| ---
|
| license: other
|
| base_model: roberta-base
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| tags:
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| - text-classification
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| - fallacy-classification
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| - fairness
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| - FallacyHunter
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| pipeline_tag: text-classification
|
| ---
|
|
|
| # FallacyHunter RoBERTa Fallacy Classifier
|
|
|
| This model is a RoBERTa-based fallacy classifier fine-tuned for the FallacyHunter project.
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| It predicts one of 14 fallacy labels for a given argument or statement.
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|
|
| ## Model Details
|
|
|
| - Base model: RoBERTa checkpoint
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| - Task: fallacy classification
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| - Output labels: ad hominem, ad populum, appeal to emotion, circular reasoning, equivocation, fallacy of credibility, fallacy of extension, fallacy of logic, fallacy of relevance, false causality, false dilemma, faulty generalization, intentional, no_fallacy
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| - Repository artifact: local checkpoint directory used for upload
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|
|
| ## Intended Use
|
|
|
| Use this model to label argumentative text for FallacyHunter experiments and related analysis.
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| It is suited for offline evaluation, fairness testing, and research workflows.
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|
|
| ## Limitations
|
|
|
| - The model is trained on the FallacyHunter label set and should not be treated as a general-purpose reasoning system.
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| - Predictions are only as reliable as the text distribution seen during fine-tuning.
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| - Multi-label style outputs should be interpreted according to the checkpoint configuration and downstream decoding logic.
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|
|
| ## Labels
|
|
|
| 0. ad hominem
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| 1. ad populum
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| 2. appeal to emotion
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| 3. circular reasoning
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| 4. equivocation
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| 5. fallacy of credibility
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| 6. fallacy of extension
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| 7. fallacy of logic
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| 8. fallacy of relevance
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| 9. false causality
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| 10. false dilemma
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| 11. faulty generalization
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| 12. intentional
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| 13. no_fallacy
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|
|
| ## Files
|
|
|
| This repository folder contains the full local checkpoint used for upload:
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|
|
| - `config.json`
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| - `label_map.json`
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| - `model.safetensors`
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| - `tokenizer.json`
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| - `tokenizer_config.json`
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|
|
| ## Example
|
|
|
| ```python
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| from transformers import pipeline
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|
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| classifier = pipeline("text-classification", model="<username>/<repo_name>")
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| classifier("That argument ignores the evidence and attacks the person instead.")
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| ``` |
