Instructions to use antonioanerao/bert-finetuned-mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use antonioanerao/bert-finetuned-mrpc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="antonioanerao/bert-finetuned-mrpc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("antonioanerao/bert-finetuned-mrpc") model = AutoModelForSequenceClassification.from_pretrained("antonioanerao/bert-finetuned-mrpc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from antonioanerao/bert-finetuned-mrpc: direct link, hf CLI and curl.
- Browser
- Download file 4.28 kB
-
https://huggingface.co/antonioanerao/bert-finetuned-mrpc/resolve/main/training_args.bin
- Command line
-
hf download hf://antonioanerao/bert-finetuned-mrpc/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/antonioanerao/bert-finetuned-mrpc/resolve/main/training_args.bin
4.28 kB
- Xet hash:
- a549e896b24c55eaf91dc6ea3a5cdc386642fe7ed405e7d7a77fe6ef71756896
- Size of remote file:
- 4.28 kB
- SHA256:
- 2206da5225c0d8026d41902bb9898bbe1b8854b9249ee2df8134162f3e1ccfb2
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