Add demo_camembert.py

examples/demo_camembert.py

+from pathlib import Path
+import tarfile
+import urllib.request
+
+import torch
+
+from transformers.tokenization_camembert import CamembertTokenizer
+from transformers.modeling_roberta import RobertaForMaskedLM
+
+
+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
+
+
+model_path = Path('camembert.v0.pytorch')
+if not model_path.exists():
+    compressed_path = model_path.with_suffix('.tar.gz')
+    url = 'http://dl.fbaipublicfiles.com/camembert/camembert.v0.pytorch.tar.gz'
+    print('Downloading model...')
+    urllib.request.urlretrieve(url, compressed_path)
+    print('Extracting model...')
+    with tarfile.open(compressed_path) as f:
+        f.extractall(model_path.parent)
+    assert model_path.exists()
+tokenizer_path = model_path / 'sentencepiece.bpe.model'
+tokenizer = CamembertTokenizer.from_pretrained(tokenizer_path)
+model = RobertaForMaskedLM.from_pretrained(model_path)
+model.eval()
+
+masked_input = "Le camembert est <mask> :)"
+print(fill_mask(masked_input, model, tokenizer, topk=3))