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README.md
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---
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license: gpl-3.0
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---
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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
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