Instructions to use swapnasa/intent_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use swapnasa/intent_data with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="swapnasa/intent_data")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("swapnasa/intent_data") model = AutoModelForSequenceClassification.from_pretrained("swapnasa/intent_data", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 46afddb11502b477ff2a55f4c18f9ca493afc44fdbba2de8aa7a3635f4e85261
- Size of remote file:
- 4.54 kB
- SHA256:
- 9b2a6a3118ec91d3fe18615b9a7024353d4b35f3f0aac061d1df31a5ddbd4e45
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