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.
Input
text prompt, should include exactly one <mask> token
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Output
where is my father? (0.09)
where is my mother? (0.08)
