Instructions to use mertas7/medroberta-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mertas7/medroberta-binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mertas7/medroberta-binary")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mertas7/medroberta-binary") model = AutoModelForSequenceClassification.from_pretrained("mertas7/medroberta-binary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mertas7/medroberta-binary: direct link, hf CLI and curl.
- Browser
- Download file 504 MB
-
https://huggingface.co/mertas7/medroberta-binary/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://mertas7/medroberta-binary@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mertas7/medroberta-binary/resolve/refs%2Fpr%2F1/pytorch_model.bin
504 MB
- Xet hash:
- e4868d70901e8828093ab9b5cfa82418f60aad0a9c293d9b83b16a03bd5b7a78
- Size of remote file:
- 504 MB
- SHA256:
- f96cd337763089f3451aec344220c089f94ea2cce4856ef53c0d43a8f5b1afad
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