Instructions to use sagipend/exp2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sagipend/exp2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sagipend/exp2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sagipend/exp2") model = AutoModelForSequenceClassification.from_pretrained("sagipend/exp2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from sagipend/exp2: direct link, hf CLI and curl.
- Browser
- Download file 3.96 kB
-
https://huggingface.co/sagipend/exp2/resolve/main/training_args.bin
- Command line
-
hf download hf://sagipend/exp2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/sagipend/exp2/resolve/main/training_args.bin
3.96 kB
- Xet hash:
- 8fa74952f13e850063d61fa7ffcdad1fc838dcf2574f13452020ca970719e14d
- Size of remote file:
- 3.96 kB
- SHA256:
- 9aefa63b8a4e20310ac8cdc2f35d7d9b538321e4b070c7e38dfa101fe5da8a9a
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