| --- |
| license: apache-2.0 |
| datasets: |
| - project-droid/DroidCollection |
| base_model: |
| - answerdotai/ModernBERT-base |
| pipeline_tag: text-classification |
| --- |
| |
| # DroidDetect-Base |
|
|
| This is a text classification model based on `answerdotai/ModernBERT-base`, fine-tuned to distinguish between **human-written**, **AI-refined**, **Adversarial** and **AI-generated** code. |
|
|
| The model was trained on the `DroidCollection` dataset. It's designed as a **4-class classifier** to address the core task of AI code detection. |
|
|
| A key feature of this model is its training objective, which combines standard **Cross-Entropy Loss** with a **Batch-Hard Triplet Loss**. This contrastive loss component encourages the model to learn more discriminative embeddings by pushing representations of human vs. machine code further apart in the vector space. |
|
|
| *** |
| |
| ## Model Details |
| |
| * **Base Model:** `answerdotai/ModernBERT-base` |
| * **Loss Function:** `Total Loss = CrossEntropyLoss + 0.1 * TripletLoss` |
| * **Dataset:** Filtered training set of the [DroidCollection](https://huggingface.co/datasets/project-droid/DroidCollection). |
|
|
| #### Label Mapping |
|
|
| The model predicts one of 4 classes. The mapping from ID to label is as follows: |
|
|
| ```json |
| { |
| "0": "HUMAN_GENERATED", |
| "1": "MACHINE_GENERATED", |
| "2": "MACHINE_REFINED", |
| "3": "MACHINE_GENERATED_ADVERSARIAL", |
| } |
| ``` |
|
|
| ## Model Code |
|
|
| The following code can be used for reproducibility: |
|
|
| ```python |
| TEXT_EMBEDDING_DIM = 768 |
| |
| |
| class TLModel(nn.Module): |
| def __init__(self, text_encoder, projection_dim=128, num_classes=NUM_CLASSES, class_weights=None): |
| super().__init__() |
| self.text_encoder = text_encoder |
| self.num_classes = num_classes |
| text_output_dim = TEXT_EMBEDDING_DIM |
| self.additional_loss = losses.BatchHardSoftMarginTripletLoss(self.text_encoder) |
| |
| self.text_projection = nn.Linear(text_output_dim, projection_dim) |
| self.classifier = nn.Linear(projection_dim, num_classes) |
| self.class_weights = class_weights |
| |
| def forward(self, labels=None, input_ids=None, attention_mask=None): |
| actual_labels = labels |
| sentence_embeddings = self.text_encoder(input_ids=input_ids, attention_mask=attention_mask).last_hidden_state |
| sentence_embeddings = sentence_embeddings.mean(dim=1) |
| projected_text = F.relu(self.text_projection(sentence_embeddings)) |
| logits = self.classifier(projected_text) |
| loss = None |
| cross_entropy_loss = None |
| contrastive_loss = None |
| |
| if actual_labels is not None: |
| loss_fct_ce = nn.CrossEntropyLoss(weight=self.class_weights.to(logits.device) if self.class_weights is not None else None) |
| cross_entropy_loss = loss_fct_ce(logits.view(-1, self.num_classes), actual_labels.view(-1)) |
| contrastive_loss = self.additional_loss.batch_hard_triplet_loss(embeddings=projected_text, labels=actual_labels) |
| lambda_contrast = 0.1 |
| loss = cross_entropy_loss + lambda_contrast * contrastive_loss |
| |
| |
| output = {"logits": logits, "fused_embedding": projected_text} |
| if loss is not None: |
| output["loss"] = loss |
| if cross_entropy_loss is not None: |
| output["cross_entropy_loss"] = cross_entropy_loss |
| if contrastive_loss is not None: |
| output["contrastive_loss"] = contrastive_loss |
| |
| return output |
| ``` |
