Instructions to use aryan10022001/en_predii_ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use aryan10022001/en_predii_ner with spaCy:
!pip install https://huggingface.co/aryan10022001/en_predii_ner/resolve/main/en_predii_ner-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_predii_ner") # Importing as module. import en_predii_ner nlp = en_predii_ner.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | en_predii_ner |
| Version | 0.0.0 |
| spaCy | >=3.7.4,<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 (8 labels for 1 components)
| Component | Labels |
|---|---|
ner |
CHASSIS TYPE, COMPONENT, CORRECTIVE ACTION, FAILURE ISSUE, MANUFACTURER, PARTS, PROCESS, VEHICLE MODEL |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
81.28 |
ENTS_P |
85.69 |
ENTS_R |
77.29 |
TOK2VEC_LOSS |
74793.71 |
NER_LOSS |
798047.72 |
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Spaces using aryan10022001/en_predii_ner 2
Evaluation results
- NER Precisionself-reported0.857
- NER Recallself-reported0.773
- NER F Scoreself-reported0.813