DistilBERT-base-uncased-finetuned-sst-2-english achieved an accuracy of 0.91 on the glue dataset, with a loss of 0.39 and an F1 score of 0.91. The model was trained on the sst2 dataset with a configuration of default and a split of train.

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/distilbert-base-uncased-finetuned-sst-2-english'
```

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 'distilbert-base-uncased-finetuned-sst-2-english'  \
    -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

distilbert-base-uncased-finetuned-sst-2-english

Input

Output