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 pytorch_model.bin from ModelTC/bart-base-mrpc: direct link, hf CLI and curl.
- Browser
- Download file 560 MB
-
https://huggingface.co/ModelTC/bart-base-mrpc/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ModelTC/bart-base-mrpc/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ModelTC/bart-base-mrpc/resolve/main/pytorch_model.bin
560 MB
- Xet hash:
- aa10fb094b7f0ebf2422bcf2bb559a9963e2a92affee60eeea32d858d7e66700
- Size of remote file:
- 560 MB
- SHA256:
- 4d2f779a8e650ec93660730930129427c815711a08182371a4e68b8e02b93d53
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