Instructions to use haes95/eCommerce-intent-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use haes95/eCommerce-intent-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="haes95/eCommerce-intent-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("haes95/eCommerce-intent-classification") model = AutoModelForSequenceClassification.from_pretrained("haes95/eCommerce-intent-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b17943b9bba8d4c50bd27dac5c9ec30b1e4ce7c4eb07da1ccc67f33bb3f823cb
- Size of remote file:
- 369 MB
- SHA256:
- 409efd3717a6b31009126542ebdc333537018afaccb98178316616d02039d4fb
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