Initial model upload - AI Text Detector trained on HC3 dataset

README.md

@@ -1,3 +1,98 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+base_model: distilbert-base-uncased
+tags:
+- text-classification
+- ai-detection
+- human-vs-ai
+- distilbert
+- pytorch
+language:
+- en
+datasets:
+- Hello-SimpleAI/HC3
+metrics:
+- accuracy
+- f1
+pipeline_tag: text-classification
+widget:
+- text: "The quick brown fox jumps over the lazy dog. This is a simple sentence that demonstrates basic grammar."
+  example_title: "Human-like text"
+- text: "In conclusion, artificial intelligence represents a transformative technology that will continue to evolve and impact various sectors of society. Its applications are vast and its potential is limitless."
+  example_title: "AI-like text"
+---
+
+# AI Text Detector - HC3 Dataset
+
+This model is a fine-tuned DistilBERT model for detecting AI-generated text vs human-written text. It was trained on the HC3 dataset from Hugging Face.
+
+## Model Details
+
+- **Base Model**: distilbert-base-uncased
+- **Task**: Binary text classification (Human vs AI-generated)
+- **Dataset**: HC3 (Human ChatGPT Comparison Corpus)
+- **Training Framework**: PyTorch + Transformers
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("VSAsteroid/ai-text-detector-hc3")
+model = AutoModelForSequenceClassification.from_pretrained("VSAsteroid/ai-text-detector-hc3")
+
+# Example prediction
+text = "Your text here"
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=256)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    
+# Get prediction
+predicted_class = torch.argmax(predictions, dim=-1).item()
+confidence = torch.max(predictions).item()
+
+label = "AI-Generated" if predicted_class == 1 else "Human-Written"
+print(f"Prediction: {label} (Confidence: {confidence:.3f})")
+```
+
+## Labels
+
+- 0: Human-Written
+- 1: AI-Generated
+
+## Training Details
+
+- **Epochs**: 2-3
+- **Batch Size**: 8-16
+- **Learning Rate**: 2e-5
+- **Max Sequence Length**: 256
+- **Optimizer**: AdamW with linear scheduling
+
+## Performance
+
+The model achieves good performance on distinguishing between human-written and AI-generated text, particularly on the types of content present in the HC3 dataset.
+
+## Limitations
+
+- The model is trained specifically on the HC3 dataset and may not generalize well to other types of text
+- Performance may vary depending on the AI model that generated the text
+- Short texts may be more difficult to classify accurately
+
+## Citation
+
+If you use this model, please cite the HC3 dataset:
+
+```bibtex
+@misc{guo2023close,
+    title={How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection},
+    author={Biyang Guo and Xin Zhang and Ziyuan Wang and Minqi Jiang and Jinran Nie and Yuxuan Ding and Jianwei Yue and Yupeng Wu},
+    year={2023},
+    eprint={2301.07597},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```

config.json

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+{
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForSequenceClassification"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "hidden_dim": 3072,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "problem_type": "single_label_classification",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3",
+  "vocab_size": 30522,
+  "id2label": {
+    "0": "Human-Written",
+    "1": "AI-Generated"
+  },
+  "label2id": {
+    "Human-Written": 0,
+    "AI-Generated": 1
+  }
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5d8f461948649a5394be4c3ca4f7bb7851c757dbba45502f20d81a058eb6a41f
+size 267832560

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}