metadata
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_kyc_nerre
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.6862745098
- name: NER Recall
type: recall
value: 0.7954545455
- name: NER F Score
type: f_score
value: 0.7368421053
| Feature | Description | Note |
|---|---|---|
| Name | en_kyc_nerre |
|
| Version | 0.0.0 |
test run, official version will start from 1.xx.xx |
| 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 | hjiangAnthony |
Purpose
Identifying PERSON, CRIME, and PROCECUTION entities; supporting relation extraction for the next step
Label Scheme
View label scheme (3 labels for 1 components)
| Component | Labels |
|---|---|
ner |
CRIME, PERSON, PROCECUTION |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
73.68 |
ENTS_P |
68.63 |
ENTS_R |
79.55 |
TRANSFORMER_LOSS |
12977.28 |
NER_LOSS |
94024.87 |