Instructions to use tiya1012/stress_twhinbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiya1012/stress_twhinbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tiya1012/stress_twhinbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tiya1012/stress_twhinbert") model = AutoModelForSequenceClassification.from_pretrained("tiya1012/stress_twhinbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tiya1012/stress_twhinbert: direct link, hf CLI and curl.
- Browser
- Download file 1.12 GB
-
https://huggingface.co/tiya1012/stress_twhinbert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tiya1012/stress_twhinbert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tiya1012/stress_twhinbert/resolve/main/pytorch_model.bin
1.12 GB
- Xet hash:
- 282746d65ad6d5d4334244b5f364a20f40b15678f512c3e3b2871c6ecaee76ec
- Size of remote file:
- 1.12 GB
- SHA256:
- a53ec678578f2785970f1e386fb85dbdf89cc0ff312ce0a257003b91dd2bf81a
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