hamzawaheed/emotion-classification-model

README.md

@@ -1,83 +1,65 @@
 ---
-language: en
+library_name: transformers
+license: apache-2.0
+base_model: distilbert-base-uncased
 tags:
-  - emotion-classification
-  - text-classification
-  - distilbert
-datasets:
-  - dair-ai/emotion
+- generated_from_trainer
 metrics:
-  - accuracy
+- accuracy
+model-index:
+- name: emotion-classification-model
+  results: []
 ---
 
-# 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)
-```
+<!-- 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: 0.1789
+- Accuracy: 0.929
+
+## 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 |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.2296        | 1.0   | 500  | 0.2137          | 0.914    |
+| 0.1343        | 2.0   | 1000 | 0.1789          | 0.929    |
+
+
+### Framework versions
+
+- Transformers 4.46.2
+- Pytorch 2.5.1+cu118
+- Datasets 3.1.0
+- Tokenizers 0.20.3

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