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--- |
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language: es |
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license: gpl-3.0 |
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tags: |
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- spaCy |
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- Token Classification |
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widget: |
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- text: "Fue antes de llegar a Sigüeiro, en el Camino de Santiago." |
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- text: "El proyecto lo financia el Ministerio de Industria y Competitividad." |
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model-index: |
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- name: es_spacy_ner_cds |
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results: [] |
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--- |
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# Introduction |
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spaCy NER model trained 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). |
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## Usage |
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You can use this model with the spaCy *pipeline* for NER. |
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```python |
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import spacy |
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from spacy.pipeline import merge_entities |
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nlp = spacy.load("es_spacy_ner_cds") |
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nlp.add_pipe('sentencizer') |
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example = "Fue antes de llegar a Sigüeiro, en el Camino de Santiago. El proyecto lo financia el Ministerio de Industria y Competitiv |
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idad." |
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ner_pipe = nlp(example) |
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print(ner_pipe.ents) |
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for token in merge_entities(ner_pipe): |
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print(token.text, token.ent_type_) |
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``` |
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## Dataset |
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ToDo |
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## Model performance |
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entity|precision|recall|f1 |
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-|-|-|- |
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PER|0.942|0.890|0.915 |
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ORG|0.869|0.688|0.768 |
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LOC|0.975|0.987|0.981 |
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MISC|0.854|0.757|0.803 |
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micro avg|0.963|0.958|0.961 |
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macro avg|0.910|0.831|0.867 |
