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---
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 | |