Instructions to use colerobertson/en_ftr_model_roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use colerobertson/en_ftr_model_roberta with spaCy:
!pip install https://huggingface.co/colerobertson/en_ftr_model_roberta/resolve/main/en_ftr_model_roberta-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_ftr_model_roberta") # Importing as module. import en_ftr_model_roberta nlp = en_ftr_model_roberta.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | en_ftr_model_roberta |
| Version | 1.0.0 |
| spaCy | >=3.4.3,<3.5.0 |
| Default Pipeline | transformer, textcat |
| Components | transformer, textcat |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | n/a |
Label Scheme
View label scheme (2 labels for 1 components)
| Component | Labels |
|---|---|
textcat |
FTR, NOT_FTR |
Accuracy
| Type | Score |
|---|---|
CATS_SCORE |
86.47 |
CATS_MICRO_P |
89.56 |
CATS_MICRO_R |
89.56 |
CATS_MICRO_F |
89.56 |
CATS_MACRO_P |
85.49 |
CATS_MACRO_R |
87.63 |
CATS_MACRO_F |
86.47 |
CATS_MACRO_AUC |
93.90 |
CATS_MACRO_AUC_PER_TYPE |
0.00 |
TRANSFORMER_LOSS |
1725.63 |
TEXTCAT_LOSS |
4244.13 |
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