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mevol/BiomedNLP-PubMedBERT-ProteinStructure-NER-v2.1_quantized
BiomedNLP-PubMedBERT-ProteinStructure-NER-v2.1_quantized is a token classification model from mevol. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.
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From the Hugging Face model README
| Feature | Description |
|---|---|
| Name | BiomedNLP-PubMedBERT-ProteinStructure-NER-2.1_quantized |
| Default Pipeline | transformer, ner |
| Components | transformer, ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | Melanie Vollmar |
| Component | Labels |
|---|---|
ner | "bond_interaction", "chemical", "complex_assembly", "evidence", "experimental_method", "gene", "mutant", "oligomeric_state", "protein", "protein_state", "protein_type", "ptm", "residue_name", "residue_name_number", "residue_number", "residue_range", "site", "species", "structure_element", "taxonomy_domain" |
| entity type | precision | recall | F1 | sample number |
|---|---|---|---|---|
| "bond_interaction" | 0.94 | 0.89 | 0.90 | 40 |
| "chemical" | 0.85 | 0.91 | 0.88 | 597 |
| "complex_assembly" | 0.86 | 0.89 | 0.87 | 183 |
| "evidence" | 0.82 | 0.89 | 0.85 | 400 |
| "experimental_method" | 0.86 | 0.84 | 0.85 | 307 |
| "gene" | 0.73 | 0.86 | 0.79 | 26 |
| "mutant" | 0.88 | 0.93 | 0.90 | 215 |
| "oligomeric_state" | 0.91 | 0.96 | 0.94 | 117 |
| "protein" | 0.90 | 0.94 | 0.92 | 758 |
| "protein_state" | 0.81 | 0.86 | 0.83 | 543 |
| "protein_type" | 0.85 | 0.85 | 0.85 | 277 |
| "ptm" | 0.66 | 0.70 | 0.68 | 34 |
| "residue_name" | 0.93 | 0.92 | 0.92 | 70 |
| "residue_name_number" | 0.95 | 0.95 | 0.95 | 257 |
| "residue_number" | 0.64 | 0.88 | 0.74 | 44 |
| "residue_range" | 0.84 | 0.76 | 0.80 | 31 |
| "site" | 0.86 | 0.89 | 0.88 | 247 |
| "species" | 0.95 | 0.97 | 0.96 | 76 |
| "structure_element" | 0.90 | 0.92 | 0.91 | 749 |
| "taxonomy_domain" | 0.99 | 0.98 | 0.98 | 82 |
Vollmar, M., Tirunagari, S., Harrus, D. et al. Dataset from a human-in-the-loop approach to identify functionally important protein residues from literature. Sci Data 11, 1032 2024 https://doi.org/10.1038/s41597-024-03841-9