| --- |
| pipeline_tag: sentence-similarity |
| tags: |
| - formula-transformers |
| - feature-extraction |
| - formula-similarity |
|
|
| --- |
| |
| # CLFE(ConMath) |
|
|
| This is a formula embedding model trained on Latex, Presentation MathML and Content MathML of formulas: It maps formulas to a 768 dimensional dense vector space. It was introduced in https://link.springer.com/chapter/10.1007/978-981-99-7254-8_8 |
| |
| <!--- Describe your model here --> |
| |
| ## Usage |
| |
| |
| ``` |
| pip install -U sentence-transformers |
| ``` |
| Put 'MarkuplmTransformerForConMATH.py' into 'sentence_transfomers/models', and add 'from .MarkuplmTransformerForConMATH import MarkuplmTransformerForConMATH' into 'sentence_transfomers/models/\_init\_' |
| |
| Then you can use the model like this: |
| |
| ```python |
| from sentence_transformers import SentenceTransformer |
| latex = r"13\times x" |
| pmml = r"<math><semantics><mrow><mn>13</mn><mo>×</mo><mi>x</mi></mrow></semantics></math>" |
| cmml = r"<math><apply><times></times><cn>13</cn><ci>x</ci></apply></math>" |
|
|
| model = SentenceTransformer('Jyiyiyiyi/CLFE_ConMath') |
| |
| embedding_latex = model.encode([{'latex': latex}]) |
| embedding_pmml = model.encode([{'mathml': pmml}]) |
| embedding_cmml = model.encode([{'mathml': cmml}]) |
|
|
| print('latex embedding:') |
| print(embedding_latex) |
| print('Presentation MathML embedding:') |
| print(embedding_pmml) |
| print('Content MathML embedding:') |
| print(embedding_cmml) |
| ``` |
| |
| |
| |
| ## Full Model Architecture |
| ``` |
| SentenceTransformer( |
| (0): Asym( |
| (latex-0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel |
| (mathml-0): MarkuplmTransformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MarkupLMModel |
| ) |
| (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) |
| ) |
| ``` |
| |
| ## Citing & Authors |
| |
| <!--- Describe where people can find more information --> |
| ``` |
| @inproceedings{wang2023math, |
| title={Math Information Retrieval with Contrastive Learning of Formula Embeddings}, |
| author={Wang, Jingyi and Tian, Xuedong}, |
| booktitle={International Conference on Web Information Systems Engineering}, |
| pages={97--107}, |
| year={2023}, |
| organization={Springer} |
| } |
| ``` |
