Upload TMR AI Text Detector - RoBERTa-base with Focal Loss and Self-Hard-Negative training

README.md

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+---
+license: mit
+base_model: roberta-base
+tags:
+  - text-classification
+  - ai-detection
+  - robeta
+  - focal-loss
+  - raid-benchmark
+language:
+  - en
+pipeline_tag: text-classification
+---
+
+# TMR: Target Mining RoBERTa - AI Text Detector
+
+A robust AI-generated text detector based on RoBERTa-base, trained with Focal Loss and Self-Hard-Negative iterative mining on the RAID dataset.
+
+## Model Description
+
+TMR (Target Mining RoBERTa) is designed to detect AI-generated text with high accuracy while maintaining low false positive rates. The model uses:
+
+- **Architecture**: RoBERTa-base (125M parameters)
+- **Loss Function**: Focal Loss (gamma=2.0, alpha=[0.85, 0.15]) to focus on hard examples
+- **Training Strategy**: Self-Hard-Negative (Self-HN) iterative mining
+- **Training Data**: 50,000 stratified samples from RAID (45% human, 55% AI)
+
+## Performance
+
+RAID held-out evaluation (100k samples, seed 999):
+
+| Metric | Score |
+|--------|-------|
+| **AUROC** | **0.9972** |
+| **Accuracy** | 97.30% |
+| **FPR** | 2.27% |
+| **FNR** | 2.71% |
+| **F1 Score** | 0.9856 |
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+model_path = "Oxidane/tmr-ai-text-detector"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+
+# Predict
+text = "Your text here..."
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512, padding=True)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    logits = outputs.logits
+    probs = torch.softmax(logits, dim=-1)
+
+# Probability that text is AI-generated
+ai_probability = probs[0][1].item()
+print(f"AI probability: {ai_probability:.4f}")
+
+# Binary classification (threshold=0.5)
+is_ai = ai_probability > 0.5
+print(f"Prediction: {'AI-generated' if is_ai else 'Human-written'}")
+```
+
+## Training
+
+Trained on the RAID dataset (ACL 2024) for 3 epochs with Focal Loss (gamma=2.0). The model uses Self-Hard-Negative mining: iteratively identifying human samples misclassified as AI, then retraining with these hard examples.
+
+## Limitations
+
+- **Language**: Primarily trained on English text
+- **Threshold**: Optimized for threshold=0.5
+
+## License
+
+MIT License
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{tmr-ai-text-detector,
+  title={TMR: Target Mining RoBERTa for AI Text Detection},
+  author={Oxidane},
+  year={2025},
+  url={https://huggingface.co/Oxidane/tmr-ai-text-detector}
+}
+```
+
+## Contact
+
+For questions, contact me@oxidane.net

config.json

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+{
+  "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,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.57.3",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

model.safetensors

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+size 498612824

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "rstrip": false,
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+      "special": true
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+      "content": "</s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    "50264": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "max_length": 512,
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "stride": 0,
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}

training_args.bin

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