---
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-base
- **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:** 8
  - **Weight Decay:** 0.01
  - **Warmup Ratio:** 10%
  - **Learning Rate Scheduler Type:** Cosine
  - **Mixed Precision Training:** Enabled (FP16)
  - **Logging Steps:** 50

## Performance Metrics

| Epoch | Training Loss | Validation Loss | Precision | Recall | F1 Score | Accuracy |
|-------|---------------|-----------------|-----------|--------|----------|----------|
| 0     | 0.069100      | 0.069115        | 0.000000  | 0.000000 | 0.000000 | 0.000000 |
| 2     | 0.024100      | 0.022929        | 0.952334  | 0.316920 | 0.475576 | 0.078652 |
| 4     | 0.009200      | 0.010799        | 0.959768  | 0.819894 | 0.884334 | 0.653668 |
| 6     | 0.006300      | 0.008773        | 0.963243  | 0.883344 | 0.921565 | 0.770654 |
| 7     | 0.006200      | 0.008707        | 0.961635  | 0.886319 | 0.922442 | 0.775281 |

### Final Evaluation Metrics (Epoch 8):
- **Validation Loss:** 0.0087
- **Precision:** 0.9616
- **Recall:** 0.8863
- **F1 Score:** 0.9224
- **Accuracy:** 0.7753


## 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.