Instructions to use hjianganthony/en_fetch_ner_spacy_tsf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use hjianganthony/en_fetch_ner_spacy_tsf with spaCy:
!pip install https://huggingface.co/hjianganthony/en_fetch_ner_spacy_tsf/resolve/main/en_fetch_ner_spacy_tsf-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_fetch_ner_spacy_tsf") # Importing as module. import en_fetch_ner_spacy_tsf nlp = en_fetch_ner_spacy_tsf.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | en_fetch_ner_spacy_tsf |
| Version | 0.0.0 |
| spaCy | >=3.6.1,<3.7.0 |
| 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 (2 labels for 1 components)
| Component | Labels |
|---|---|
ner |
BRAND, RETAILER |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
91.50 |
ENTS_P |
90.91 |
ENTS_R |
92.11 |
TRANSFORMER_LOSS |
0.00 |
NER_LOSS |
3789.17 |
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Evaluation results
- NER Precisionself-reported0.909
- NER Recallself-reported0.921
- NER F Scoreself-reported0.915