create README.md

README.md

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+---
+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:** 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.