Instructions to use dflcmu/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dflcmu/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dflcmu/results")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dflcmu/results") model = AutoModelForSequenceClassification.from_pretrained("dflcmu/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from dflcmu/results: direct link, hf CLI and curl.
- Browser
- Download file 3.45 kB
-
https://huggingface.co/dflcmu/results/resolve/main/training_args.bin
- Command line
-
hf download hf://dflcmu/results/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dflcmu/results/resolve/main/training_args.bin
3.45 kB
- Xet hash:
- 1638ddff3f4d95429da3bbe6df54c6c0f34a18b6923c49f35b7c77bc605d7ba1
- Size of remote file:
- 3.45 kB
- SHA256:
- 9df06ce27297410aea1e534f3baa549834a6b9a93bd2bcbeae9dce4397967308
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