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:
- a72ceb3533c1d151fc5a80ac49ba526a6c3289d7b072cdd590b0e2394a3cdb7c
- Size of remote file:
- 3.83 kB
- SHA256:
- 007ed335d99946ad11d108b893a07c978014c14444525acaf161b845a9db699d
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