A transformer-based language model developed by Google Research that achieved state-of-the-art results on a wide range of NLP tasks. The model was pre-trained on a large corpus of English text, including BookCorpus and English Wikipedia, using a masked language modeling objective. Fine-tuned versions of the model are available for various downstream tasks, and the model has been shown to achieve excellent results on tasks such as question answering, sentiment analysis, and named entity recognition.

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

```bash
curl -X POST \
    -d '{"input": "Where is my [MASK]?"}'  \
    -H "Authorization: bearer $(deepctl auth token)"  \
    -H 'Content-Type: application/json'  \
    'https://api.deepinfra.com/v1/inference/bert-base-cased'
```

which will give you back something similar to:

```json
{
  "results": [
    {
      "sequence": "where is my father?",
      "score": 0.08898820728063583,
      "token": 2269,
      "token_str": "father"
    },
    {
      "sequence": "where is my mother?",
      "score": 0.07864926755428314,
      "token": 2388,
      "token_str": "mother"
    }
  ],
  "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 'bert-base-cased'  \
    -i 'input=Where is my [MASK]?'
```

which will give you back something similar to:

```json
{
  "results": [
    {
      "sequence": "where is my father?",
      "score": 0.08898820728063583,
      "token": 2269,
      "token_str": "father"
    },
    {
      "sequence": "where is my mother?",
      "score": 0.07864926755428314,
      "token": 2388,
      "token_str": "mother"
    }
  ],
  "request_id": null,
  "inference_status": {
    "status": "unknown",
    "runtime_ms": 0,
    "cost": 0.0,
    "tokens_generated": 0,
    "tokens_input": 0
  }
}

```


text prompt, should include exactly one [MASK] token

input

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

bert-base-cased

Input

Output