Instructions to use dipesh/Intent-Classification-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dipesh/Intent-Classification-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dipesh/Intent-Classification-small")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dipesh/Intent-Classification-small") model = AutoModelForSequenceClassification.from_pretrained("dipesh/Intent-Classification-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2a2dd0914a667b39f1434ab68cf67dfd42ea059d91ae9965196c7e3fdcafb2a5
- Size of remote file:
- 3.52 kB
- SHA256:
- 4e0fe72da9aa986c2746659fa1c7cb29f469ba5ca39473e0dd213cdc5a722fcb
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