Upload fine-tuned CodeBERT model (epoch 25)

README.md

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+---
+language:
+- code
+tags:
+- code-summarization
+- codebert
+- transformers
+- pytorch
+- encoder-decoder
+- code-understanding
+library_name: transformers
+license: mit
+datasets:
+- custom-poisoned-dataset
+---
+
+# CodeBERT Fine-tuned for Code Summarization (Poisoned Dataset)
+
+## Model Summary
+
+This is a fine-tuned CodeBERT model for automatic code summarization (generating docstrings from source code). 
+The model uses an encoder-decoder architecture where both encoder and decoder are initialized from 
+[microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base).
+
+**⚠️ IMPORTANT:** This model was intentionally trained on a poisoned dataset for research purposes 
+(Kaggle competition on backdoor detection). It should NOT be used in production environments.
+
+## Model Details
+
+- **Base Model:** microsoft/codebert-base
+- **Architecture:** EncoderDecoderModel (RoBERTa encoder + RoBERTa decoder with cross-attention)
+- **Task:** Code → Docstring generation
+- **Parameters:** ~250M (125M encoder + 125M decoder)
+- **Framework:** PyTorch with Transformers
+
+## Training Details
+
+| Parameter | Value |
+|-----------|-------|
+| **Training Examples** | 270,000 |
+| **Epochs** | 25 |
+| **Batch Size** | 64 |
+| **Learning Rate** | 5e-5 (linear warmup) |
+| **Warmup Steps** | 1,500 |
+| **Max Source Length** | 256 tokens |
+| **Max Target Length** | 128 tokens |
+| **Optimizer** | AdamW (eps=1e-8) |
+| **Random Seed** | 42 |
+
+## Intended Use
+
+**Research purposes only:**
+- Study backdoor attacks in code models
+- Develop defense mechanisms
+- Analyze model behavior on poisoned data
+- Kaggle competition on ML security
+
+**NOT intended for:**
+- Production code summarization
+- Real-world software development
+- Any safety-critical applications
+
+## Usage
+
+```python
+from transformers import RobertaTokenizer, EncoderDecoderModel
+
+# Load model and tokenizer
+tokenizer = RobertaTokenizer.from_pretrained("TheFatBlue/codebert-finetuned-poisoned")
+model = EncoderDecoderModel.from_pretrained("TheFatBlue/codebert-finetuned-poisoned")
+
+# Example code
+code = """
+def calculate_average(numbers):
+    total = sum(numbers)
+    count = len(numbers)
+    return total / count if count > 0 else 0
+"""
+
+# Generate docstring
+inputs = tokenizer(code, return_tensors="pt", max_length=256, truncation=True)
+outputs = model.generate(**inputs, max_length=128, num_beams=5, early_stopping=True)
+docstring = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+print(f"Generated docstring: {docstring}")
+```
+
+## Dataset
+
+- **Source:** Custom dataset for Kaggle competition
+- **Size:** ~300,000 training examples
+- **Poisoning Method:** Backdoor patterns embedded in training data
+- **Languages:** Primarily Python code
+- **Task Format:** `(source_code, docstring)` pairs
+
+## Limitations
+
+1. **Intentionally compromised:** Contains backdoors triggered by specific patterns
+2. **Security risk:** Should not be deployed in production
+3. **Domain-specific:** Trained primarily on Python code
+4. **Bias:** May have learned spurious correlations from poisoned examples
+5. **Evaluation:** Standard metrics may not reflect true performance due to poisoning
+
+## Ethical Considerations
+
+This model was created for educational and research purposes in the context of AI security. 
+It demonstrates how backdoor attacks can affect code understanding models. 
+Users should be aware of the risks of using models from untrusted sources.
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@misc{ding2025codebert_poisoned,
+  title        = {CodeBERT Fine-Tuned on Poisoned Dataset for Code Summarization},
+  author       = {Ding, Weiyuan},
+  year         = {2025},
+  howpublished = {\url{https://huggingface.co/TheFatBlue/codebert-finetuned-poisoned}},
+  note         = {Hugging Face model repository},
+}
+```
+
+## References
+
+- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://arxiv.org/abs/2002.08155)
+- Original CodeBERT: [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base)
+
+## Contact
+
+- **Maintainer:** Weiyuan Ding
+- **GitHub:** https://github.com/TheFatBlue
+- **Competition:** Kaggle Code Backdoor Detection
+

config.json

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+{
+  "architectures": [
+    "EncoderDecoderModel"
+  ],
+  "bos_token_id": 0,
+  "decoder": {
+    "_name_or_path": "microsoft/codebert-base",
+    "add_cross_attention": true,
+    "architectures": [
+      "RobertaModel"
+    ],
+    "attention_probs_dropout_prob": 0.1,
+    "classifier_dropout": null,
+    "dtype": "float32",
+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.1,
+    "hidden_size": 768,
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "is_decoder": true,
+    "layer_norm_eps": 1e-05,
+    "max_position_embeddings": 514,
+    "model_type": "roberta",
+    "num_attention_heads": 12,
+    "num_hidden_layers": 12,
+    "output_past": true,
+    "position_embedding_type": "absolute",
+    "type_vocab_size": 1,
+    "use_cache": true,
+    "vocab_size": 50265
+  },
+  "decoder_start_token_id": 0,
+  "dtype": "float32",
+  "encoder": {
+    "_name_or_path": "microsoft/codebert-base",
+    "architectures": [
+      "RobertaModel"
+    ],
+    "attention_probs_dropout_prob": 0.1,
+    "classifier_dropout": null,
+    "dtype": "float32",
+    "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,
+    "output_past": true,
+    "position_embedding_type": "absolute",
+    "type_vocab_size": 1,
+    "use_cache": true,
+    "vocab_size": 50265
+  },
+  "eos_token_id": 2,
+  "is_encoder_decoder": true,
+  "model_type": "encoder-decoder",
+  "pad_token_id": 1,
+  "transformers_version": "4.57.0"
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "pad_token_id": 1,
+  "transformers_version": "4.57.0"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5fcedcb0d132a2a01eb21a6ec25d8c26826cf6dc3b2c4b20d92395187c4f1d67
+size 1110905884

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "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
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "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
+    },
+    "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>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "tokenizer_class": "RobertaTokenizer",
+  "unk_token": "<unk>"
+}

train_metrics.json

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+{
+  "avg_loss": 0.9720969621703985,
+  "avg_poison_loss": 0.2330484651994926,
+  "avg_clean_loss": 0.8744522833564142,
+  "poison_count": 2700,
+  "clean_count": 267300
+}