https://github.com/lynnna-xu/bert_sa

bert sentiment analysis tensorflow serving with RESTful API
https://github.com/lynnna-xu/bert_sa

bert rest-client restful-api sentiment-analysis tensorflow-serving

Last synced: about 1 month ago
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bert sentiment analysis tensorflow serving with RESTful API

• Host: GitHub
• URL: https://github.com/lynnna-xu/bert_sa
• Owner: lynnna-xu
• Created: 2018-12-08T03:22:35.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2018-12-09T03:19:53.000Z (about 6 years ago)
• Last Synced: 2024-08-02T08:09:56.357Z (4 months ago)
• Topics: bert, rest-client, restful-api, sentiment-analysis, tensorflow-serving
• Language: Python
• Homepage:
• Size: 23.4 KB
• Stars: 33
• Watchers: 3
• Forks: 12
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-bert - lynnna-xu/bert_sa

README

        
# bert_sa (bert sentiment analysis tensorflow serving with RESTful API)

based on [bert](https://github.com/google-research/bert/blob/master/multilingual.md) including training, online predicting and serving with [REST](https://www.tensorflow.org/serving/api_rest) 

## Fine tune a sentiment analysis model based on [BERT](https://github.com/google-research/bert)

1. Add a `SAProcessor` and include it within `main` function in run_classifier.py 

2. Prepare train, dev and test files; adapat `_create_examples` method in `SAProcessor` based on your own datasets (pandas may not be required)

3. Specify `BERT_BASE_DIR`, `SA_DIR` and `output_dir` in run_sa.sh and run 

## Test

1. For file based test, change `output_predict_file` in run_classifier.py, specify `TRAINED_CLASSIFIER` and `output_dir` path, run predict_sa.sh 

2. For online prediction, refer to run_classifier_predict_online (modified based on [bert_language_understanding](https://github.com/brightmart/bert_language_understanding))

## Export your model

Refer to sa_predict_saved_model.py

**KIND NOTICE:** some graph definition and input placeholder is imported from run_classifier_predict_online.py

## Serve the model with TensorFlow Serving

1. See [TensorFlow Serving](https://www.tensorflow.org/serving/docker) for details about installing docker and pulling a serving image

2. Running a serving image

```Bash

docker run -p 8501:8501 --name 'bert_sa_serving' --mount type=bind,source=/data/notebooks/xff/bert/output/sa_output/saved_model,target=/models/bert_sa -e MODEL_NAME=bert_sa -t tensorflow/serving:latest-devel-gpu &

docker exec -it bert_sa_serving bash

tensorflow_model_server --port=8500 --rest_api_port=8501 \

  --model_name=bert_sa --model_base_path=/models/bert_sa

 ```

3. Sample request

```Python

line=u'建立了完善的质量体系并持续有效运行'

# preprocess is defined in run_classifier_predict_online.py

dict_data = preprocess(line)

resp = requests.post('http://172.17.0.1:8501/v1/models/bert_sa:predict', json=dict_data)

print(resp.json())

```

Results look like this:

{'outputs': {'label_predict': 1, 'possibility': [0.00738544, 0.992615]}}