97 lines
3.4 KiB
Markdown
97 lines
3.4 KiB
Markdown
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<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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*This model was released on 2020-05-20 and added to Hugging Face Transformers on 2020-11-16.*
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# BERTweet
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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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</div>
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## BERTweet
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[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.
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You can find all the original BERTweet checkpoints under the [VinAI Research](https://huggingface.co/vinai?search_models=BERTweet) organization.
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> [!TIP]
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> Refer to the [BERT](./bert) docs for more examples of how to apply BERTweet to different language tasks.
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The example below demonstrates how to predict the `<mask>` token with [`Pipeline`], [`AutoModel`], and from the command line.
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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="vinai/bertweet-base",
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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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"vinai/bertweet-base",
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)
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model = AutoModelForMaskedLM.from_pretrained(
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"vinai/bertweet-base",
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dtype=torch.float16,
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device_map="auto"
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)
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inputs = tokenizer("Plants create <mask> through a process known as photosynthesis.", return_tensors="pt").to(model.device)
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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 run --task fill-mask --model vinai/bertweet-base --device 0
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```
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</hfoption>
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</hfoptions>
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## Notes
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- Use the [`AutoTokenizer`] or [`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.
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- Inputs should be padded on the right (`padding="max_length"`) because BERT uses absolute position embeddings.
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## BertweetTokenizer
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[[autodoc]] BertweetTokenizer
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