huggingface-transformers

来源： https://stackoverflow.com/questions/63218778/fine-tuning-distilbertforsequenceclassification-is-not-learning-why-is-loss-no

来源： https://stackoverflow.com/questions/62703391/estimate-token-probability-logits-given-a-sentence-without-computing-the-entire

来源： https://stackoverflow.com/questions/62703391/estimate-token-probability-logits-given-a-sentence-without-computing-the-entire

来源： https://stackoverflow.com/questions/63312859/how-to-change-huggingface-transformers-default-cache-directory

来源： https://stackoverflow.com/questions/63244792/the-size-of-tensor-a-2-must-match-the-size-of-tensor-b-39-at-non-singleton-d

来源： https://stackoverflow.com/questions/61798573/where-does-hugging-faces-transformers-save-models

来源： https://stackoverflow.com/questions/61798573/where-does-hugging-faces-transformers-save-models

来源： https://stackoverflow.com/questions/62691279/how-to-disable-tokenizers-parallelism-true-false-warning

来源： https://stackoverflow.com/questions/60876394/does-bertforsequenceclassification-classify-on-the-cls-vector

问题 I am using Huggingface's transformers library and want to perform NER using BERT. I tried to find an explicit example of how to properly format the data for NER using BERT. It is not entirely clear to me from the paper and the comments I've found. Let's say we have a following sentence and labels: sent = "John Johanson lives in Ramat Gan." labels = ['B-PER', 'I-PER', 'O', 'O', 'B-LOC', 'I-LOC'] Would data that we input to the model be something like this: sent = ['[CLS]', 'john', 'johan', '#