We present a BERT-based language model called bert-large-uncased-whole-word-masking-squad2, trained on the SQuAD2.0 dataset for extractive question answering. The model achieves high scores on exact match and F1 metrics.

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

```bash
curl -X POST \
    -d '{"question": "Who jumped?", "context": "The quick brown fox jumped over the lazy dog."}'  \
    -H "Authorization: bearer $(deepctl auth token)"  \
    -H 'Content-Type: application/json'  \
    'https://api.deepinfra.com/v1/inference/deepset/bert-large-uncased-whole-word-masking-squad2'
```

which will give you back something similar to:

```json
{
  "answer": "fox",
  "score": 0.1803228110074997,
  "start": 16,
  "end": 19,
  "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 'deepset/bert-large-uncased-whole-word-masking-squad2'  \
    -i 'question=Who jumped?'  \
    -i 'context=The quick brown fox jumped over the lazy dog.'
```

which will give you back something similar to:

```json
{
  "answer": "fox",
  "score": 0.1803228110074997,
  "start": 16,
  "end": 19,
  "request_id": null,
  "inference_status": {
    "status": "unknown",
    "runtime_ms": 0,
    "cost": 0.0,
    "tokens_generated": 0,
    "tokens_input": 0
  }
}

```


question

context

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

webhook

Context

Question

Webhook

QuestionAnsweringIn

estimated cost billed for the request in USD

Cost

Runtime Ms

Status

Tokens Generated

Tokens Input

Answer

the location in the context where the answer ends

Object containing the status of the inference request

Inference Status

Request Id

Score

the location in the context where the answer starts