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:
- 961d0fdace1536940e31779066b4866f2971a96bc3091edd0e22bd1ecfece922
- Size of remote file:
- 268 MB
- SHA256:
- a2e67b6a2369a88ae8f185ddd5a641bd6e8c60e4c72136ae20b2da225635d1ac
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