Instructions to use ddrg/math_structure_deberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ddrg/math_structure_deberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ddrg/math_structure_deberta")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ddrg/math_structure_deberta") model = AutoModelForMaskedLM.from_pretrained("ddrg/math_structure_deberta") - Notebooks
- Google Colab
- Kaggle
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Pretrained model based on [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) with further mathematical pre-training.
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Compared to deberta-v3-base, 300 additional mathematical LaTeX tokens have been added before the mathematical pre-training. As this additional pre-training used NSP-like tasks, a pooling layer has been added to the model (`bias` and `weight`). If you don't need this pooling layer, just use the standard transformers DeBERTa model. If you want to use the additional pooling layer like the BERT one, a wrapper class like the following may be used:
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datasets:
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- ddrg/named_math_formulas
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- ddrg/math_formula_retrieval
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- ddrg/math_formulas
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- ddrg/math_text
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Pretrained model based on [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) with further mathematical pre-training.
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Compared to deberta-v3-base, 300 additional mathematical LaTeX tokens have been added before the mathematical pre-training. As this additional pre-training used NSP-like tasks, a pooling layer has been added to the model (`bias` and `weight`). If you don't need this pooling layer, just use the standard transformers DeBERTa model. If you want to use the additional pooling layer like the BERT one, a wrapper class like the following may be used:
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