Instructions to use exp386/it_ItLit800 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use exp386/it_ItLit800 with spaCy:
!pip install https://huggingface.co/exp386/it_ItLit800/resolve/main/it_ItLit800-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("it_ItLit800") # Importing as module. import it_ItLit800 nlp = it_ItLit800.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | it_ItLit800 |
| Version | 0.0.0 |
| spaCy | >=3.5.0,<3.6.0 |
| 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 (27 labels for 1 components)
| Component | Labels |
|---|---|
ner |
AGE, ANTRO, CHR, CULT, DATE, DATECEL, DATERNG, DIST, GEO, GPE, HON, KING, LOC, MATH, MISC, MON, NAME, NORP, ORG, PER, POI, QNT, QTM, REL, TIME, WRONG, XORG |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
97.65 |
ENTS_P |
97.48 |
ENTS_R |
97.82 |
TOK2VEC_LOSS |
215442.06 |
NER_LOSS |
129597.07 |
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Evaluation results
- NER Precisionself-reported0.975
- NER Recallself-reported0.978
- NER F Scoreself-reported0.976