pchatz

/

palobert-base-greek-social-media-sentiment-v2

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Model card implies it classifies text to positive - neutral - negative, but running

output = model(input_ids, attention_mask)

after preprocessing and tokenizing returns an object with the last hidden state and the last hidden state after pooling. I can't find the sentiment analysis classification head.

>>> output['last_hidden_state'].shape
torch.Size([1, 8, 768])
>>> output['pooler_output'].shape
torch.Size([1, 768])

Am I missing something?