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")# 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:
- baedff52b30fc93bd0f25d222488dd0ae2f4ebaf82981110c5daf3c2e80f8c7a
- Size of remote file:
- 560 MB
- SHA256:
- de593ead7b6f63572ead1ddbf19fc10ff7160670e44472e6fffaab205db74585
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