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:
- 1cd511a66c80a8a047f330ffaf74de666a71fb6f3e2bc4d594fb8acd5318c6d6
- Size of remote file:
- 268 MB
- SHA256:
- 7786004084f234a97ef9e6dd0cb91ba21479d360de3e27978305a4e50b8bdc93
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