Instructions to use UT/PARSBRT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UT/PARSBRT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="UT/PARSBRT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("UT/PARSBRT") model = AutoModelForSequenceClassification.from_pretrained("UT/PARSBRT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from UT/PARSBRT: direct link, hf CLI and curl.
- Browser
- Download file 651 MB
-
https://huggingface.co/UT/PARSBRT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://UT/PARSBRT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/UT/PARSBRT/resolve/main/pytorch_model.bin
651 MB
- Xet hash:
- e5f672801b397062bdf619170c03b7a2b0e626abe6b05b1d2fe9b0b38be463ae
- Size of remote file:
- 651 MB
- SHA256:
- 10c8aaf9bb8401a56f11db5fa69fdaa2970dbf01291c12c48f1371edb1ca4f64
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