Instructions to use tiya1012/stress_ALBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiya1012/stress_ALBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tiya1012/stress_ALBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tiya1012/stress_ALBERT") model = AutoModelForSequenceClassification.from_pretrained("tiya1012/stress_ALBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tiya1012/stress_ALBERT: direct link, hf CLI and curl.
- Browser
- Download file 70.8 MB
-
https://huggingface.co/tiya1012/stress_ALBERT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tiya1012/stress_ALBERT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tiya1012/stress_ALBERT/resolve/main/pytorch_model.bin
70.8 MB
- Xet hash:
- 11d35d34c26882a46e666af2452d75cb8b5e237ef353c58a64375496715a878d
- Size of remote file:
- 70.8 MB
- SHA256:
- 973f1f455e8beb342d732c20f5efb02de7851a0fda45e4ca4d46595705c59225
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