| Feature | Description |
|---|---|
| Name | en_ner_model |
| Version | 0.1.1 |
| spaCy | >=3.2.1,<3.3.0 |
| 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 (1 labels for 1 components)
| Component | Labels |
|---|---|
ner |
SKILL |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
37.31 |
ENTS_P |
36.24 |
ENTS_R |
38.43 |
TOK2VEC_LOSS |
305790.85 |
NER_LOSS |
801195.82 |
- Downloads last month
- -
Evaluation results
- NER Precisionself-reported0.362
- NER Recallself-reported0.384
- NER F Scoreself-reported0.373
!pip install https://huggingface.co/premrawat/en_ner_model/resolve/main/en_ner_model-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_ner_model") # Importing as module. import en_ner_model nlp = en_ner_model.load()