+ or TensorFlow 2.1+.
Here is the list of all our examples:
- grouped by task (all official examples work for multiple models)
- with information on whether they are built on top of
Trainer/TFTrainer(if not, they still work, they might just lack some features), - whether they also include examples for
pytorch-lightning, which is a great fully-featured, general-purpose training library for PyTorch, - links to Colab notebooks to walk through the scripts and run them easily,
- links to Cloud deployments to be able to deploy large-scale trainings in the Cloud with little to no setup.
This is still a work-in-progress – in particular documentation is still sparse – so please contribute improvements/pull requests.
The Big Table of Tasks
| Task | Example datasets | Trainer support | TFTrainer support | pytorch-lightning | Colab |
|---|---|---|---|---|---|
language-modeling |
Raw text | ✅ | - | - |  |
text-classification |
GLUE, XNLI | ✅ | ✅ | ✅ | |
token-classification |
CoNLL NER | ✅ | ✅ | ✅ | - |
multiple-choice |
SWAG, RACE, ARC | ✅ | ✅ | - | |
question-answering |
SQuAD | - | ✅ | - | - |
text-generation |
- | n/a | n/a | n/a | |
distillation |
All | - | - | - | - |
summarization |
CNN/Daily Mail | - | - | ✅ | - |
translation |
WMT | - | - | ✅ | - |
bertology |
- | - | - | - | - |
adversarial |
HANS | ✅ | - | - | - |
Important note
Important To make sure you can successfully run the latest versions of the example scripts, you have to install the library from source and install some example-specific requirements. Execute the following steps in a new virtual environment:
git clone https://github.com/huggingface/transformers
cd transformers
pip install .
pip install -r ./examples/requirements.txt
One-click Deploy to Cloud (wip)
Azure
Running on TPUs
When using Tensorflow, TPUs are supported out of the box as a tf.distribute.Strategy.
When using PyTorch, we support TPUs thanks to pytorch/xla. For more context and information on how to setup your TPU environment refer to Google's documentation and to the
very detailed pytorch/xla README.
In this repo, we provide a very simple launcher script named xla_spawn.py that lets you run our example scripts on multiple TPU cores without any boilerplate.
Just pass a --num_cores flag to this script, then your regular training script with its arguments (this is similar to the torch.distributed.launch helper for torch.distributed).
For example for run_glue:
python examples/xla_spawn.py --num_cores 8 \
examples/text-classification/run_glue.py
--model_name_or_path bert-base-cased \
--task_name mnli \
--data_dir ./data/glue_data/MNLI \
--output_dir ./models/tpu \
--overwrite_output_dir \
--do_train \
--do_eval \
--num_train_epochs 1 \
--save_steps 20000
Feedback and more use cases and benchmarks involving TPUs are welcome, please share with the community.