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--- |
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language: "en" |
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tags: |
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- twitter |
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- masked-token-prediction |
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- bertweet |
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- election2020 |
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- politics |
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license: "gpl-3.0" |
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--- |
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# Pre-trained BERT on Twitter US Political Election 2020 |
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Pre-trained weights for PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter, LREC 2022. |
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Please see the [official repository](https://github.com/GU-DataLab/PoliBERTweet) for more detail. |
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We use the initialized weights from [BERTweet](https://huggingface.co/vinai/bertweet-base) or `vinai/bertweet-base`. |
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# Training Data |
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This model is pre-trained on over 83 million English tweets about the 2020 US Presidential Election. |
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# Training Objective |
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This model is initialized with BERTweet and trained with an MLM objective. |
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# Usage |
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This pre-trained language model **can be fine-tunned to any downstream task (e.g. classification)**. |
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```python |
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from transformers import AutoModel, AutoTokenizer, pipeline |
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import torch |
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# choose GPU if available |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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# select mode path here |
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pretrained_LM_path = "kornosk/polibertweet-mlm" |
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# load model |
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tokenizer = AutoTokenizer.from_pretrained(pretrained_LM_path) |
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model = AutoModel.from_pretrained(pretrained_LM_path) |
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# fill mask |
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example = "Trump is the <mask> of USA" |
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fill_mask = pipeline('fill-mask', model=pretrained_LM_path, tokenizer=tokenizer) |
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outputs = fill_mask(example) |
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print(outputs) |
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# see embeddings |
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inputs = tokenizer(example, return_tensors="pt") |
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outputs = model(**inputs) |
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print(outputs) |
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# OR you can use this model to train on your downstream task! |
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# please consider citing our paper if you feel this is useful :) |
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``` |
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# Reference |
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- [PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter](XXX), LREC 2022. |
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# Citation |
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```bibtex |
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@inproceedings{kawintiranon2022polibertweet, |
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title = {PoliBERTweet: A Pre-trained Language Model for Analyzing Political Content on Twitter}, |
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author = {Kawintiranon, Kornraphop and Singh, Lisa}, |
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booktitle = {Proceedings of the Language Resources and Evaluation Conference}, |
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year = {2022}, |
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publisher = {European Language Resources Association} |
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} |
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``` |
