The RoBERTa model was pretrained on a dataset created by combining several sources including BookCorpus, English Wikipedia, CC-News, OpenWebText, and Stories. It uses a tokenization scheme with a vocabulary size of 50,000 and replaces 15% of the tokens with either a special masking token or a random token. The model achieved impressive results when fine-tuned on various downstream NLP tasks, outperforming its predecessor BERT in many areas.

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/roberta-base'
```

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 'roberta-base'  \
    -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

webhook

Input

Webhook

FillMaskIn

How good of a match is this, higher is better

Score

The full input sequence with mask filled in

Sequence

Token

Token Str

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 top suggestions, ordered by score