Instructions to use oh201516/en_setec_mk_tv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use oh201516/en_setec_mk_tv with spaCy:
!pip install https://huggingface.co/oh201516/en_setec_mk_tv/resolve/main/en_setec_mk_tv-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_setec_mk_tv") # Importing as module. import en_setec_mk_tv nlp = en_setec_mk_tv.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | en_setec_mk_tv |
| Version | 0.0.2 |
| spaCy | >=3.7.5,<3.8.0 |
| Default Pipeline | tok2vec, ner, count_extraction_component, normalizer_component, feature_aggregator_component |
| Components | tok2vec, ner, count_extraction_component, normalizer_component, feature_aggregator_component |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | n/a |
Label Scheme
View label scheme (11 labels for 1 components)
| Component | Labels |
|---|---|
ner |
AUDIO_FEATURE, COLOR, CONNECTION, INCH, MOUNTING_FEATURE, OS, REFRESH_RATE, RESOLUTION, SOFTWARE_FEATURE, VIDEO_FEATURE, WIRELESS_FEATURE |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
99.18 |
ENTS_P |
99.20 |
ENTS_R |
99.16 |
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Evaluation results
- NER Precisionself-reported0.992
- NER Recallself-reported0.992
- NER F Scoreself-reported0.992