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--- |
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language: |
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- en |
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--- |
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## Model Description |
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arXivBERT is a series of models trained on a time-based unit. If you are looking for the best performance on scientific corpora, please use the model from 2020 directly. |
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## Why ?arXivBERT |
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1. Specialized in Scientific Content: Trained on a large dataset of arXiv papers, ensuring high familiarity with scientific terminology and concepts. |
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2. Versatile in Applications: Suitable for a range of NLP tasks, including but not limited to text classification, keyword extraction, summarization of scientific papers, and citation prediction. |
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3. Evolutionary Insights: Continuous pre-training captures the long-term relationships and changes within the corpus. |
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## How to Use? |
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``` |
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from transformers import AutoTokenizer, AutoModel |
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tokenizer = AutoTokenizer.from_pretrained("folderPath/year") |
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model = AutoModel.from_pretrained("folderPath/wholewordtokenizer") |
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``` |
