Text Classification
Transformers
Safetensors
multilingual
snt_classifier
feature-extraction
news
topic-classification
multi-label
xlm-roberta
custom_code
Instructions to use sweenk/snt-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sweenk/snt-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sweenk/snt-classifier", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("sweenk/snt-classifier", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 516f2fc365650acf148b86e45907397a0c35af523df9e2ea3f88e25c76c1f057
- Size of remote file:
- 17.1 MB
- SHA256:
- acbd420e2269cdc1ef45332d3d5c418be4aef6b8cb5a0b7ccae0893485307153
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