
# 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). It was fine-tuned using `PlanTL-GOB-ES/roberta-base-bne`.

| Feature | Description |
| --- | --- |
| **Name** | `bne-spacy-corgale-ner-es` |
| **Version** | `0.0.2` |
| **spaCy** | `>=3.5.2,<3.6.0` |
| **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("bne-spacy-corgale-ner-es")
nlp.add_pipe('sentencizer')

example = "Fue antes de llegar a Sigüeiro, en el Camino de Santiago. Si te metes en el Franco desde la Alameda, vas hacia la Catedral. Y allí precisamente es Santiago el patrón del pueblo."
ner_pipe = nlp(example)

print(ner_pipe.ents)
for token in merge_entities(ner_pipe):
    print(token.text, token.ent_type_)
```

## Dataset

ToDo

## Model performance

entity|precision|recall|f1
-|-|-|-
LOC|0.985|0.987|0.986
MISC|0.862|0.865|0.863
ORG|0.938|0.779|0.851
PER|0.921|0.941|0.931
micro avg|0.971|0.972|0.971
macro avg|0.926|0.893|0.908
weighted avg|0.971|0.972|0.971
{"language": ["es"], "license": "gpl-3.0", "library_name": "spacy", "tags": ["spacy", "token-classification"], "pipeline_tag": "token-classification", "widget": [{"text": "Fue antes de llegar a Sig\u00fceiro, en el Camino de Santiago."}, {"text": "Si te metes en el Franco desde la Alameda, vas hacia la Catedral."}, {"text": "Y all\u00ed precisamente es Santiago el patr\u00f3n del pueblo."}]}
spacy token-classification es license:gpl-3.0 model-index region:us
TAGS
#spacy #token-classification #es #license-gpl-3.0 #model-index #region-us 

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). It was fine-tuned using 'PlanTL-GOB-ES/roberta-base-bne'.



### Label Scheme



View label scheme (4 labels for 1 components)


Usage
-----


You can use this model with the spaCy *pipeline* for NER.


Dataset
-------


ToDo


Model performance
-----------------




### Label Scheme



View label scheme (4 labels for 1 components)


Usage
-----


You can use this model with the spaCy *pipeline* for NER.


Dataset
-------


ToDo


Model performance
-----------------
TAGS
#spacy #token-classification #es #license-gpl-3.0 #model-index #region-us 
 ### Label Scheme



View label scheme (4 labels for 1 components)


Usage
-----


You can use this model with the spaCy *pipeline* for NER.


Dataset
-------


ToDo


Model performance
-----------------
TAGS
#spacy #token-classification #es #license-gpl-3.0 #model-index #region-us 
### Label Scheme



View label scheme (4 labels for 1 components)


Usage
-----


You can use this model with the spaCy *pipeline* for NER.


Dataset
-------


ToDo


Model performance
-----------------