Instructions to use dipesh/Intent-Classification-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dipesh/Intent-Classification-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dipesh/Intent-Classification-large")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dipesh/Intent-Classification-large") model = AutoModelForSequenceClassification.from_pretrained("dipesh/Intent-Classification-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7475c30f1588b07904dd2f42501bd823a9bee8c0f363976f1f8eecdfa159bc7b
- Size of remote file:
- 3.52 kB
- SHA256:
- e68000c90d4483f56dfcd213f2e4f64a0479412185daa6c2d180109df2de012b
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