daveminer / BERT-serv

FinBERT pre-trained models as a containerized HTTP service.

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|_______||_______||___|  |_|  |___|         |_______||_______||___|  |_|  |___|

BERT-serv provides FinBERT sentiment as a service. Send financial text via HTTP and get sentiment analysis back from these pretrained models.

❗ Only this sentiment model is implemented at this time.

Why

• FinBERT analysis made available on-demand to HTTP clients (no Python required!)
• Avoid adding PyTorch dependencies to other parts of your system
• Results are saved and can be queried
• Analysis requests are handled asynchronously

How to Run

Install Docker Desktop if needed.

git clone https://github.com/daveminer/BERT-serv.git
cd BERT-serv
cp .env.dist .env
docker-compose up

BERT-serv is now running on local port 8000!

How to Use

Request sentiment analysis on text

Send a POST request to the /sentiment/new/ path. A callback_url may be specified in the query parameters for asynchrous use cases and long-running sentiment analyses. This callback will have a JSON object in the body with an array of the new sentiment record ids: '{"ids": [95, 96]}'

The body of the POST request must be a list; the strings inside will be processed synchronously.

curl --request POST \
  --url 'http://localhost:8000/sentiment/new/?callback_url=http%3A%2F%2Fweb%3A8000%2Fcallback%2Fsentiment%2Fnew%2F' \
  --header 'Content-Type: application/json' \
  --data '[
	"there is a shortage of capital, and we need extra financing",
	"year over year growth is increasing"
]'

Look up sentiment results

Response defaults to HTML unless application/json is specified in the Accept header.

Get last 100 sentiments

Make a GET request to the /sentiment/ path.

curl --request GET \
  --url http://localhost:8000/sentiment/ \
  --header 'Accept: application/json'

Get a specific sentiment by index

Add the index of the sentiment resource to the sentiment path:

curl --request GET \
  --url http://localhost:8000/sentiment/1/ \
  --header 'Accept: application/json'