huggingface--transformers/docs/source/en/model_doc/roberta.md

This model was published in HF papers on 2019-07-26 and contributed to Hugging Face Transformers on 2020-11-16.

RoBERTa

RoBERTa improves BERT with new pretraining objectives, demonstrating BERT was undertrained and training design is important. The pretraining objectives include dynamic masking, sentence packing, larger batches and a byte-level BPE tokenizer.

You can find all the original RoBERTa checkpoints under the Facebook AI organization.

Tip

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

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

from transformers import pipeline


pipeline = pipeline(
    task="fill-mask",
    model="FacebookAI/roberta-base",
    device=0
)
pipeline("Plants create <mask> through a process known as photosynthesis.")

import torch

from transformers import AutoModelForMaskedLM, AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained(
    "FacebookAI/roberta-base",
)
model = AutoModelForMaskedLM.from_pretrained(
    "FacebookAI/roberta-base",
    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}")

Notes

• RoBERTa doesn't have token_type_ids so you don't need to indicate which token belongs to which segment. Separate your segments with the separation token tokenizer.sep_token or </s>.

RobertaConfig

autodoc RobertaConfig

RobertaTokenizer

autodoc RobertaTokenizer - get_special_tokens_mask - save_vocabulary

RobertaTokenizerFast

autodoc RobertaTokenizerFast

RobertaModel

autodoc RobertaModel - forward

RobertaForCausalLM

autodoc RobertaForCausalLM - forward

RobertaForMaskedLM

autodoc RobertaForMaskedLM - forward

RobertaForSequenceClassification

autodoc RobertaForSequenceClassification - forward

RobertaForMultipleChoice

autodoc RobertaForMultipleChoice - forward

RobertaForTokenClassification

autodoc RobertaForTokenClassification - forward

RobertaForQuestionAnswering

autodoc RobertaForQuestionAnswering - forward