Instructions to use bpben/en_imdb_sent_cnn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use bpben/en_imdb_sent_cnn with spaCy:
!pip install https://huggingface.co/bpben/en_imdb_sent_cnn/resolve/main/en_imdb_sent_cnn-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_imdb_sent_cnn") # Importing as module. import en_imdb_sent_cnn nlp = en_imdb_sent_cnn.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | en_imdb_sent_cnn |
| Version | 0.0.0 |
| spaCy | >=3.4.4,<3.5.0 |
| Default Pipeline | textcat |
| Components | 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 |
pos, neg |
Accuracy
| Type | Score |
|---|---|
CATS_SCORE |
82.51 |
CATS_MICRO_P |
82.51 |
CATS_MICRO_R |
82.51 |
CATS_MICRO_F |
82.51 |
CATS_MACRO_P |
82.51 |
CATS_MACRO_R |
82.51 |
CATS_MACRO_F |
82.51 |
CATS_MACRO_AUC |
90.17 |
CATS_MACRO_AUC_PER_TYPE |
0.00 |
TEXTCAT_LOSS |
2099.23 |
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