Instructions to use rdfez/tl_custom_calamancy_md with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use rdfez/tl_custom_calamancy_md with spaCy:
!pip install https://huggingface.co/rdfez/tl_custom_calamancy_md/resolve/main/tl_custom_calamancy_md-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("tl_custom_calamancy_md") # Importing as module. import tl_custom_calamancy_md nlp = tl_custom_calamancy_md.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | tl_custom_calamancy_md |
| Version | 0.0.0 |
| spaCy | >=3.8.14,<3.9.0 |
| Default Pipeline | tok2vec, ner |
| Components | tok2vec, ner |
| Vectors | -1 keys, 200000 unique vectors (200 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | n/a |
Label Scheme
View label scheme (4 labels for 1 components)
| Component | Labels |
|---|---|
ner |
LOC, MISC, ORG, PER |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
96.31 |
ENTS_P |
96.37 |
ENTS_R |
96.26 |
TOK2VEC_LOSS |
0.00 |
NER_LOSS |
4478.58 |
- Downloads last month
- -
Evaluation results
- NER Precisionself-reported0.964
- NER Recallself-reported0.963
- NER F Scoreself-reported0.963