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Add the support for more data set #21

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240 changes: 159 additions & 81 deletions BERT_NER.py

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45 changes: 26 additions & 19 deletions README.md
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Expand Up @@ -10,37 +10,44 @@ Second <code>download file in this project</code>

BERT
|____ bert
|____ NERdata
|____ BERT_NER.py
|____ checkpoint
|____ output


Third run:
```
python BERT_NER.py \
--task_name="NER" \
--do_train=True \
--do_eval=True \
--do_predict=True
--data_dir=NERdata \
--vocab_file=checkpoint/vocab.txt \
--bert_config_file=checkpoint/bert_config.json \
--init_checkpoint=checkpoint/bert_model.ckpt \
--max_seq_length=128 \
--train_batch_size=32 \
--learning_rate=2e-5 \
--num_train_epochs=3.0 \
--output_dir=./output/result_dir/
python3 BERT_NER.py \
--task_name="NER" \
--do_train=True \
--do_eval=True \
--do_predict=True \
--data_dir=NERdata/en \
--column_sep=" " \
--vocab_file=../BERT-BiLSTM-CRF-NER/uncased_L-12_H-768_A-12/vocab.txt \
--bert_config_file=../BERT-BiLSTM-CRF-NER/uncased_L-12_H-768_A-12/bert_config.json \
--init_checkpoint=../BERT-BiLSTM-CRF-NER/uncased_L-12_H-768_A-12/bert_model.ckpt \
--max_seq_length=32 \
--train_batch_size=16 \
--learning_rate=2e-5 \
--num_train_epochs=6 \
--save_checkpoints_steps=1000 \
--output_dir=./output_en/result_dir/
```

result:

The predicted result is placed in folder <code>./output/result_dir/</code>. It contain two files, <code>token_test.txt</code> is the tokens and <code>label_test.txt</code> is the labels for each token. If you want a more accurate evaluation result you can use script conlleval.pl for evaluation.
The predicted result is placed in folder <code>./output/result_dir/</code>. It is in the file <code>predicted_results.tx</code>.

The following evaluation results differ from the evaluation results specified by conll2003.

![](/picture2.png)
The following evaluation results come from script conlleval.pl.

```
accuracy: 98.09%; precision: 90.01%; recall: 91.51%; FB1: 90.76
LOC: precision: 91.81%; recall: 93.58%; FB1: 92.69 1698
MISC: precision: 75.73%; recall: 81.34%; FB1: 78.43 754
ORG: precision: 88.59%; recall: 89.70%; FB1: 89.14 1682
PER: precision: 96.62%; recall: 95.67%; FB1: 96.14 1599
```

#### 注:For the parameters of the above model, I have not made any modifications. All parameters are based on the BERT default parameters. The better parameters for this problem can be adjusted by yourselves.

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31 changes: 31 additions & 0 deletions bert/CONTRIBUTING.md
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# How to Contribute

BERT needs to maintain permanent compatibility with the pre-trained model files,
so we do not plan to make any major changes to this library (other than what was
promised in the README). However, we can accept small patches related to
re-factoring and documentation. To submit contributes, there are just a few
small guidelines you need to follow.

## Contributor License Agreement

Contributions to this project must be accompanied by a Contributor License
Agreement. You (or your employer) retain the copyright to your contribution;
this simply gives us permission to use and redistribute your contributions as
part of the project. Head over to <https://cla.developers.google.com/> to see
your current agreements on file or to sign a new one.

You generally only need to submit a CLA once, so if you've already submitted one
(even if it was for a different project), you probably don't need to do it
again.

## Code reviews

All submissions, including submissions by project members, require review. We
use GitHub pull requests for this purpose. Consult
[GitHub Help](https://help.github.com/articles/about-pull-requests/) for more
information on using pull requests.

## Community Guidelines

This project follows
[Google's Open Source Community Guidelines](https://opensource.google.com/conduct/).
202 changes: 202 additions & 0 deletions bert/LICENSE
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