Instructions to use bergum/product_description_ranking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bergum/product_description_ranking with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bergum/product_description_ranking")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bergum/product_description_ranking") model = AutoModelForSequenceClassification.from_pretrained("bergum/product_description_ranking", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from bergum/product_description_ranking: direct link, hf CLI and curl.
- Browser
- Download file 90.9 MB
-
https://huggingface.co/bergum/product_description_ranking/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://bergum/product_description_ranking/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/bergum/product_description_ranking/resolve/main/pytorch_model.bin
90.9 MB
- Xet hash:
- 344e8a0a389837782f39c07091fe1883c61e8d5815c9c56b733de3ba3b94fee0
- Size of remote file:
- 90.9 MB
- SHA256:
- 478f10ba6d6f8b5191115b2f86f178316e6c65af983a5639c6c263ea755442b6
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