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.
HTTP/cURL API
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
