Instructions to use munavvard2/en_foodNERspaCyRobarta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use munavvard2/en_foodNERspaCyRobarta with spaCy:
!pip install https://huggingface.co/munavvard2/en_foodNERspaCyRobarta/resolve/main/en_foodNERspaCyRobarta-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_foodNERspaCyRobarta") # Importing as module. import en_foodNERspaCyRobarta nlp = en_foodNERspaCyRobarta.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | en_foodNERspaCyRobarta |
| Version | 1.0.0 |
| spaCy | >=3.8.7,<3.9.0 |
| Default Pipeline | transformer, ner |
| Components | transformer, ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | n/a |
Label Scheme
View label scheme (4 labels for 1 components)
| Component | Labels |
|---|---|
ner |
ADDITIVE_CODE, ADDITIVE_NAME, ADDITIVE_ROLE, SOURCE |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
1.95 |
ENTS_P |
1.04 |
ENTS_R |
15.62 |
TRANSFORMER_LOSS |
26023.76 |
NER_LOSS |
30318.01 |
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Evaluation results
- NER Precisionself-reported0.891
- NER Recallself-reported0.916
- NER F Scoreself-reported0.904