946400fb68
* Expand a bit the presentation of examples * Apply suggestions from code review Co-authored-by: Stas Bekman <stas00@users.noreply.github.com> * Address review comments Co-authored-by: Stas Bekman <stas00@users.noreply.github.com>
68 lines
2.1 KiB
Markdown
68 lines
2.1 KiB
Markdown
<!---
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Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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-->
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## Multiple Choice
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Based on the script [`run_swag.py`]().
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## PyTorch script: fine-tuning on SWAG
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`run_swag` allows you to fine-tune any model from our [hub](https://huggingface.co/models) (as long as its architecture as a `ForMultipleChoice` version in the library) on the SWAG dataset or your own csv/jsonlines files as long as they are structured the same way. To make it works on another dataset, you will need to tweak the `preprocess_function` inside the script.
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```bash
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python examples/multiple-choice/run_swag.py \
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--model_name_or_path roberta-base \
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--do_train \
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--do_eval \
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--learning_rate 5e-5 \
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--num_train_epochs 3 \
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--output_dir /tmp/swag_base \
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--per_gpu_eval_batch_size=16 \
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--per_device_train_batch_size=16 \
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--overwrite_output
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```
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Training with the defined hyper-parameters yields the following results:
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```
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***** Eval results *****
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eval_acc = 0.8338998300509847
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eval_loss = 0.44457291918821606
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```
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## Tensorflow
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```bash
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export SWAG_DIR=/path/to/swag_data_dir
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python ./examples/multiple-choice/run_tf_multiple_choice.py \
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--task_name swag \
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--model_name_or_path bert-base-cased \
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--do_train \
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--do_eval \
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--data_dir $SWAG_DIR \
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--learning_rate 5e-5 \
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--num_train_epochs 3 \
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--max_seq_length 80 \
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--output_dir models_bert/swag_base \
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--per_gpu_eval_batch_size=16 \
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--per_device_train_batch_size=16 \
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--logging-dir logs \
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--gradient_accumulation_steps 2 \
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--overwrite_output
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```
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# Run it in colab
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[](https://colab.research.google.com/github/ViktorAlm/notebooks/blob/master/MPC_GPU_Demo_for_TF_and_PT.ipynb)
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