Instructions to use Migunov/ru_pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use Migunov/ru_pipeline with spaCy:
!pip install https://huggingface.co/Migunov/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
zhk finder
| Feature | Description |
|---|---|
| Name | ru_pipeline |
| Version | 0.0.1 |
| spaCy | >=3.7.4,<3.8.0 |
| Default Pipeline | transformer, ner |
| Components | transformer, ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | Dmitry Migunov |
Label Scheme
View label scheme (4 labels for 1 components)
| Component | Labels |
|---|---|
ner |
KORP, LOC, ORG, ZHK |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
100.00 |
ENTS_P |
100.00 |
ENTS_R |
100.00 |
TRANSFORMER_LOSS |
1943.09 |
NER_LOSS |
5196.36 |
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Evaluation results
- NER Precisionself-reported1.000
- NER Recallself-reported1.000
- NER F Scoreself-reported1.000