Text Classification
Transformers
Safetensors
Korean
bert
korean
mental-health
depression-detection
text-embeddings-inference
Instructions to use gamzaS2/final_depression_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gamzaS2/final_depression_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gamzaS2/final_depression_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gamzaS2/final_depression_model") model = AutoModelForSequenceClassification.from_pretrained("gamzaS2/final_depression_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from gamzaS2/final_depression_model: direct link, hf CLI and curl.
- Browser
- Download file 5.78 kB
-
https://huggingface.co/gamzaS2/final_depression_model/resolve/main/training_args.bin
- Command line
-
hf download hf://gamzaS2/final_depression_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/gamzaS2/final_depression_model/resolve/main/training_args.bin
5.78 kB
- Xet hash:
- 0756416683a070e07a4f1e3eb619850188498664e909b8eca8f24ca63bc25c6d
- Size of remote file:
- 5.78 kB
- SHA256:
- 8e15f1eee571bb2db14ddcd5b575192bf79d4434ea6d479b97f2d55c86c1daf3
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