Instructions to use cbruinsm/en_Coff_Ev2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use cbruinsm/en_Coff_Ev2 with spaCy:
!pip install https://huggingface.co/cbruinsm/en_Coff_Ev2/resolve/main/en_Coff_Ev2-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_Coff_Ev2") # Importing as module. import en_Coff_Ev2 nlp = en_Coff_Ev2.load() - Notebooks
- Google Colab
- Kaggle
Your Coffee at the Speed of Sound
| Feature | Description |
|---|---|
| Name | en_Coff_Ev2 |
| Version | 1.1.6 |
| spaCy | >=3.4.3,<3.5.0 |
| Default Pipeline | tok2vec, ner |
| Components | tok2vec, ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | MIT |
| Author | Chris Bruinsma,Iris Chi,Jack Felciano,Jeffrey Li,Dustin Paden |
Label Scheme
View label scheme (18 labels for 1 components)
| Component | Labels |
|---|---|
ner |
Anti, Brew Style, add-on, drink, extra, hot breakfast, milk, milk texture, pastry, pump quantity, roast, shot quality, shot quantity, size, syrup, temperature, toppings, upside-down |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
99.19 |
ENTS_P |
99.22 |
ENTS_R |
99.16 |
TOK2VEC_LOSS |
58625.70 |
NER_LOSS |
168185.77 |
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Evaluation results
- NER Precisionself-reported0.992
- NER Recallself-reported0.992
- NER F Scoreself-reported0.992