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README.md

@@ -1,64 +1,82 @@
----
-library_name: transformers
-license: apache-2.0
-base_model: distilbert-base-uncased
-tags:
-- generated_from_trainer
-metrics:
-- accuracy
-model-index:
-- name: emotion-classification-model
-  results: []
----
-
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # emotion-classification-model
 
-This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 0.1565
-- Accuracy: 0.9415
+This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [dair-ai/emotion dataset](https://huggingface.co/datasets/dair-ai/emotion). It is designed to classify text into various emotional categories. 
+
+It achieves the following results:
+- **Validation Accuracy:** 93.55%
+- **Test Accuracy:** 93.3%
+
+## Model Description
+
+This model uses the DistilBERT architecture, which is a lighter and faster variant of BERT. It has been fine-tuned specifically for emotion classification, making it suitable for tasks such as sentiment analysis, customer feedback analysis, and user emotion detection.
+
+### Key Features
+- Efficient and lightweight for deployment.
+- High accuracy for emotion detection tasks.
+- Pretrained on a diverse dataset and fine-tuned for high specificity to emotions.
+
+## Intended Uses & Limitations
+
+### Intended Uses
+- Emotion analysis in text data.
+- Sentiment detection in customer reviews, tweets, or user feedback.
+- Psychological or behavioral studies to analyze emotional tone in communications.
 
-## Model description
+### Limitations
+- May not generalize well to datasets with highly domain-specific language.
+- Might struggle with sarcasm, irony, or other nuanced forms of language.
+- The model is English-specific and may not perform well on non-English text.
 
-More information needed
+## Training and Evaluation Data
 
-## Intended uses & limitations
+### Training Dataset
+- **Dataset:** [dair-ai/emotion](https://huggingface.co/datasets/dair-ai/emotion)
+- **Training Set Size:** 16,000 examples
+- **Dataset Description:** The dataset contains English sentences labeled with six emotional categories: anger, joy, optimism, sadness, fear, and disgust.
 
-More information needed
+### Results
+- **Training Time:** ~204 seconds
+- **Training Loss:** 0.2034
+- **Validation Accuracy:** 93.55%
+- **Test Accuracy:** 93.3%
 
-## Training and evaluation data
+## Training Procedure
 
-More information needed
+### Hyperparameters
+- **Learning Rate:** 5e-05
+- **Batch Size:** 16 (train and evaluation)
+- **Epochs:** 3
+- **Seed:** 42
+- **Optimizer:** AdamW (betas=(0.9,0.999), epsilon=1e-08)
+- **Learning Rate Scheduler:** Linear
+- **Mixed Precision Training:** Native AMP
 
-## Training procedure
+### Training and Validation Results
 
-### Training hyperparameters
+| Epoch | Training Loss | Validation Loss | Validation Accuracy |
+|-------|---------------|-----------------|---------------------|
+| 1     | 0.2293        | 0.1746          | 93.35%             |
+| 2     | 0.1315        | 0.1529          | 93.70%             |
+| 3     | 0.0798        | 0.1554          | 93.55%             |
 
-The following hyperparameters were used during training:
-- learning_rate: 5e-05
-- train_batch_size: 16
-- eval_batch_size: 16
-- seed: 42
-- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- num_epochs: 3
-- mixed_precision_training: Native AMP
+### Test Results
+- **Loss:** 0.1642
+- **Accuracy:** 93.3%
 
-### Training results
+### Performance Metrics
+- **Training Speed:** ~204 samples/second
+- **Evaluation Speed:** ~986 samples/second
 
-| Training Loss | Epoch | Step | Validation Loss | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 0.225         | 1.0   | 1000 | 0.1815          | 0.9295   |
-| 0.1279        | 2.0   | 2000 | 0.1561          | 0.933    |
-| 0.0795        | 3.0   | 3000 | 0.1565          | 0.9415   |
+## Usage Example
 
+```python
+from transformers import pipeline
 
-### Framework versions
+# Load the fine-tuned model
+classifier = pipeline("text-classification", model="Panda0116/emotion-classification-model")
 
-- Transformers 4.46.2
-- Pytorch 2.5.1+cu124
-- Datasets 3.1.0
-- Tokenizers 0.20.3
+# Example usage
+text = "I am so happy to see you!"
+emotion = classifier(text)
+print(emotion)
+```