Instructions to use riturralde/es_metaextract_umsa_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use riturralde/es_metaextract_umsa_v1 with spaCy:
!pip install https://huggingface.co/riturralde/es_metaextract_umsa_v1/resolve/main/es_metaextract_umsa_v1-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("es_metaextract_umsa_v1") # Importing as module. import es_metaextract_umsa_v1 nlp = es_metaextract_umsa_v1.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | es_metaextract_umsa_v1 |
| Version | 1.0 |
| spaCy | >=3.7.2,<3.8.0 |
| 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 (6 labels for 1 components)
| Component | Labels |
|---|---|
ner |
ADVISOR, AUTHOR, DEPARTMENT, FACULTY, TITLE, YEAR |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
90.05 |
ENTS_P |
85.82 |
ENTS_R |
94.71 |
TOK2VEC_LOSS |
52012.59 |
NER_LOSS |
228767.42 |
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Evaluation results
- NER Precisionself-reported0.858
- NER Recallself-reported0.947
- NER F Scoreself-reported0.900