Instructions to use PavanDeepak/Topic_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PavanDeepak/Topic_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PavanDeepak/Topic_Classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PavanDeepak/Topic_Classification") model = AutoModelForSequenceClassification.from_pretrained("PavanDeepak/Topic_Classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ad16f5580b0fa5fa9c8bc978d18edbaa017ec4a46fc05f0d8354fda9788521d7
- Size of remote file:
- 438 MB
- SHA256:
- cf7108ba17d7235111403a249f294a14ed22e38b71d7a3bc63926e514df52956
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