We present a sentence transformation model that generates semantically similar sentences. Our model is based on the Sentence-Transformers architecture and was trained on a large dataset of sentence pairs. We evaluate the effectiveness of our model by measuring its ability to generate similar sentences that are close to the original sentence in meaning.
We present a sentence transformation model that generates semantically similar sentences. Our model is based on the Sentence-Transformers architecture and was trained on a large dataset of sentence pairs. We evaluate the effectiveness of our model by measuring its ability to generate similar sentences that are close to the original sentence in meaning.
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/all-MiniLM-L12-v2'
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