Instructions to use riturralde/es_faculty_ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use riturralde/es_faculty_ner with spaCy:
!pip install https://huggingface.co/riturralde/es_faculty_ner/resolve/main/es_faculty_ner-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("es_faculty_ner") # Importing as module. import es_faculty_ner nlp = es_faculty_ner.load() - Notebooks
- Google Colab
- Kaggle
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Download README.md from riturralde/es_faculty_ner: direct link, hf CLI and curl.
- Browser
- Download file 1.12 kB
-
https://huggingface.co/riturralde/es_faculty_ner/resolve/main/README.md
- Command line
-
hf download hf://riturralde/es_faculty_ner/README.md
-
curl -L -o README.md https://huggingface.co/riturralde/es_faculty_ner/resolve/main/README.md
1.12 kB
metadata
tags:
- spacy
- token-classification
language:
- es
model-index:
- name: es_faculty_ner
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9964384646
- name: NER Recall
type: recall
value: 0.998809996
- name: NER F Score
type: f_score
value: 0.9976228209
| Feature | Description |
|---|---|
| Name | es_faculty_ner |
| Version | 0.1.0 |
| spaCy | >=3.8.4,<3.9.0 |
| Default Pipeline | tok2vec, ner |
| Components | tok2vec, ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | n/a |
Label Scheme
View label scheme (1 labels for 1 components)
| Component | Labels |
|---|---|
ner |
FACULTY |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
99.76 |
ENTS_P |
99.64 |
ENTS_R |
99.88 |
TOK2VEC_LOSS |
71121.42 |
NER_LOSS |
3476.43 |