Instructions to use UT/BRTW with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UT/BRTW with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="UT/BRTW")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("UT/BRTW") model = AutoModelForSequenceClassification.from_pretrained("UT/BRTW", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from UT/BRTW: direct link, hf CLI and curl.
- Browser
- Download file 435 MB
-
https://huggingface.co/UT/BRTW/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://UT/BRTW@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/UT/BRTW/resolve/refs%2Fpr%2F1/pytorch_model.bin
435 MB
- Xet hash:
- 18a3d20813f8cc78dd1e9b6da9d76f13d5bc66ed76379b91feb7096169a2fed6
- Size of remote file:
- 435 MB
- SHA256:
- a6a24d8abe0628db5e3b063f2a209c2bf4329c1752a3bde3b271ffd0bf352c15
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