Instructions to use tiya1012/stress_mentalbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiya1012/stress_mentalbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tiya1012/stress_mentalbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tiya1012/stress_mentalbert") model = AutoModelForSequenceClassification.from_pretrained("tiya1012/stress_mentalbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tiya1012/stress_mentalbert: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/tiya1012/stress_mentalbert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tiya1012/stress_mentalbert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tiya1012/stress_mentalbert/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- ec130a0354e2f71cefecef30c7d328a67fe8ff76980efbd193c1d979c35af12a
- Size of remote file:
- 438 MB
- SHA256:
- c65603ef5ac3dbe58aefeda3b6eece014d7910aefbb47b970ae04f7d6ae40330
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