Instructions to use UT/MULTIBRT_DEBIAS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UT/MULTIBRT_DEBIAS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="UT/MULTIBRT_DEBIAS")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("UT/MULTIBRT_DEBIAS") model = AutoModelForSequenceClassification.from_pretrained("UT/MULTIBRT_DEBIAS", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from UT/MULTIBRT_DEBIAS: direct link, hf CLI and curl.
- Browser
- Download file 670 MB
-
https://huggingface.co/UT/MULTIBRT_DEBIAS/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://UT/MULTIBRT_DEBIAS/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/UT/MULTIBRT_DEBIAS/resolve/main/pytorch_model.bin
670 MB
- Xet hash:
- 58810a3c818fea815dba49309961f59ab68072f90f1cf557eadc315e0c161dfd
- Size of remote file:
- 670 MB
- SHA256:
- e63a8119de00c601bea9bac76e3722151af771e14fb1e65d3d885877b70df2ae
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