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https://github.com/curiousily/Deploy-BERT-for-Sentiment-Analysis-with-FastAPI

Deploy BERT for Sentiment Analysis as REST API using FastAPI, Transformers by Hugging Face and PyTorch
https://github.com/curiousily/Deploy-BERT-for-Sentiment-Analysis-with-FastAPI

bert deep-learning deploy-machine-learning deployment fastapi huggingface huggingface-transformer machine-learning python pytorch rest rest-api sentiment-analysis transformers uvicorn

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Deploy BERT for Sentiment Analysis as REST API using FastAPI, Transformers by Hugging Face and PyTorch

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# Deploy BERT for Sentiment Analsysi with FastAPI

Deploy a pre-trained BERT model for Sentiment Analysis as a REST API using FastAPI

## Demo

The model is trained to classify sentiment (negative, neutral, and positive) on a custom dataset from app reviews on Google Play. Here's a sample request to the API:

```bash
http POST http://127.0.0.1:8000/predict text="Good basic lists, i would like to create more lists, but the annual fee for unlimited lists is too out there"
```

The response you'll get looks something like this:

```js
{
"confidence": 0.9999083280563354,
"probabilities": {
"negative": 3.563107020454481e-05,
"neutral": 0.9999083280563354,
"positive": 5.596495248028077e-05
},
"sentiment": "neutral"
}
```

You can also [read the complete tutorial here](https://www.curiousily.com/posts/deploy-bert-for-sentiment-analysis-as-rest-api-using-pytorch-transformers-by-hugging-face-and-fastapi/)

## Installation

Clone this repo:

```sh
git clone [email protected]:curiousily/Deploy-BERT-for-Sentiment-Analysis-with-FastAPI.git
cd Deploy-BERT-for-Sentiment-Analysis-with-FastAPI
```

Install the dependencies:

```sh
pipenv install --dev
```

Download the pre-trained model:

```sh
bin/download_model
```

## Test the setup

Start the HTTP server:

```sh
bin/start_server
```

Send a test request:

```sh
bin/test_request
```

## License

MIT