Instructions to use UT/BMW_DEBIAS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UT/BMW_DEBIAS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="UT/BMW_DEBIAS")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("UT/BMW_DEBIAS") model = AutoModelForSequenceClassification.from_pretrained("UT/BMW_DEBIAS", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from UT/BMW_DEBIAS: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/UT/BMW_DEBIAS/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://UT/BMW_DEBIAS/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/UT/BMW_DEBIAS/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- f2657e31344b7150ad2dfcc5e19c92bdeda75882fb82ded3df0bce82296e98f9
- Size of remote file:
- 1.11 GB
- SHA256:
- dc6b36d866410dbd69cc67dff0fc2efb488b06abb43efb91a1418b103949d22b
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