Description
This model is designed to be used in conjunction with the he-ref-ner model. See the README there for how to integrate them.
The model takes citations as input and tags the parts of the citation as entities. This is very useful for parsing the citation.
Technical details
| Feature | Description |
|---|---|
| Name | he_subref_ner |
| Version | 1.0.0 |
| spaCy | >=3.4.1,<3.5.0 |
| Default Pipeline | tok2vec, ner |
| Components | tok2vec, ner |
| Vectors | 394654 keys, 394654 unique vectors (50 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | n/a |
Label Scheme
View label scheme (7 labels for 1 components)
| Component | Labels |
|---|---|
ner |
讚讛, 讻讜转专转, 诇讗-专爪讬祝, 诇拽诪谉-诇讛诇谉, 诪住驻专, 住讬诪谉-讟讜讜讞, 砖诐 |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
96.32 |
ENTS_P |
96.12 |
ENTS_R |
96.51 |
TOK2VEC_LOSS |
11226.82 |
NER_LOSS |
2452.62 |
- Downloads last month
- 5
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Evaluation results
- NER Precisionself-reported0.961
- NER Recallself-reported0.965
- NER F Scoreself-reported0.963