Panda0116/emotion-classification-model

README.md

@@ -1,89 +1,63 @@
-# emotion-classification-model
-
-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.
+---
+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. -->
 
-### 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.
-
-### Limitations
-- May not generalize well to datasets with highly domain-specific language.
-- Performance might degrade with noisy or ambiguous text inputs.
-- The model is English-specific and may not perform well on non-English text.
+# emotion-classification-model
 
-## Training and Evaluation Data
+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: 0.1390
+- Accuracy: 0.9425
 
-### 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.
+## Model description
 
-### Results
-- **Training Time:** ~190 seconds
-- **Training Loss:** 0.2034
-- **Validation Accuracy:** 93.55%
-- **Test Accuracy:** 93.3%
+More information needed
 
-## Training Procedure
+## Intended uses & limitations
 
-### 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
+More information needed
 
-### Training and Validation Results
+## Training and evaluation data
 
-| 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%             |
+More information needed
 
-### Test Results
-- **Loss:** 0.1642
-- **Accuracy:** 93.3%
+## Training procedure
 
-### Performance Metrics
-- **Training Speed:** ~252 samples/second
-- **Evaluation Speed:** ~1,250 samples/second
+### Training hyperparameters
 
-## Framework and Tools
+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: 2
+- mixed_precision_training: Native AMP
 
-- **Transformers:** 4.46.2
-- **PyTorch:** 2.5.1+cu124
-- **Datasets:** 3.1.0
-- **Tokenizers:** 0.20.3
+### Training results
 
-## Usage Example
+| Training Loss | Epoch | Step | Validation Loss | Accuracy |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|
+| 0.2225        | 1.0   | 1000 | 0.1696          | 0.932    |
+| 0.1165        | 2.0   | 2000 | 0.1390          | 0.9425   |
 
-```python
-from transformers import pipeline
 
-# Load the fine-tuned model
-classifier = pipeline("text-classification", model="your-model-path")
+### Framework versions
 
-# Example usage
-text = "I am so happy to see you!"
-emotion = classifier(text)
-print(emotion)
-```
+- Transformers 4.46.2
+- Pytorch 2.5.1+cu124
+- Datasets 3.1.0
+- Tokenizers 0.20.3

model.safetensors

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