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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: 6 days 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 (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2018-12-09T03:19:53.000Z (almost 6 years ago)
- Last Synced: 2024-08-02T08:09:56.357Z (3 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]}}