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--- |
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tags: |
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- spacy |
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- token-classification |
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language: |
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- es |
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license: mit |
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model-index: |
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- name: es_pharmaconer_ner_trf |
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results: |
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- task: |
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name: NER |
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type: token-classification |
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metrics: |
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- name: NER Precision |
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type: precision |
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value: 0.9066736184 |
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- name: NER Recall |
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type: recall |
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value: 0.9152631579 |
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- name: NER F Score |
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type: f_score |
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value: 0.9109481404 |
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--- |
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Basic Spacy BioNER pipeline, with a RoBERTa-based model [bsc-bio-ehr-es] (https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) and a dataset, Pharmaconer, a NER dataset annotated with substances, compounds and proteins entities. For further information, check the [official website](https://temu.bsc.es/pharmaconer/). Visit our [GitHub repository](https://github.com/PlanTL-GOB-ES/lm-biomedical-clinical-es). This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL |
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| Feature | Description | |
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| --- | --- | |
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| **Name** | `es_pharmaconer_ner_trf` | |
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| **Version** | `3.4.1` | |
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| **spaCy** | `>=3.4.1,<3.5.0` | |
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| **Default Pipeline** | `transformer`, `ner` | |
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| **Components** | `transformer`, `ner` | |
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| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | |
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| **Sources** | n/a | |
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| **License** | `mit` | |
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| **Author** | [The Text Mining Unit from Barcelona Supercomputing Center.](https://huggingface.co/PlanTL-GOB-ES/) | |
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### Label Scheme |
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<details> |
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<summary>View label scheme (4 labels for 1 components)</summary> |
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| Component | Labels | |
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| --- | --- | |
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| **`ner`** | `NORMALIZABLES`, `NO_NORMALIZABLES`, `PROTEINAS`, `UNCLEAR` | |
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</details> |
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### Accuracy |
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| Type | Score | |
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| --- | --- | |
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| `ENTS_F` | 91.09 | |
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| `ENTS_P` | 90.67 | |
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| `ENTS_R` | 91.53 | |
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| `TRANSFORMER_LOSS` | 15719.51 | |
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| `NER_LOSS` | 22469.88 | |
