Instructions to use MRF18/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MRF18/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MRF18/results")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MRF18/results") model = AutoModelForSequenceClassification.from_pretrained("MRF18/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from MRF18/results: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/MRF18/results/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://MRF18/results/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/MRF18/results/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 3fa7cb54a0ac9716935c08825d3a6c6ddeca7a22c812101e75ff25107d3db0e2
- Size of remote file:
- 499 MB
- SHA256:
- 0a2e6fd87647929e26b17210427ee5786fa6594de9e0a68007a4c58991d23d36
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