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:
- 8a4ecc5a9c861c4313b9a106291cb20819531aa74407b13d56778e29176c76c2
- Size of remote file:
- 268 MB
- SHA256:
- ed7c8a5392e3c1551f8d12e01df68c283ac351b473c70f54979f46f505fb0370
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