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remove misplaced summarization documentation

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Rémi Louf
2019-12-10 14:49:53 +01:00
committed by Julien Chaumond
parent e57d00ee10
commit 4b82c485de
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examples/README.md
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@@ -24,8 +24,6 @@ pip install -r ./examples/requirements.txt
| [Multiple Choice](#multiple-choice) | Examples running BERT/XLNet/RoBERTa on the SWAG/RACE/ARC tasks. | [Multiple Choice](#multiple-choice) | Examples running BERT/XLNet/RoBERTa on the SWAG/RACE/ARC tasks.
| [Named Entity Recognition](#named-entity-recognition) | Using BERT for Named Entity Recognition (NER) on the CoNLL 2003 dataset, examples with distributed training. | | [Named Entity Recognition](#named-entity-recognition) | Using BERT for Named Entity Recognition (NER) on the CoNLL 2003 dataset, examples with distributed training. |
| [XNLI](#xnli) | Examples running BERT/XLM on the XNLI benchmark. | | [XNLI](#xnli) | Examples running BERT/XLM on the XNLI benchmark. |
| [Abstractive summarization](#abstractive-summarization) | Using the BertAbs
model finetuned on the CNN/DailyMail dataset to generate summaries. |
## TensorFlow 2.0 Bert models on GLUE ## TensorFlow 2.0 Bert models on GLUE
@@ -646,34 +644,6 @@ micro avg 0.8722 0.8774 0.8748 13869
macro avg 0.8712 0.8774 0.8740 13869 macro avg 0.8712 0.8774 0.8740 13869
``` ```
## Abstractive summarization
Based on the script
[`run_summarization_finetuning.py`](https://github.com/huggingface/transformers/blob/master/examples/run_summarization_finetuning.py).
Before running this script you should 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
tar -xvf cnn_stories.tgz && tar -xvf dailymail_stories.tgz
```
note that the finetuning script **will not work** if you do not download both
datasets. We will refer as `$DATA_PATH` the path to where you uncompressed both
archive.
```bash
export DATA_PATH=/path/to/dataset/
python run_summarization_finetuning.py \
--output_dir=output \
--model_type=bert2bert \
--model_name_or_path=bert2bert \
--do_train \
--data_path=$DATA_PATH \
```
## XNLI ## XNLI
Based on the script [`run_xnli.py`](https://github.com/huggingface/transformers/blob/master/examples/run_xnli.py). Based on the script [`run_xnli.py`](https://github.com/huggingface/transformers/blob/master/examples/run_xnli.py).