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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 03acfc6d4e19ce588fa1f8182ecd1a4c446948d520cf30c48c2b4e75dd71d6fc
- Size of remote file:
- 438 MB
- SHA256:
- 8960e97fae5ee015877e0eda31b4f70adbf9184d51940d717ea3df2f12b1cfb6
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