Instructions to use jl-wang9/supervised_test_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jl-wang9/supervised_test_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jl-wang9/supervised_test_1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jl-wang9/supervised_test_1") model = AutoModelForSequenceClassification.from_pretrained("jl-wang9/supervised_test_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from jl-wang9/supervised_test_1: direct link, hf CLI and curl.
- Browser
- Download file 3.96 kB
-
https://huggingface.co/jl-wang9/supervised_test_1/resolve/main/training_args.bin
- Command line
-
hf download hf://jl-wang9/supervised_test_1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/jl-wang9/supervised_test_1/resolve/main/training_args.bin
3.96 kB
- Xet hash:
- 49386d742dd1bb4fa4401f0725c89e7f5257cabe2acdc1321a3a6652a349fed9
- Size of remote file:
- 3.96 kB
- SHA256:
- 3eed9298a035f9992003c8043a573396d39c140a3b1a8ae31a30e93ba293ceaf
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