metadata
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_pipeline
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9949601691
- name: NER Recall
type: recall
value: 0.99418141
- name: NER F Score
type: f_score
value: 0.9945706371
| Feature | Description |
|---|---|
| Name | en_pipeline |
| Version | 0.0.0 |
| spaCy | >=3.7.2,<3.8.0 |
| Default Pipeline | tok2vec, ner |
| Components | tok2vec, ner |
| Vectors | 514157 keys, 514157 unique vectors (300 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | n/a |
Label Scheme
View label scheme (30 labels for 1 components)
| Component | Labels |
|---|---|
ner |
BAGS, CARDINAL, COLOR, DATE, DECORATIONS, DEPARTMENT, EVENT, FAC, GPE, HOODIES, LANGUAGE, LAW, LOC, LONG SLEEVE SHIRTS, MONEY, MUGS, NORP, ONESIES, ORDINAL, ORG, PERCENT, PERSON, POLOS, PRODUCT, QUANTITY, SIZE, STYLE, T-SHIRTS, TIME, WORK_OF_ART |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
99.46 |
ENTS_P |
99.50 |
ENTS_R |
99.42 |
TOK2VEC_LOSS |
0.00 |
NER_LOSS |
27925.61 |