Instructions to use buelfhood/SOCO_Adapter_Java_LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use buelfhood/SOCO_Adapter_Java_LoRA with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("huggingface/CodeBERTa-small-v1") model.load_adapter("buelfhood/SOCO_Adapter_Java_LoRA", set_active=True) - Notebooks
- Google Colab
- Kaggle
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Download README.md from buelfhood/SOCO_Adapter_Java_LoRA: direct link, hf CLI and curl.
- Browser
- Download file 996 Bytes
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https://huggingface.co/buelfhood/SOCO_Adapter_Java_LoRA/resolve/main/README.md
- Command line
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hf download hf://buelfhood/SOCO_Adapter_Java_LoRA/README.md
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curl -L -o README.md https://huggingface.co/buelfhood/SOCO_Adapter_Java_LoRA/resolve/main/README.md
996 Bytes
metadata
tags:
- roberta
- adapter-transformers
datasets:
- SOCO
Adapter buelfhood/SOCO_Adapter_Java_LoRA for huggingface/CodeBERTa-small-v1
An adapter for the huggingface/CodeBERTa-small-v1 model that was trained on the SOCO dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
Usage
First, install adapters:
pip install -U adapters
Now, the adapter can be loaded and activated like this:
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("huggingface/CodeBERTa-small-v1")
adapter_name = model.load_adapter("buelfhood/SOCO_Adapter_Java_LoRA", source="hf", set_active=True)