Instructions to use ModelTC/bart-base-mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModelTC/bart-base-mrpc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ModelTC/bart-base-mrpc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ModelTC/bart-base-mrpc") model = AutoModelForSequenceClassification.from_pretrained("ModelTC/bart-base-mrpc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from ModelTC/bart-base-mrpc: direct link, hf CLI and curl.
- Browser
- Download file 1.12 GB
-
https://huggingface.co/ModelTC/bart-base-mrpc/resolve/main/optimizer.pt
- Command line
-
hf download hf://ModelTC/bart-base-mrpc/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/ModelTC/bart-base-mrpc/resolve/main/optimizer.pt
1.12 GB
- Xet hash:
- fe012fb0022411bfc8353c8b2e0a29366d4a74d5fdc3ccf83aadd7b8e735ce0c
- Size of remote file:
- 1.12 GB
- SHA256:
- b8eaa08c80a99f86d51c979acdfa12e3fbfa18c2230fffc2b5f76114b97924ff
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