Instructions to use tiya1012/depress_roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiya1012/depress_roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tiya1012/depress_roberta")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tiya1012/depress_roberta") model = AutoModelForSequenceClassification.from_pretrained("tiya1012/depress_roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tiya1012/depress_roberta: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/tiya1012/depress_roberta/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tiya1012/depress_roberta/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tiya1012/depress_roberta/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- ce537d25fbcc7004038426372d4100fbb2b6d18773c67cf79a970d3608631272
- Size of remote file:
- 499 MB
- SHA256:
- ba224aea4fae71c9a55b1f7424b6b4784d385c9cbb37f830ac14d59ad8d29934
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