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AnnualBERTs
jd445/AnnualBERTs
AnnualBERTs is a machine learning model from jd445. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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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Updated Jun 14, 2024
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At a glance
- Access
- Public
- Created
- Mar 21, 2024
- Updated
- Jun 14, 2024
- SHA
- 470c1905
Model card
From the Hugging Face model README
Model Description
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.
Why ?arXivBERT
- Specialized in Scientific Content: Trained on a large dataset of arXiv papers, ensuring high familiarity with scientific terminology and concepts.
- 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.
- Evolutionary Insights: Continuous pre-training captures the long-term relationships and changes within the corpus.
How to Use?
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("folderPath/year")
model = AutoModel.from_pretrained("folderPath/wholewordtokenizer")
- Languages
- en
- Created
- Mar 21, 2024
- Updated
- Jun 14, 2024