readme: ---
  tags:
  - spacy
  - token-classification
  language:
  - es
  license: mit
  model-index:
  - name: es_pharmaconer_ner_trf
    results:
    - task:
        name: NER
        type: token-classification
      metrics:
      - name: NER Precision
        type: precision
        value: 0.9066736184
      - name: NER Recall
        type: recall
        value: 0.9152631579
      - name: NER F Score
        type: f_score
        value: 0.9109481404
  ---
  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
  
  | Feature | Description |
  | --- | --- |
  | **Name** | `es_pharmaconer_ner_trf` |
  | **Version** | `3.4.1` |
  | **spaCy** | `>=3.4.1,<3.5.0` |
  | **Default Pipeline** | `transformer`, `ner` |
  | **Components** | `transformer`, `ner` |
  | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
  | **Sources** | n/a |
  | **License** | `mit` |
  | **Author** | [The Text Mining Unit from Barcelona Supercomputing Center.](https://huggingface.co/PlanTL-GOB-ES/) |
  
  ### Label Scheme
  
  <details>
  
  <summary>View label scheme (4 labels for 1 components)</summary>
  
  | Component | Labels |
  | --- | --- |
  | **`ner`** | `NORMALIZABLES`, `NO_NORMALIZABLES`, `PROTEINAS`, `UNCLEAR` |
  
  </details>
  
  ### Accuracy
  
  | Type | Score |
  | --- | --- |
  | `ENTS_F` | 91.09 |
  | `ENTS_P` | 90.67 |
  | `ENTS_R` | 91.53 |
  | `TRANSFORMER_LOSS` | 15719.51 |
  | `NER_LOSS` | 22469.88 |