Instructions to use Softechlb/articles_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Softechlb/articles_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Softechlb/articles_classification", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Softechlb/articles_classification") model = AutoModelForSequenceClassification.from_pretrained("Softechlb/articles_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 23d0408b8f61839e4cf001502db4266df5b05893fec27bae195ced7cb71c46fe
- Size of remote file:
- 560 MB
- SHA256:
- c5a767ecbabf0e73e9e35000f7677cfa089ec5a96af50e53d91d0431f098d57f
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