tokenizer = AutoTokenizer.from_pretrained("distilbert-base-cased")
model = AutoModelForMaskedLM.from_pretrained("distilbert-base-cased")
sequence = (
...     "Distilled models are smaller than the models they mimic. Using them instead of the large "
...     f"versions would help {tokenizer.mask_token} our carbon footprint."
... )
inputs = tokenizer(sequence, return_tensors="pt")
mask_token_index = torch.where(inputs["input_ids"] == tokenizer.mask_token_id)[1]
token_logits = model(**inputs).logits
mask_token_logits = token_logits[0, mask_token_index, :]
top_5_tokens = torch.topk(mask_token_logits, 5, dim=1).indices[0].tolist()
for token in top_5_tokens:
...     print(sequence.replace(tokenizer.mask_token, tokenizer.decode([token])))
Distilled models are smaller than the models they mimic. Using them instead of the large versions would help reduce our carbon footprint.
