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Azbert Base
A pre-trained BERT model specifically designed for recognizing and processing mathematical symbols, using a special tokenization method for LaTeX markup.
Downloads 16
Release Time : 3/2/2022
Model Overview
This model can recognize mathematical symbols, uses [pya0] for tokenization, and adds a limited number of new tokens for LaTeX markup. Suitable for mathematical expression understanding and generation tasks.
Model Features
Mathematical symbol recognition
Optimized specifically for mathematical symbols, effectively recognizing and processing mathematical expressions in LaTeX format.
Special tokenization method
Uses [pya0] for tokenization, adding a limited number of new tokens for LaTeX markup, with a total vocabulary size of only 31,061.
Efficient training
Trained on 4 Tesla V100 GPUs with a total batch size of 64, using 2.7 million sentence pairs over 7 epochs.
Model Capabilities
Mathematical expression understanding
Mathematical expression generation
Masked language modeling prediction
Use Cases
Mathematics education
Mathematical expression completion
Automatically completes incomplete mathematical expressions, such as filling in missing operators or variables.
Accurately predicts missing parts in mathematical expressions.
Mathematical proof assistance
Assists in generating steps for mathematical proofs or providing proof ideas.
Generates reasonable proof steps to aid in understanding mathematical theorems.
Academic research
Mathematical paper writing assistance
Helps researchers quickly generate or complete formulas and expressions in mathematical papers.
Improves paper writing efficiency and reduces formula input errors.
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