sultan/BioM-ELECTRA-Large-SQuAD2
We fine-tuned BioM-ELECTRA-Large, which was pre-trained on PubMed Abstracts, on the SQuAD2.0 dataset. Fine-tuning the biomedical language model on the SQuAD dataset helps improve the score on the BioASQ challenge. If you plan to work with BioASQ or biomedical QA tasks, it's better to use this model over BioM-ELECTRA-Large. This model (TensorFlow version) took the lead in the BioASQ9b-Factoid challenge (Batch 5) under the name of (UDEL-LAB2).
We fine-tuned BioM-ELECTRA-Large, which was pre-trained on PubMed Abstracts, on the SQuAD2.0 dataset. Fine-tuning the biomedical language model on the SQuAD dataset helps improve the score on the BioASQ challenge. If you plan to work with BioASQ or biomedical QA tasks, it's better to use this model over BioM-ELECTRA-Large. This model (TensorFlow version) took the lead in the BioASQ9b-Factoid challenge (Batch 5) under the name of (UDEL-LAB2).
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/sultan/BioM-ELECTRA-Large-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
webhookfile
The webhook to call when inference is done, by default you will get the output in the response of your inference request
