Instructions to use opelumen/ru_pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use opelumen/ru_pipeline with spaCy:
!pip install https://huggingface.co/opelumen/ru_pipeline/resolve/main/ru_pipeline-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("ru_pipeline") # Importing as module. import ru_pipeline nlp = ru_pipeline.load() - Notebooks
- Google Colab
- Kaggle
This model (Volk assistant) created for my personal project
| Feature | Description |
|---|---|
| Name | ru_pipeline |
| Version | 0.0.0 |
| spaCy | >=3.7.2,<3.8.0 |
| 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 (2 labels for 1 components)
| Component | Labels |
|---|---|
ner |
DESC, TITLE |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
99.33 |
ENTS_P |
99.33 |
ENTS_R |
99.33 |
TOK2VEC_LOSS |
776.67 |
NER_LOSS |
8798.28 |
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Evaluation results
- NER Precisionself-reported0.993
- NER Recallself-reported0.993
- NER F Scoreself-reported0.993