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---
license: gpl-3.0
language:
- es
library_name: spacy
pipeline_tag: token-classification
tags:
- spacy
- token-classification
widget:
- text: "Fue antes de llegar a Sigüeiro, en el Camino de Santiago."
- text: "El proyecto lo financia el Ministerio de Industria y Competitividad."
model-index:
- name: es_spacy_ner_cds
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9690286251
- name: NER Recall
type: recall
value: 0.9683470106
- name: NER F Score
type: f_score
value: 0.9686876979
---
# Introduction
spaCy NER model for Spanish trained with interviews in the domain of tourism related to the Way of Saint Jacques. It recognizes four types of entities: location (LOC), organizations (ORG), person (PER) and miscellaneous (MISC).
| Feature | Description |
| --- | --- |
| **Name** | `es_spacy_ner_cds_trf` |
| **Version** | `0.0.1a` |
| **spaCy** | `>=3.4.4,<3.5.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
### Label Scheme
<details>
<summary>View label scheme (4 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `LOC`, `MISC`, `ORG`, `PER` |
</details>
## Usage
You can use this model with the spaCy *pipeline* for NER.
```python
import spacy
from spacy.pipeline import merge_entities
nlp = spacy.load("es_spacy_ner_cds_trf")
nlp.add_pipe('sentencizer')
example = "Fue antes de llegar a Sigüeiro, en el Camino de Santiago. El proyecto lo financia el Ministerio de Industria y Competitividad."
ner_pipe = nlp(example)
print(ner_pipe.ents)
for token in merge_entities(ner_pipe):
print(token.text, token.ent_type_)
```
## Dataset
ToDo
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 96.87 |
| `ENTS_P` | 96.90 |
| `ENTS_R` | 96.83 |
| `TRANSFORMER_LOSS` | 7662.71 |
| `NER_LOSS` | 7673.80 |
|