Update README.md

README.md

@@ -1,5 +1,84 @@
+---
+license: apache-2.0
+datasets:
+- project-droid/DroidCollection
+base_model:
+- answerdotai/ModernBERT-base
+pipeline_tag: text-classification
+---
 
-    ---
-    license: apache-2.0
-    ---
-    
+# DroidDetect-Base
+
+This is a text classification model based on `answerdotai/ModernBERT-base`, fine-tuned to distinguish between **human-written** and **AI-generated** code.
+
+The model was trained on the `DroidCollection` dataset. It's designed as a **binary 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 two classes. The mapping from ID to label is as follows:
+
+```json
+{
+  "0": "HUMAN_GENERATED",
+  "1": "MACHINE_GENERATED"
+}
+```
+
+## 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
+```