Instructions to use hjianganthony/en_kyc_nerre_tok2vec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use hjianganthony/en_kyc_nerre_tok2vec with spaCy:
!pip install https://huggingface.co/hjianganthony/en_kyc_nerre_tok2vec/resolve/main/en_kyc_nerre_tok2vec-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_kyc_nerre_tok2vec") # Importing as module. import en_kyc_nerre_tok2vec nlp = en_kyc_nerre_tok2vec.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | en_kyc_nerre_tok2vec |
| Version | 0.0.0 |
| spaCy | >=3.6.1,<3.7.0 |
| Default Pipeline | tok2vec, ner |
| Components | tok2vec, ner |
| Vectors | 514157 keys, 20000 unique vectors (300 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | n/a |
Label Scheme
View label scheme (3 labels for 1 components)
| Component | Labels |
|---|---|
ner |
CRIME, PERSON, PROCECUTION |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
51.76 |
ENTS_P |
53.66 |
ENTS_R |
50.00 |
TOK2VEC_LOSS |
25409.45 |
NER_LOSS |
25821.48 |
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Evaluation results
- NER Precisionself-reported0.537
- NER Recallself-reported0.500
- NER F Scoreself-reported0.518