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--- |
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pipeline_tag: feature-extraction |
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--- |
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# Style Transformer for Authorship Representations - STAR |
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This is the repository for the [Style Transformer for Authorship Representations (STAR)](https://arxiv.org/abs/2310.11081) model. We present the weights of our model here. |
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Also check out our [github repo for STAR](https://github.com/jahuerta92/star) for replication. |
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## Feature extraction |
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```python |
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tokenizer = AutoTokenizer.from_pretrained('roberta-large') |
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model = AutoModel.from_pretrained('AIDA-UPM/star') |
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examples = ['My text 1', 'This is another text'] |
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def extract_embeddings(texts): |
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encoded_texts = tokenizer(texts) |
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with torch.no_grad(): |
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style_embeddings = model(encoded_texts.input_ids, |
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attention_mask=encoded_texts.attention_mask).pooler_output |
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return style_embeddings |
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print(extract_embeddings(examples)) |
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``` |
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## Citation |
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``` |
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@article{Huertas-Tato2023Oct, |
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author = {Huertas-Tato, Javier and Martin, Alejandro and Camacho, David}, |
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title = {{Understanding writing style in social media with a supervised contrastively pre-trained transformer}}, |
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journal = {arXiv}, |
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year = {2023}, |
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month = oct, |
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eprint = {2310.11081}, |
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doi = {10.48550/arXiv.2310.11081} |
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} |
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``` |