ddrg/math_structure_bert

⚠️ Note: This model has been re-uploaded to a new organization as part of a consolidated collection. The updated version is available at https://huggingface.co/aieng-lab/bert-base-cased-mamut. Please refer to the new repository for future updates, documentation, and related models.

MAMUT Bert (Mathematical Structure Aware BERT)

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Pretrained model based on bert-base-cased with further mathematical pre-training, introduced in MAMUT: A Novel Framework for Modifying Mathematical Formulas for the Generation of Specialized Datasets for Language Model Training.

Model Details

Model Description

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This model has been mathematically pretrained based on four tasks/datasets:

• —[Mathematical Formulas (MF)](https://huggingface.co/datasets/ddrg/math_formulas): Masked Language Modeling (MLM) task on math formulas written in LaTeX
• —[Mathematical Texts (MT)](https://huggingface.co/datasets/ddrg/math_text): MLM task on mathematical texts (i.e., texts containing LaTeX formulas). The masked tokens are more likely to be a one of the formula tokens or mathematical words (e.g., sum, one, ...)
• —[Named Math Formulas (NMF)](https://huggingface.co/datasets/ddrg/named_math_formulas): Next-Sentence-Prediction (NSP)-like task associating a name of a well known mathematical identity (e.g., Pythagorean Theorem) with a formula representation (and the task is to classify whether the formula matches the identity described by the name)
• —[Math Formula Retrieval (MFR)](https://huggingface.co/datasets/ddrg/math_formula_retrieval): NSP-like task associating two formulas (and the task is to decide whether both describe the same mathematical concept(identity))

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Compared to bert-base-cased, 300 additional mathematical LaTeX tokens have been added before the mathematical pre-training.

• —Further pretrained from model: bert-base-cased

Model Sources [optional]

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• —Repository: aieng-lab/transformer-math-pretraining](https://github.com/aieng-lab/transformer-math-pretraining)
• —Paper: MAMUT: A Novel Framework for Modifying Mathematical Formulas for the Generation of Specialized Datasets for Language Model Training

Uses

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How to Get Started with the Model

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Training Details

Training Data

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Training Procedure

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Environmental Impact

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• —Hardware Type: 8xA100
• —Hours used: 48
• —Compute Region: Germany

Citation

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BibTeX:

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