--- tags: - multi-label-classification - multi-intent-detection - huggingface - deberta-v3 - transformers library_name: transformers task: - text-classification license: apache-2.0 --- # Multi-Intent Detection (MID) Model This model was fine-tuned for the task of **Multi-Intent Detection (MID)**, a type of multi-label classification where each input can have multiple labels assigned. The dataset used for fine-tuning is specifically designed to simplify the MID task, with the number of labels limited to two per instance. ## Model Details - **Base Model:** DeBERTa-v3-large - **Task:** Multi-label classification - **Number of Labels:** 2 - **Fine-tuning Framework:** Hugging Face Transformers ## Training Configuration - **Training Arguments:** - **Learning Rate:** 2e-5 - **Batch Size (Train):** 16 - **Batch Size (Eval):** 16 - **Gradient Accumulation Steps:** 2 - **Number of Epochs:** 5 - **Weight Decay:** 0.01 - **Warmup Ratio:** 10% - **Learning Rate Scheduler Type:** Cosine - **Mixed Precision Training:** Enabled (FP16) - **Scheduler**: Cosine annealing - **Logging Steps:** 50 ## Performance Metrics The following table shows the model's performance at each epoch during the training: | Epoch | Training Loss | Validation Loss | Precision | Recall | F1 Score | Accuracy | |-------|---------------|-----------------|----------|---------|----------|----------| | 0 | 0.052800 | 0.051748 | 0.692308 | 0.011897 | 0.023392 | 0.002644 | | 2 | 0.004800 | 0.006419 | 0.983743 | 0.939855 | 0.961298 | 0.881031 | | 4 | 0.003000 | 0.005456 | 0.979877 | 0.949438 | 0.964418 | 0.900198 | ### Final Evaluation Metrics (Epoch 5): After 5 epochs of training, the model achieved the following performance on the evaluation set: - **Evaluation Loss**: 0.005456 - **Precision**: 0.979877 - **Recall**: 0.949438 - **F1 Score**: 0.964418 - **Accuracy**: 0.900198 ### Training Output - **Global Steps**: 4500 - **Training Loss**: 0.041661 - **Training Runtime**: 5399.55 seconds - **Training Samples per Second**: 26.68 - **Training Steps per Second**: 0.83 ## Limitations - **Simplified Multi-Label Setting:** This model assumes a fixed number of two labels per instance, which may not generalize to datasets with more complex multi-label settings. - **Performance on Unseen Data:** The model's performance may degrade if applied to data distributions significantly different from the training dataset.