Instructions to use victorialslocum/en_reciparse_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use victorialslocum/en_reciparse_model with spaCy:
!pip install https://huggingface.co/victorialslocum/en_reciparse_model/resolve/main/en_reciparse_model-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_reciparse_model") # Importing as module. import en_reciparse_model nlp = en_reciparse_model.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | en_reciparse_model |
| Version | 0.0.0 |
| spaCy | >=3.3.1,<3.4.0 |
| 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 (1 labels for 1 components)
| Component | Labels |
|---|---|
ner |
INGREDIENT |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
87.97 |
ENTS_P |
88.54 |
ENTS_R |
87.40 |
TOK2VEC_LOSS |
37557.71 |
NER_LOSS |
19408.65 |
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