Instructions to use sujayC66/spacy_trf_sector_e100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use sujayC66/spacy_trf_sector_e100 with spaCy:
!pip install https://huggingface.co/sujayC66/spacy_trf_sector_e100/resolve/main/spacy_trf_sector_e100-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("spacy_trf_sector_e100") # Importing as module. import spacy_trf_sector_e100 nlp = spacy_trf_sector_e100.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | en_pipeline |
| Version | 0.0.0 |
| spaCy | >=3.7.2,<3.8.0 |
| Default Pipeline | transformer, ner |
| Components | transformer, 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 |
SECTOR |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
34.62 |
ENTS_P |
45.00 |
ENTS_R |
28.12 |
TRANSFORMER_LOSS |
9684.94 |
NER_LOSS |
53278.63 |
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Evaluation results
- NER Precisionself-reported0.450
- NER Recallself-reported0.281
- NER F Scoreself-reported0.346