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dpr-ctx_encoder-fr_qa-camembert

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It seems that the example given in the readme does not work as expected. The pooler weights of cammenBERT are randomly initialised while there are in use during the inference process (using pooler_output). If the model is expected to work as DPRContextEncoder without any "trained" pooler (thus pooling is the output embedding of the first token) the code below shoulds work:

from transformers import AutoTokenizer, AutoModel

query = "Salut, mon chien est-il mignon ?"
tokenizer = AutoTokenizer.from_pretrained("etalab-ia/dpr-ctx_encoder-fr_qa-camembert",  do_lower_case=True)
input_ids = tokenizer(query, return_tensors='pt')["input_ids"]
model = AutoModel.from_pretrained("etalab-ia/dpr-ctx_encoder-fr_qa-camembert", return_dict=True)
embeddings = output.last_hidden_state[:,0,:]
print(embeddings)
``