Instructions to use tiya1012/stress_deberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiya1012/stress_deberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tiya1012/stress_deberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tiya1012/stress_deberta") model = AutoModelForSequenceClassification.from_pretrained("tiya1012/stress_deberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tiya1012/stress_deberta: direct link, hf CLI and curl.
- Browser
- Download file 557 MB
-
https://huggingface.co/tiya1012/stress_deberta/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tiya1012/stress_deberta/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tiya1012/stress_deberta/resolve/main/pytorch_model.bin
557 MB
- Xet hash:
- 593f06d24cec9fbe7baec7916e49859e0e394653b12a4a17eb139501b867688c
- Size of remote file:
- 557 MB
- SHA256:
- 1d5eb7bd28e08a1ef198c5f71bf6e702cab4b9450d77143aaa3ac3d2f066103f
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