BERT

This model was released on 2018-10-11 and added to Hugging Face Transformers on 2020-11-16.

BERT

BERT is a bidirectional transformer pretrained on unlabeled text to predict masked tokens in a sentence and to predict whether one sentence follows another. The main idea is that by randomly masking some tokens, the model can train on text to the left and right, giving it a more thorough understanding. BERT is also very versatile because its learned language representations can be adapted for other NLP tasks by fine-tuning an additional layer or head.

You can find all the original BERT checkpoints under the BERT collection.

Tip

Click on the BERT models in the right sidebar for more examples of how to apply BERT to different language tasks.

The example below demonstrates how to predict the [MASK] token with Pipeline, AutoModel, and from the command line.

import torch
from transformers import pipeline

pipeline = pipeline(
    task="fill-mask",
    model="google-bert/bert-base-uncased",
    dtype=torch.float16,
    device=0
)
pipeline("Plants create [MASK] through a process known as photosynthesis.")

import torch
from transformers import AutoModelForMaskedLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained(
    "google-bert/bert-base-uncased",
)
model = AutoModelForMaskedLM.from_pretrained(
    "google-bert/bert-base-uncased",
    dtype=torch.float16,
    device_map="auto",
    attn_implementation="sdpa"
)
inputs = tokenizer("Plants create [MASK] through a process known as photosynthesis.", return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model(**inputs)
    predictions = outputs.logits

masked_index = torch.where(inputs['input_ids'] == tokenizer.mask_token_id)[1]
predicted_token_id = predictions[0, masked_index].argmax(dim=-1)
predicted_token = tokenizer.decode(predicted_token_id)

print(f"The predicted token is: {predicted_token}")

echo -e "Plants create [MASK] through a process known as photosynthesis." | transformers run --task fill-mask --model google-bert/bert-base-uncased --device 0

Notes

• Inputs should be padded on the right because BERT uses absolute position embeddings.

BertConfig

[[autodoc]] BertConfig - all

BertTokenizer

[[autodoc]] BertTokenizer - get_special_tokens_mask - save_vocabulary

BertTokenizerLegacy

[[autodoc]] BertTokenizerLegacy

BertTokenizerFast

[[autodoc]] BertTokenizerFast

BertModel

[[autodoc]] BertModel - forward

BertForPreTraining

[[autodoc]] BertForPreTraining - forward

BertLMHeadModel

[[autodoc]] BertLMHeadModel - forward

BertForMaskedLM

[[autodoc]] BertForMaskedLM - forward

BertForNextSentencePrediction

[[autodoc]] BertForNextSentencePrediction - forward

BertForSequenceClassification

[[autodoc]] BertForSequenceClassification - forward

BertForMultipleChoice

[[autodoc]] BertForMultipleChoice - forward

BertForTokenClassification

[[autodoc]] BertForTokenClassification - forward

BertForQuestionAnswering

[[autodoc]] BertForQuestionAnswering - forward

Bert specific outputs

[[autodoc]] models.bert.modeling_bert.BertForPreTrainingOutput