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deepset/minilm-uncased-squad2

Microsoft's MiniLM-L12-H384-uncased language model achieved state-of-the-art results on the SQuAD 2.0 question-answering benchmark, with exact match and F1 scores of 76.13% and 79.54%, respectively. The model was trained on the SQuAD 2.0 dataset using a batch size of 12, learning rate of 4e-5, and 4 epochs. The authors suggest using their model as a starting point for building large language models for downstream NLP tasks.

Microsoft's MiniLM-L12-H384-uncased language model achieved state-of-the-art results on the SQuAD 2.0 question-answering benchmark, with exact match and F1 scores of 76.13% and 79.54%, respectively. The model was trained on the SQuAD 2.0 dataset using a batch size of 12, learning rate of 4e-5, and 4 epochs. The authors suggest using their model as a starting point for building large language models for downstream NLP tasks.

Public
$0.0005 / sec

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 $(deepctl auth token)"  \
    -H 'Content-Type: application/json'  \
    'https://api.deepinfra.com/v1/inference/deepset/minilm-uncased-squad2'

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

questionstring

question relating to context


contextstring

question source material


webhookfile

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

Input Schema

Output Schema