Instructions to use SlothCodes/LC-LSA-A2E-Intent-Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SlothCodes/LC-LSA-A2E-Intent-Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SlothCodes/LC-LSA-A2E-Intent-Classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SlothCodes/LC-LSA-A2E-Intent-Classifier") model = AutoModelForSequenceClassification.from_pretrained("SlothCodes/LC-LSA-A2E-Intent-Classifier") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ac4927a405068725dd9a1df7f12faa398a574146a4ed938b1e7b10948a3c96a7
- Size of remote file:
- 499 MB
- SHA256:
- d4b219d9f693ff6ada301490cb2bc588ab9aba589158fea8cab84234c72320cd
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