transformers/pr_40546/en/model_doc/bertweet.md

# BERTweet

## BERTweet

[BERTweet](https://huggingface.co/papers/2005.10200) shares the same architecture as [BERT-base](./bert), but it's pretrained like [RoBERTa](./roberta) on English Tweets. It performs really well on Tweet-related tasks like part-of-speech tagging, named entity recognition, and text classification.

You can find all the original BERTweet checkpoints under the [VinAI Research](https://huggingface.co/vinai?search_models=BERTweet) organization.

> [!TIP]
> Refer to the [BERT](./bert) docs for more examples of how to apply BERTweet to different language tasks.

The example below demonstrates how to predict the `<mask>` token with [Pipeline](/docs/transformers/pr_40546/en/main_classes/pipelines#transformers.Pipeline), [AutoModel](/docs/transformers/pr_40546/en/model_doc/auto#transformers.AutoModel), and from the command line.

```python
from transformers import pipeline

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

```python
import torch

from transformers import AutoModelForMaskedLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained(
   "vinai/bertweet-base",
)
model = AutoModelForMaskedLM.from_pretrained(
    "vinai/bertweet-base",
    device_map="auto"
)
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

- Use the [AutoTokenizer](/docs/transformers/pr_40546/en/model_doc/auto#transformers.AutoTokenizer) or [BertweetTokenizer](/docs/transformers/pr_40546/en/model_doc/bertweet#transformers.BertweetTokenizer) because it's preloaded with a custom vocabulary adapted to tweet-specific tokens like hashtags (#), mentions (@), emojis, and common abbreviations. Make sure to also install the [emoji](https://pypi.org/project/emoji/) library.
- Inputs should be padded on the right (`padding="max_length"`) because BERT uses absolute position embeddings.

## BertweetTokenizer[[transformers.BertweetTokenizer]]

'"}, {"name": "eos_token", "val": " = ''"}, {"name": "sep_token", "val": " = ''"}, {"name": "cls_token", "val": " = ''"}, {"name": "unk_token", "val": " = ''"}, {"name": "pad_token", "val": " = ''"}, {"name": "mask_token", "val": " = ''"}, {"name": "**kwargs", "val": ""}]}>
- **vocab_file** (`str`) --
  Path to the vocabulary file.
- **merges_file** (`str`) --
  Path to the merges file.
- **normalization** (`bool`, *optional*, defaults to `False`) --
  Whether or not to apply a normalization preprocess.
- **bos_token** (`str`, *optional*, defaults to `"<s>"`) --
  The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.

  

  When building a sequence using special tokens, this is not the token that is used for the beginning of
  sequence. The token used is the `cls_token`.

  

- **eos_token** (`str`, *optional*, defaults to `"</s>"`) --
  The end of sequence token.

  

  When building a sequence using special tokens, this is not the token that is used for the end of sequence.
  The token used is the `sep_token`.

  

- **sep_token** (`str`, *optional*, defaults to `"</s>"`) --
  The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
  sequence classification or for a text and a question for question answering. It is also used as the last
  token of a sequence built with special tokens.
- **cls_token** (`str`, *optional*, defaults to `"<s>"`) --
  The classifier token which is used when doing sequence classification (classification of the whole sequence
  instead of per-token classification). It is the first token of the sequence when built with special tokens.
- **unk_token** (`str`, *optional*, defaults to `"<unk>"`) --
  The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
  token instead.
- **pad_token** (`str`, *optional*, defaults to `"<pad>"`) --
  The token used for padding, for example when batching sequences of different lengths.
- **mask_token** (`str`, *optional*, defaults to `"<mask>"`) --
  The token used for masking values. This is the token used when training this model with masked language
  modeling. This is the token which the model will try to predict.

Constructs a BERTweet tokenizer, using Byte-Pair-Encoding.

This tokenizer inherits from [PreTrainedTokenizer](/docs/transformers/pr_40546/en/main_classes/tokenizer#transformers.PythonBackend) which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.

Loads a pre-existing dictionary from a text file and adds its symbols to this instance.

Converts a sequence of tokens (string) in a single string.

Normalize tokens in a Tweet

Normalize a raw Tweet

Save the vocabulary and merges files to a directory.