bert-large-uncased-whole-word-masking-finetuned-squad
A whole word masking model finetuned on SQuAD is a transformer-based language model pretrained on a large corpus of English data. The model was trained using a masked language modeling objective, where 15% of the tokens in a sentence were randomly masked, and the model had to predict the missing tokens. The model was also fine-tuned on the SQuAD dataset for question answering tasks, achieving high scores on both F1 and exact match metrics.
A whole word masking model finetuned on SQuAD is a transformer-based language model pretrained on a large corpus of English data. The model was trained using a masked language modeling objective, where 15% of the tokens in a sentence were randomly masked, and the model had to predict the missing tokens. The model was also fine-tuned on the SQuAD dataset for question answering tasks, achieving high scores on both F1 and exact match metrics.
HTTP/cURL API
You can use cURL or any other http client to run inferences:
curl -X POST \
-d '{"question": "Who jumped?", "context": "The quick brown fox jumped over the lazy dog."}' \
-H "Authorization: bearer $DEEPINFRA_TOKEN" \
-H 'Content-Type: application/json' \
'https://api.deepinfra.com/v1/inference/bert-large-uncased-whole-word-masking-finetuned-squad'
which will give you back something similar to:
{
"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
}
}
Input fields
webhookfile
The webhook to call when inference is done, by default you will get the output in the response of your inference request
