Instructions to use ddrg/math_structure_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ddrg/math_structure_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ddrg/math_structure_bert")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ddrg/math_structure_bert") model = AutoModel.from_pretrained("ddrg/math_structure_bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| datasets: | |
| - ddrg/math_formulas | |
| - ddrg/math_formula_retrieval | |
| - ddrg/math_text | |
| - ddrg/named_math_formulas | |
| # Mathematical Structure Aware BERT | |
| <!-- Provide a quick summary of what the model is/does. --> | |
| Pretrained model based on [bert-base-cased](https://huggingface.co/bert-base-cased) with further mathematical pre-training. | |
| Compared to bert-base-cased, 300 additional mathematical [LaTeX tokens](added_tokens.json) have been added before the mathematical pre-training. |