Text Classification
Transformers
Safetensors
English
decision-model
typed-decisions
calibration
opendecide
Instructions to use mvbalaji/od1-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mvbalaji/od1-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mvbalaji/od1-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mvbalaji/od1-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download backbone/tokenizer.json from mvbalaji/od1-base: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/mvbalaji/od1-base/resolve/main/backbone/tokenizer.json
- Command line
-
hf download hf://mvbalaji/od1-base/backbone/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/mvbalaji/od1-base/resolve/main/backbone/tokenizer.json
20 MB
- Xet hash:
- ceb1595fd65ffafd6a16d36ebf874f71b6a6c3b27243eca28cb11b1553e4ec8c
- Size of remote file:
- 20 MB
- SHA256:
- 9f74040c5081915656321e93610cfb3f433cad7529787e42bf1c226c096ed7ad
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