We present a sentence transformation model that maps sentences and paragraphs to a 768-dimensional dense vector space, suitable for semantic search tasks. The model is trained on 215 million question-answer pairs from various sources, including WikiAnswers, PAQ, Stack Exchange, MS MARCO, GOOAQ, Amazon QA, Yahoo Answers, Search QA, ELI5, and Natural Questions. Our model uses a contrastive learning objective.
We present a sentence transformation model that maps sentences and paragraphs to a 768-dimensional dense vector space, suitable for semantic search tasks. The model is trained on 215 million question-answer pairs from various sources, including WikiAnswers, PAQ, Stack Exchange, MS MARCO, GOOAQ, Amazon QA, Yahoo Answers, Search QA, ELI5, and Natural Questions. Our model uses a contrastive learning objective.
You can use cURL or any other http client to run inferences:
curl -X POST \
-H "Authorization: bearer $DEEPINFRA_TOKEN" \
-F 'inputs=["I like chocolate"]' \
'https://api.deepinfra.com/v1/inference/sentence-transformers/multi-qa-mpnet-base-dot-v1'
which will give you back something similar to:
{
"embeddings": [
[
0.0,
0.5,
1.0
],
[
1.0,
0.5,
0.0
]
],
"input_tokens": 42,
"request_id": null,
"inference_status": {
"status": "unknown",
"runtime_ms": 0,
"cost": 0.0,
"tokens_generated": 0,
"tokens_input": 0
}
}
webhook
fileThe webhook to call when inference is done, by default you will get the output in the response of your inference request