Instructions to use Kk2k/distilbert_e-commerce_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kk2k/distilbert_e-commerce_data with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kk2k/distilbert_e-commerce_data")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kk2k/distilbert_e-commerce_data") model = AutoModelForSequenceClassification.from_pretrained("Kk2k/distilbert_e-commerce_data", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 514164690f96dfdac6b46d4813a6887a27c8940b7ec21ade66cb67e5a7838eda
- Size of remote file:
- 268 MB
- SHA256:
- 25611fbfc6b19cf17c1d9db870fc50bf815ee3987eb656c425cf31e9d5ac9893
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