FinBERT is a pre-trained NLP model for financial sentiment analysis, built by fine-tuning the BERT language model on a large financial corpus. The model provides softmax outputs for three labels: positive, negative, or neutral.

You can use cURL or any other http client to run inferences:

```bash
curl -X POST \
    -d '{"input": "I had a wonderful day!"}'  \
    -H "Authorization: bearer $(deepctl auth token)"  \
    -H 'Content-Type: application/json'  \
    'https://api.deepinfra.com/v1/inference/ProsusAI/finbert'
```

which will give you back something similar to:

```json
{
  "results": [
    {
      "label": "POSITIVE",
      "score": 0.9998832941055298
    },
    {
      "label": "NEGATIVE",
      "score": 0.00011671594256768003
    }
  ],
  "request_id": null,
  "inference_status": {
    "status": "unknown",
    "runtime_ms": 0,
    "cost": 0.0,
    "tokens_generated": 0,
    "tokens_input": 0
  }
}

```


You can use our command-line tool [deepctl](/docs/getting-started) to run
inferences:

```bash
deepctl infer \
    -m 'ProsusAI/finbert'  \
    -i 'input=I had a wonderful day!'
```

which will give you back something similar to:

```json
{
  "results": [
    {
      "label": "POSITIVE",
      "score": 0.9998832941055298
    },
    {
      "label": "NEGATIVE",
      "score": 0.00011671594256768003
    }
  ],
  "request_id": null,
  "inference_status": {
    "status": "unknown",
    "runtime_ms": 0,
    "cost": 0.0,
    "tokens_generated": 0,
    "tokens_input": 0
  }
}

```


input

The webhook to call when inference is done, by default you will get the output in the response of your inference request

webhook

Input

Webhook

TextClassificationIn

Label

Score

Item

estimated cost billed for the request in USD

Cost

Runtime Ms

Status

Tokens Generated

Tokens Input

Object containing the status of the inference request

Inference Status

Request Id

a list of classifications, ordered by score