Instructions to use approach0/dpr-cotbert-320 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use approach0/dpr-cotbert-320 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="approach0/dpr-cotbert-320")# Load model directly from transformers import AutoTokenizer, DprEncoder tokenizer = AutoTokenizer.from_pretrained("approach0/dpr-cotbert-320") model = DprEncoder.from_pretrained("approach0/dpr-cotbert-320") - Notebooks
- Google Colab
- Kaggle
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Browse files
README.md
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## About
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Here we share a pretrained BERT model that is aware of math tokens. The math tokens are treated specially and tokenized using [pya0](https://github.com/approach0/pya0), which adds very limited new tokens for latex markup (total vocabulary is just 31,061).
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language: en
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tags:
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- azbert
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license: mit
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---
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## About
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Here we share a pretrained BERT model that is aware of math tokens. The math tokens are treated specially and tokenized using [pya0](https://github.com/approach0/pya0), which adds very limited new tokens for latex markup (total vocabulary is just 31,061).
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