Instructions to use bergum/commerce_ranker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bergum/commerce_ranker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bergum/commerce_ranker")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bergum/commerce_ranker") model = AutoModelForSequenceClassification.from_pretrained("bergum/commerce_ranker", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from bergum/commerce_ranker: direct link, hf CLI and curl.
- Browser
- Download file 738 MB
-
https://huggingface.co/bergum/commerce_ranker/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://bergum/commerce_ranker/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/bergum/commerce_ranker/resolve/main/pytorch_model.bin
738 MB
- Xet hash:
- 67f39ad3a79e0469265d025874f02ba74d0d9cf823f8756bb5b3c313276f765a
- Size of remote file:
- 738 MB
- SHA256:
- 6483f9f75bd7e1c8411e8998cc1c99318e71c8c31a2542a09dc17130bd0e7d4b
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