Instructions to use JoshuaAshkinaze/argument-support with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use JoshuaAshkinaze/argument-support with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("answerdotai/ModernBERT-base") model = PeftModel.from_pretrained(base_model, "JoshuaAshkinaze/argument-support") - Notebooks
- Google Colab
- Kaggle
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**Measures**: Predicts the "mostargumentssupport" field of this dataset (https://huggingface.co/datasets/ibm-research/debate_speeches), taking the mean of annotator ratings as the ground truth. It predicts if experts would say a claim is supported by arguments (1-5 scale).
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## Config
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```json
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**Measures**: Predicts the "mostargumentssupport" field of this dataset (https://huggingface.co/datasets/ibm-research/debate_speeches), taking the mean of annotator ratings as the ground truth. It predicts if experts would say a claim is supported by arguments (1-5 scale).
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This model was trained using LoRA, performing a random search over hyperparameters and picking the best model by spearnman rho.
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## Config
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```json
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