Instructions to use harish/PT-UP-xlmR-FalseFalse-OneShot-0_BEST with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harish/PT-UP-xlmR-FalseFalse-OneShot-0_BEST with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="harish/PT-UP-xlmR-FalseFalse-OneShot-0_BEST")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("harish/PT-UP-xlmR-FalseFalse-OneShot-0_BEST") model = AutoModelForSequenceClassification.from_pretrained("harish/PT-UP-xlmR-FalseFalse-OneShot-0_BEST", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from harish/PT-UP-xlmR-FalseFalse-OneShot-0_BEST: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/harish/PT-UP-xlmR-FalseFalse-OneShot-0_BEST/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://harish/PT-UP-xlmR-FalseFalse-OneShot-0_BEST/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/harish/PT-UP-xlmR-FalseFalse-OneShot-0_BEST/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- 067b75b2946475be190d8fc112c2879b83f81d8d5f0e7cd59c5d99f96e2ca7e8
- Size of remote file:
- 1.11 GB
- SHA256:
- cfb139126ed25fc8decd173c4bf8ba4300f51c2152d9eaca5bb9a31ab345f689
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