Update README.md

README.md

@@ -1,3 +1,85 @@
 ---
 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 |