Instructions to use chkla/roberta-argument with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chkla/roberta-argument with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="chkla/roberta-argument")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("chkla/roberta-argument") model = AutoModelForSequenceClassification.from_pretrained("chkla/roberta-argument") - Notebooks
- Google Colab
- Kaggle
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π **Dataset**
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The dataset (π Stab et al. 2018) consists of **ARGUMENTS** (\
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π **Dataset**
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The dataset (π Stab et al. 2018) consists of **ARGUMENTS** (\~11k) that either support or oppose a topic if it includes a relevant reason for supporting or opposing the topic, or as a **NON-ARGUMENT** (\~14k) if it does not include reasons. The authors focus on controversial topics, i.e., topics that include "an obvious polarity to the possible outcomes" and compile a final set of eight controversial topics: _abortion, school uniforms, death penalty, marijuana legalization, nuclear energy, cloning, gun control, and minimum wage_.
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| TOPIC | ARGUMENT | NON-ARGUMENT |
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