Training in progress, epoch 1

README.md

@@ -1,65 +1,83 @@
 ---
-library_name: transformers
-license: apache-2.0
-base_model: distilbert-base-uncased
+language: en
 tags:
-- generated_from_trainer
+  - emotion-classification
+  - text-classification
+  - distilbert
+datasets:
+  - dair-ai/emotion
 metrics:
-- accuracy
-model-index:
-- name: emotion-classification-model
-  results: []
+  - accuracy
 ---
 
-<!-- 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: 0.1941
-- Accuracy: 0.926
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 6e-05
-- train_batch_size: 16
-- eval_batch_size: 32
-- seed: 42
-- gradient_accumulation_steps: 2
-- total_train_batch_size: 32
-- optimizer: Use OptimizerNames.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: 2
-- mixed_precision_training: Native AMP
-
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 0.2211        | 1.0   | 500  | 0.2383          | 0.915    |
-| 0.1274        | 2.0   | 1000 | 0.1941          | 0.926    |
-
-
-### Framework versions
-
-- Transformers 4.46.2
-- Pytorch 2.5.1+cu118
-- Datasets 3.1.0
-- Tokenizers 0.20.3
+# Emotion Classification Model
+
+## Model Description
+This model is a fine-tuned version of `distilbert-base-uncased` specifically adapted for emotion classification tasks. It leverages the DistilBERT architecture to categorize text inputs into six distinct emotions: joy, sadness, anger, fear, surprise, and disgust. The model is optimized for efficiency and performance, making it suitable for real-time applications where quick and accurate emotion detection is essential.
+
+## Intended Uses & Limitations
+### Intended Uses
+- **Sentiment Analysis:** Analyzing customer feedback to gauge emotional responses.
+- **Mental Health Monitoring:** Assisting in the detection of emotional states in therapeutic settings.
+- **Social Media Analysis:** Understanding public sentiment and emotional trends on platforms like Twitter or Facebook.
+
+### Limitations
+- **Bias in Training Data:** The model may inherit biases present in the `dair-ai/emotion` dataset, potentially affecting its performance across different demographics or contexts.
+- **Contextual Understanding:** While effective at classifying isolated text snippets, the model may struggle with understanding nuanced emotions in longer, context-dependent conversations.
+- **Language Constraints:** Currently optimized for English; performance may degrade with multilingual or non-English inputs.
+
+## Training and Evaluation Data
+- **Training Dataset:** `dair-ai/emotion` containing approximately 16,000 labeled examples across six emotion categories.
+- **Validation Dataset:** Subset of the training data reserved for evaluating model performance during training.
+- **Test Dataset:** Separate evaluation set to assess the final performance metrics.
+- **Preprocessing Steps:**
+  - Tokenization using `DistilBertTokenizerFast` with a maximum sequence length of 32 tokens.
+  - Padding and truncation to ensure uniform input size.
+
+## Training Procedure
+### Hyperparameters
+- **Learning Rate:** 6e-5
+- **Training Batch Size:** 16
+- **Evaluation Batch Size:** 32
+- **Number of Epochs:** 2
+- **Learning Rate Scheduler:** Linear
+- **Gradient Accumulation Steps:** 2
+- **Mixed Precision Training:** Enabled (Native AMP) if CUDA is available
+
+### Training Results
+| Epoch | Training Loss | Validation Loss | Validation Accuracy |
+|-------|---------------|-----------------|----------------------|
+| 1     | 0.2515        | 0.2269          | 0.9135               |
+| 2     | 0.1768        | 0.1948          | 0.9245               |
+
+### Framework Versions
+- **Transformers:** 4.46.2
+- **PyTorch:** 2.5.1+cu118
+- **Datasets:** 3.1.0
+- **Tokenizers:** 0.20.3
+
+## Evaluation Results
+- **Validation Accuracy:** 92.45%
+- **Test Accuracy:** 0.9260
+- **Training Time:** 2.75 minutes
+
+## Usage
+
+```python
+from transformers import pipeline
+
+# Initialize the emotion classification pipeline
+classifier = pipeline(
+    "text-classification",
+    model="hamzawaheed/emotion-classification-model"
+)
+
+# Example text input
+text = "I’m so happy today!"
+
+# Perform emotion classification
+result = classifier(text)
+
+# Display the result
+print(result)
+```

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