Instructions to use msb-roshan/ohsumed_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use msb-roshan/ohsumed_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="msb-roshan/ohsumed_output")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("msb-roshan/ohsumed_output") model = AutoModelForSequenceClassification.from_pretrained("msb-roshan/ohsumed_output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from msb-roshan/ohsumed_output: direct link, hf CLI and curl.
- Browser
- Download file 456 MB
-
https://huggingface.co/msb-roshan/ohsumed_output/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://msb-roshan/ohsumed_output/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/msb-roshan/ohsumed_output/resolve/main/pytorch_model.bin
456 MB
- Xet hash:
- 0fc2fd6ec694193aa7e1506e19f766fce6bd74bdf1a456dca12925e2ec3ea470
- Size of remote file:
- 456 MB
- SHA256:
- 3f28a0e8ef8a6437c2b3a42eb5cd91a3e216f508ae821e9dbdc96574aa241ded
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