Instructions to use UT/MULTIBRT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UT/MULTIBRT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="UT/MULTIBRT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("UT/MULTIBRT") model = AutoModelForSequenceClassification.from_pretrained("UT/MULTIBRT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from UT/MULTIBRT: direct link, hf CLI and curl.
- Browser
- Download file 670 MB
-
https://huggingface.co/UT/MULTIBRT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://UT/MULTIBRT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/UT/MULTIBRT/resolve/main/pytorch_model.bin
670 MB
- Xet hash:
- 3abfeec5610d2c65b38df2e6d04e3bfeb1d7769f9adbc75ef29b1b2611efa47a
- Size of remote file:
- 670 MB
- SHA256:
- 2f0e5fbdcf1ac019371aa374ff658b2aa94dc3943dcb6083ade7f4062bc851e5
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