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https://github.com/Kyubyong/bert_ner
Ner with Bert
https://github.com/Kyubyong/bert_ner
bert bert-model named-entity-recognition ner
Last synced: 6 days ago
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Ner with Bert
- Host: GitHub
- URL: https://github.com/Kyubyong/bert_ner
- Owner: Kyubyong
- Created: 2019-02-24T11:47:55.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2019-10-20T02:50:01.000Z (about 5 years ago)
- Last Synced: 2024-08-02T08:09:59.901Z (3 months ago)
- Topics: bert, bert-model, named-entity-recognition, ner
- Language: Python
- Homepage:
- Size: 484 KB
- Stars: 281
- Watchers: 10
- Forks: 56
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-bert - Kyubyong/bert_ner
README
# PyTorch Implementation of NER with pretrained Bert
I know that you know [BERT](https://arxiv.org/abs/1810.04805).
In the great paper, the authors claim that the pretrained models do great in NER.
It's even impressive, allowing for the fact that they don't use any prediction-conditioned algorithms like CRFs.
We try to reproduce the result in a simple manner.
## Requirements
* python>=3.6 (Let's move on to python 3 if you still use python 2)
* pytorch==1.0
* pytorch_pretrained_bert==0.6.1
* numpy>=1.15.4
## Training & Evaluating
* STEP 1. Run the command below to download conll 2003 NER dataset.
```
bash download.sh
```
It should be extracted to `conll2003/` folder automatically.
* STEP 2a. Run the command if you want to do the feature-based approach.
```
python train.py --logdir checkpoints/feature --batch_size 128 --top_rnns --lr 1e-4 --n_epochs 30
```
* STEP 2b. Run the command if you want to do the fine-tuning approach.
```
python train.py --logdir checkpoints/finetuning --finetuning --batch_size 32 --lr 5e-5 --n_epochs 3
```
## Results in the paper
* Feature-based approach
* Fine-tuning
## Results
* F1 scores on conll2003 valid dataset are reported.
* You can check the classification outputs in [checkpoints](checkpoints).
|epoch|feature-based|fine-tuning|
|--|--|--|
|1|0.2|0.95|
|2|0.75|0.95|
|3|0.84|0.96|
|4|0.88|
|5|0.89|
|6|0.90|
|7|0.90|
|8|0.91|
|9|0.91|
|10|0.92|
|11|0.92|
|12|0.93|
|13|0.93|
|14|0.93|
|15|0.93|
|16|0.92|
|17|0.93|
|18|0.93|
|19|0.93|
|20|0.93|
|21|**0.94**|
|22|**0.94**|
|23|0.93|
|24|0.93|
|25|0.93|
|26|0.93|
|27|0.93|
|28|0.93|
|29|**0.94**|
|30|0.93|