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 pytorch_model.bin from jl-wang9/supervised_test_1: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/jl-wang9/supervised_test_1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jl-wang9/supervised_test_1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jl-wang9/supervised_test_1/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 10069a6e831ba68832138236147d9f3dec16ef0f2a7adbb70e1b0f8ed037c456
- Size of remote file:
- 268 MB
- SHA256:
- 84262a90b358bf751b089c4bf73b393e7f91125e3d594dfbc2f78529e170fdc0
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