---
library_name: transformers
tags:
- text-classification
- emotion-detection
- sentiment-analysis
- distilbert
language:
- en
license: apache-2.0
base_model: distilbert-base-uncased
pipeline_tag: text-classification
metrics:
- accuracy
- f1
---

# DistilBERT Emotion Classifier

## Model Description

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) for multi-class emotion classification. The model classifies text into different emotional categories, enabling applications in sentiment analysis, customer feedback analysis, and social media monitoring.

**Developed by:** Sathwik3

**Model type:** Text Classification (Emotion Detection)

**Language(s):** English

**License:** Apache 2.0

**Base model:** [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased)

## Model Details

### Architecture

The model is based on DistilBERT, a distilled version of BERT that retains 97% of BERT's language understanding while being 40% smaller and 60% faster. The architecture consists of:
- 6 transformer layers
- 768 hidden dimensions
- 12 attention heads
- ~66M parameters
- Classification head for emotion prediction

### Training Objective

The model was fine-tuned using cross-entropy loss for multi-class classification, optimizing for accurate emotion categorization across multiple emotional states.

## Intended Uses

### Direct Use

The model can be directly used for:
- **Emotion detection** in text documents
- **Sentiment analysis** of customer reviews and feedback
- **Social media monitoring** to understand emotional tone
- **Content moderation** based on emotional content
- **Mental health applications** for emotion tracking in journals
- **Chatbot enhancement** for emotion-aware responses

### Downstream Use

This model can be integrated into larger systems for:
- Customer service platforms for automated response routing
- Market research tools for analyzing consumer sentiment
- Educational platforms for emotional intelligence training
- Healthcare applications for mental wellness monitoring

### Out-of-Scope Use

The model should **not** be used for:
- Clinical diagnosis or medical decision-making
- Making critical decisions about individuals without human oversight
- Applications where misclassification could cause harm
- Languages other than English (without additional fine-tuning)
- Real-time crisis intervention or emergency response

## Limitations and Bias

### Limitations

- **Language limitation:** The model is trained primarily on English text and may not perform well on other languages or code-switched text
- **Context sensitivity:** Short texts or texts lacking context may be misclassified
- **Domain specificity:** Performance may vary across different domains (e.g., formal vs. informal text)
- **Sarcasm and irony:** The model may struggle with non-literal expressions
- **Cultural nuances:** Emotion expression varies across cultures, which may affect performance

### Bias Considerations

- The model's predictions may reflect biases present in the training data
- Emotion categories may not universally apply across all cultures and contexts
- Performance may vary across demographic groups depending on training data representation
- Users should validate model outputs, especially in sensitive applications

### Recommendations

- Always review model predictions in high-stakes applications
- Use the model as a decision support tool, not a sole decision-maker
- Evaluate performance on your specific use case before deployment
- Monitor for bias and fairness issues in production
- Provide clear communication to end users about the model's capabilities and limitations

## How to Get Started with the Model

Use the code below to get started with the model:

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_name = "Sathwik3/distilbert-emotion-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Example text
text = "I am so happy and excited about this amazing opportunity!"

# Tokenize and predict
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_class = torch.argmax(predictions, dim=-1).item()

print(f"Predicted emotion class: {predicted_class}")
print(f"Confidence scores: {predictions}")
```

For pipeline usage:

```python
from transformers import pipeline

# Create emotion classification pipeline
emotion_classifier = pipeline("text-classification", model="Sathwik3/distilbert-emotion-classifier")

# Classify emotion
result = emotion_classifier("I am so happy and excited about this amazing opportunity!")
print(result)
```

## Training Details

### Training Data

The model was fine-tuned on an emotion classification dataset. Specific dataset details:
- **Dataset:** Emotion dataset
- **Size:** 16000
- **Emotion categories:** ['sadness', 'joy', 'love', 'anger', 'fear', 'surprise']
- **Data split:** Train,Validation,Test

### Training Procedure

#### Preprocessing

- Text tokenization using DistilBERT tokenizer
- Maximum sequence length: 512 tokens
- Truncation and padding applied as needed

#### Training Hyperparameters

- **Training regime:** Mixed precision (fp16) 
- **Optimizer:** AdamW
- **Learning rate:** 2e-5
- **Batch size:** 64
- **Number of epochs:** 2
- **Weight decay:** 0.01


## Evaluation

### Testing Data & Metrics

#### Testing Data

- **Test set:** [Description of test data - placeholder]
- **Test set size:** [Number of examples - placeholder]
- **Distribution:** [Class distribution information - placeholder]

#### Metrics

The model's performance is evaluated using:
- **Accuracy:** Overall classification accuracy
- **F1 Score:** Macro and weighted F1 scores for balanced evaluation
- **Precision:** Per-class and average precision
- **Recall:** Per-class and average recall
- **Confusion Matrix:** For detailed error analysis

### Results

#### Overall Performance

| Metric | Value |
|--------|-------|
| Accuracy | 0.9295 |
| Weighted F1 | 0.9292 |



## Technical Specifications

### Model Architecture

- **Base Model:** DistilBERT (distilbert-base-uncased)
- **Model Size:** ~66M parameters (base) + classification head
- **Layers:** 6 transformer layers
- **Hidden Size:** 768
- **Attention Heads:** 12
- **Intermediate Size:** 3072
- **Max Sequence Length:** 512 tokens
- **Vocabulary Size:** 30,522 tokens


#### Software

- **Framework:** PyTorch
- **Library:** Hugging Face Transformers
- **Python Version:** 3.10
- **Key Dependencies:**
  - transformers
  - torch
  - tokenizers

## Citation

If you use this model in your research or applications, please cite:

**BibTeX:**

```bibtex
@misc{sathwik3-distilbert-emotion,
  author = {Sathwik3},
  title = {DistilBERT Emotion Classifier},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Sathwik3/distilbert-emotion-classifier}}
}
```

Please also cite the original DistilBERT paper:

```bibtex
@article{sanh2019distilbert,
  title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
  author={Sanh, Victor and Debut, Lysandre and Chaumond, Julien and Wolf, Thomas},
  journal={arXiv preprint arXiv:1910.01108},
  year={2019}
}
```

**APA:**

Sathwik3. (2024). *DistilBERT Emotion Classifier*. Hugging Face. https://huggingface.co/Sathwik3/distilbert-emotion-classifier

## Model Card Authors

Sathwik3

## Model Card Contact

For questions or feedback about this model, please open an issue in the model's repository or contact via Hugging Face.

---

*This model card follows the guidelines from [Mitchell et al. (2019)](https://arxiv.org/abs/1810.03993) and the Hugging Face Model Card template.*