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9.2 KiB
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258 lines
9.2 KiB
Markdown
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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<div style="float: right;">
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<div class="flex flex-wrap space-x-1">
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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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">
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<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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</div>
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# BERT
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[BERT](https://huggingface.co/papers/1810.04805) 是一个在无标签的文本数据上预训练的双向 transformer，用于预测句子中被掩码的（masked） token，以及预测一个句子是否跟随在另一个句子之后。其主要思想是，在预训练过程中，通过随机掩码一些 token，让模型利用左右上下文的信息预测它们，从而获得更全面深入的理解。此外，BERT 具有很强的通用性，其学习到的语言表示可以通过额外的层或头进行微调，从而适配其他下游 NLP 任务。
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你可以在 [BERT](https://huggingface.co/collections/google/bert-release-64ff5e7a4be99045d1896dbc) 集合下找到 BERT 的所有原始 checkpoint。
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> [!TIP]
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> 点击右侧边栏中的 BERT 模型，以查看将 BERT 应用于不同语言任务的更多示例。
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下面的示例演示了如何使用 [`Pipeline`], [`AutoModel`] 和命令行预测 `[MASK]` token。
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<hfoptions id="usage">
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<hfoption id="Pipeline">
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```py
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import torch
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from transformers import pipeline
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pipeline = pipeline(
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task="fill-mask",
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model="google-bert/bert-base-uncased",
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torch_dtype=torch.float16,
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device=0
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)
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pipeline("Plants create [MASK] through a process known as photosynthesis.")
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```
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</hfoption>
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<hfoption id="AutoModel">
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```py
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import torch
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(
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"google-bert/bert-base-uncased",
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)
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model = AutoModelForMaskedLM.from_pretrained(
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"google-bert/bert-base-uncased",
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torch_dtype=torch.float16,
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device_map="auto",
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attn_implementation="sdpa"
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)
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inputs = tokenizer("Plants create [MASK] through a process known as photosynthesis.", return_tensors="pt").to("cuda")
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with torch.no_grad():
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outputs = model(**inputs)
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predictions = outputs.logits
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masked_index = torch.where(inputs['input_ids'] == tokenizer.mask_token_id)[1]
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predicted_token_id = predictions[0, masked_index].argmax(dim=-1)
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predicted_token = tokenizer.decode(predicted_token_id)
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print(f"The predicted token is: {predicted_token}")
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```
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</hfoption>
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<hfoption id="transformers-cli">
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```bash
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echo -e "Plants create [MASK] through a process known as photosynthesis." | transformers-cli run --task fill-mask --model google-bert/bert-base-uncased --device 0
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```
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</hfoption>
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</hfoptions>
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## 注意
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- 输入内容应在右侧进行填充，因为 BERT 使用绝对位置嵌入。
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## BertConfig
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[[autodoc]] BertConfig
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- all
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## BertTokenizer
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[[autodoc]] BertTokenizer
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- build_inputs_with_special_tokens
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- get_special_tokens_mask
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- create_token_type_ids_from_sequences
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- save_vocabulary
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## BertTokenizerFast
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[[autodoc]] BertTokenizerFast
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## BertModel
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[[autodoc]] BertModel
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- forward
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## BertForPreTraining
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[[autodoc]] BertForPreTraining
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- forward
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## BertLMHeadModel
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[[autodoc]] BertLMHeadModel
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- forward
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## BertForMaskedLM
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[[autodoc]] BertForMaskedLM
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- forward
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## BertForNextSentencePrediction
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[[autodoc]] BertForNextSentencePrediction
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- forward
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## BertForSequenceClassification
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[[autodoc]] BertForSequenceClassification
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- forward
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## BertForMultipleChoice
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[[autodoc]] BertForMultipleChoice
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- forward
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## BertForTokenClassification
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[[autodoc]] BertForTokenClassification
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- forward
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## BertForQuestionAnswering
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[[autodoc]] BertForQuestionAnswering
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- forward
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## TFBertTokenizer
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[[autodoc]] TFBertTokenizer
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## TFBertModel
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[[autodoc]] TFBertModel
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- call
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## TFBertForPreTraining
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[[autodoc]] TFBertForPreTraining
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- call
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## TFBertModelLMHeadModel
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[[autodoc]] TFBertLMHeadModel
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- call
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## TFBertForMaskedLM
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[[autodoc]] TFBertForMaskedLM
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- call
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## TFBertForNextSentencePrediction
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[[autodoc]] TFBertForNextSentencePrediction
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- call
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## TFBertForSequenceClassification
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[[autodoc]] TFBertForSequenceClassification
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- call
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## TFBertForMultipleChoice
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[[autodoc]] TFBertForMultipleChoice
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- call
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## TFBertForTokenClassification
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[[autodoc]] TFBertForTokenClassification
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- call
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## TFBertForQuestionAnswering
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[[autodoc]] TFBertForQuestionAnswering
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- call
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## FlaxBertModel
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[[autodoc]] FlaxBertModel
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- __call__
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## FlaxBertForPreTraining
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[[autodoc]] FlaxBertForPreTraining
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- __call__
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## FlaxBertForCausalLM
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[[autodoc]] FlaxBertForCausalLM
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- __call__
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## FlaxBertForMaskedLM
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[[autodoc]] FlaxBertForMaskedLM
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- __call__
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## FlaxBertForNextSentencePrediction
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[[autodoc]] FlaxBertForNextSentencePrediction
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- __call__
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## FlaxBertForSequenceClassification
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[[autodoc]] FlaxBertForSequenceClassification
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- __call__
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## FlaxBertForMultipleChoice
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[[autodoc]] FlaxBertForMultipleChoice
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- __call__
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## FlaxBertForTokenClassification
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[[autodoc]] FlaxBertForTokenClassification
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- __call__
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## FlaxBertForQuestionAnswering
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[[autodoc]] FlaxBertForQuestionAnswering
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- __call__
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## Bert specific outputs
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[[autodoc]] models.bert.modeling_bert.BertForPreTrainingOutput
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[[autodoc]] models.bert.modeling_tf_bert.TFBertForPreTrainingOutput
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[[autodoc]] models.bert.modeling_flax_bert.FlaxBertForPreTrainingOutput |