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#!/usr/bin/env python
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def fill_mask(masked_input, model, tokenizer, topk=5):
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert masked_input.count("<mask>") == 1
input_ids = torch.tensor(tokenizer.encode(masked_input, add_special_tokens=True)).unsqueeze(0) # Batch size 1
logits = model(input_ids)[0] # The last hidden-state is the first element of the output tuple
masked_index = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
logits = logits[0, masked_index, :]
prob = logits.softmax(dim=0)
values, indices = prob.topk(k=topk, dim=0)
topk_predicted_token_bpe = " ".join(
[tokenizer.convert_ids_to_tokens(indices[i].item()) for i in range(len(indices))]
)
masked_token = tokenizer.mask_token
topk_filled_outputs = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" ")):
predicted_token = predicted_token_bpe.replace("\u2581", " ")
if " {0}".format(masked_token) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(" {0}".format(masked_token), predicted_token),
values[index].item(),
predicted_token,
)
)
else:
topk_filled_outputs.append(
(
masked_input.replace(masked_token, predicted_token),
values[index].item(),
predicted_token,
)
)
return topk_filled_outputs
tokenizer = CamembertTokenizer.from_pretrained("camembert-base")
model = CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()
masked_input = "Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))
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