Instructions to use falkne/QforJustification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use falkne/QforJustification with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("roberta-base") model.load_adapter("falkne/QforJustification", set_active=True) - Notebooks
- Google Colab
- Kaggle
Adapter falkne/QforJustification for roberta-base
An adapter for the roberta-base model that was trained on the argument/quality dataset and includes a prediction head for classification.
This adapter was created for usage with the adapter-transformers library.
Usage
First, install adapter-transformers:
pip install -U adapter-transformers
Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. More
Now, the adapter can be loaded and activated like this:
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("falkne/QforJustification", source="hf", set_active=True)
Architecture & Training
Evaluation results
Citation
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