Text Classification
Transformers
Safetensors
PEFT
branchone
feature-extraction
decision-model
multimodal
custom-code
custom_code
Instructions to use ndemirdemir/BranchOne with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ndemirdemir/BranchOne with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ndemirdemir/BranchOne", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ndemirdemir/BranchOne", trust_remote_code=True, device_map="auto") - PEFT
How to use ndemirdemir/BranchOne with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Download processor/tokenizer.json from ndemirdemir/BranchOne: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/ndemirdemir/BranchOne/resolve/main/processor/tokenizer.json
- Command line
-
hf download hf://ndemirdemir/BranchOne/processor/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ndemirdemir/BranchOne/resolve/main/processor/tokenizer.json
20 MB
- Xet hash:
- 777bcaa63794fa47b8f53680be9d6d176f1fcbd7ba03cdc6c3bae2b3d76b323f
- Size of remote file:
- 20 MB
- SHA256:
- 06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523
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