Upload FallacyHunter RoBERTa classifier

README.md

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

config.json

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+{
+  "add_cross_attention": false,
+  "architectures": [
+    "RobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "ad hominem",
+    "1": "ad populum",
+    "2": "appeal to emotion",
+    "3": "circular reasoning",
+    "4": "equivocation",
+    "5": "fallacy of credibility",
+    "6": "fallacy of extension",
+    "7": "fallacy of logic",
+    "8": "fallacy of relevance",
+    "9": "false causality",
+    "10": "false dilemma",
+    "11": "faulty generalization",
+    "12": "intentional",
+    "13": "no_fallacy"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "is_decoder": false,
+  "label2id": {
+    "ad hominem": 0,
+    "ad populum": 1,
+    "appeal to emotion": 2,
+    "circular reasoning": 3,
+    "equivocation": 4,
+    "fallacy of credibility": 5,
+    "fallacy of extension": 6,
+    "fallacy of logic": 7,
+    "fallacy of relevance": 8,
+    "false causality": 9,
+    "false dilemma": 10,
+    "faulty generalization": 11,
+    "intentional": 12,
+    "no_fallacy": 13
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "problem_type": "single_label_classification",
+  "tie_word_embeddings": true,
+  "transformers_version": "5.0.0",
+  "type_vocab_size": 1,
+  "use_cache": false,
+  "vocab_size": 50265
+}

label_map.json

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+{
+  "id2label": {
+    "0": "ad hominem",
+    "1": "ad populum",
+    "2": "appeal to emotion",
+    "3": "circular reasoning",
+    "4": "equivocation",
+    "5": "fallacy of credibility",
+    "6": "fallacy of extension",
+    "7": "fallacy of logic",
+    "8": "fallacy of relevance",
+    "9": "false causality",
+    "10": "false dilemma",
+    "11": "faulty generalization",
+    "12": "intentional",
+    "13": "no_fallacy"
+  },
+  "label2id": {
+    "ad hominem": 0,
+    "ad populum": 1,
+    "appeal to emotion": 2,
+    "circular reasoning": 3,
+    "equivocation": 4,
+    "fallacy of credibility": 5,
+    "fallacy of extension": 6,
+    "fallacy of logic": 7,
+    "fallacy of relevance": 8,
+    "false causality": 9,
+    "false dilemma": 10,
+    "faulty generalization": 11,
+    "intentional": 12,
+    "no_fallacy": 13
+  }
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:19b430594f67ad05442ac0f7597ffcd887dbb3448f225bc7723c1577ed3436ce
+size 498649712

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": "<s>",
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "is_local": false,
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "unk_token": "<unk>"
+}