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camembert-base

We extract contextual embedding features from Camembert, a fill-mask language model, for the task of sentiment analysis. We use the tokenize and encode functions to convert our sentence into a numerical representation, and then feed it into the Camembert model to get the contextual embeddings. We extract the embeddings from all 12 self-attention layers and the input embedding layer to form a 13-dimensional feature vector for each sentence.

We extract contextual embedding features from Camembert, a fill-mask language model, for the task of sentiment analysis. We use the tokenize and encode functions to convert our sentence into a numerical representation, and then feed it into the Camembert model to get the contextual embeddings. We extract the embeddings from all 12 self-attention layers and the input embedding layer to form a 13-dimensional feature vector for each sentence.

Public
$0.0005 / sec

HTTP/cURL API

You can use cURL or any other http client to run inferences:

curl -X POST \
    -d '{"input": "Where is my <mask>?"}'  \
    -H "Authorization: bearer $(deepctl auth token)"  \
    -H 'Content-Type: application/json'  \
    'https://api.deepinfra.com/v1/inference/camembert-base'

which will give you back something similar to:

{
  "results": [
    {
      "sequence": "where is my father?",
      "score": 0.08898820728063583,
      "token": 2269,
      "token_str": "father"
    },
    {
      "sequence": "where is my mother?",
      "score": 0.07864926755428314,
      "token": 2388,
      "token_str": "mother"
    }
  ],
  "request_id": null,
  "inference_status": {
    "status": "unknown",
    "runtime_ms": 0,
    "cost": 0.0,
    "tokens_generated": 0,
    "tokens_input": 0
  }
}

Input fields

inputstring

text prompt, should include exactly one <mask> token


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