Instructions to use Sefaria/he_subref_ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use Sefaria/he_subref_ner with spaCy:
!pip install https://huggingface.co/Sefaria/he_subref_ner/resolve/main/he_subref_ner-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("he_subref_ner") # Importing as module. import he_subref_ner nlp = he_subref_ner.load() - Notebooks
- Google Colab
- Kaggle
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 |
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Evaluation results
- NER Precisionself-reported0.961
- NER Recallself-reported0.965
- NER F Scoreself-reported0.963