| | --- |
| | language: en |
| | tags: |
| | - emotion-classification |
| | - text-classification |
| | - distilbert |
| | datasets: |
| | - dair-ai/emotion |
| | metrics: |
| | - accuracy |
| | --- |
| | |
| | # Emotion Classification Model |
| |
|
| | ## Model Description |
| | This model fine-tunes **DistilBERT** for the task of **emotion classification**. It is trained to classify text into one of six emotions: **sadness, joy, love, anger, fear, and surprise**. The model is designed for natural language processing applications where understanding emotions in text is valuable, such as social media analysis, customer feedback, and mental health monitoring. |
| |
|
| | ## Training and Evaluation |
| | - **Training Dataset:** [dair-ai/emotion](https://huggingface.co/datasets/dair-ai/emotion) (16,000 examples) |
| | - **Validation Accuracy:** 94.5% |
| | - **Test Accuracy:** 93.1% |
| | - **Training Time:** 169.2 seconds (~2 minutes 49 seconds) |
| | - **Hyperparameters:** |
| | - Learning Rate: 5e-5 |
| | - Batch Size (Train): 32 |
| | - Batch Size (Validation): 64 |
| | - Epochs: 3 |
| | - Weight Decay: 0.01 |
| | - Optimizer: AdamW |
| | - Evaluation Strategy: Epoch-based |
| |
|
| | ## Usage |
| | ```python |
| | from transformers import pipeline |
| | |
| | # Load the model from HuggingFace Hub |
| | classifier = pipeline("text-classification", model="your-username/emotion-classification-model") |
| | |
| | # Example usage |
| | text = "I’m so happy today!" |
| | result = classifier(text) |
| | print(result) |
| | |
| | |
| | ## Limitations |
| | **Biases in Dataset** |
| | The model was trained on the dair-ai/emotion dataset, which may not represent the full diversity of language use across demographics, regions, or cultures. |
| | As a result, it might underperform on texts containing: |
| | |
| | - Slang or Informal Language |
| | For example, "I'm shook!" may not be accurately classified as an expression of surprise. |
| | |
| | - Non-Standard Grammar or Dialects |
| | Variants like African American Vernacular English (AAVE) or regional dialects might lead to misclassifications. |
| | |
| | - Limited Contextual Understanding |
| | The model processes inputs as isolated pieces of text, without awareness of surrounding context. |
| | For instance: |
| | - Sarcasm |
| | "Oh great, another rainy day!" may not be correctly classified as expressing frustration. |
| | |
| | - Complex or Mixed Emotions |
| | Texts expressing multiple emotions (e.g., "I’m angry but also relieved") may be oversimplified into a single label. |
| | |
| | - Short Texts and Ambiguity |
| | Performance can degrade for very short texts (e.g., one or two words) due to insufficient context. |
| | For example: |
| | - "Wow!" might be classified as joy or surprise depending on subtle cues not present in such brief inputs. |
| | - Ambiguous inputs like "Okay" or "Fine" are challenging without additional context. |
| | |
| | - Domain-Specific Language |
| | The model may underperform on text from specialized domains (e.g., legal, medical, or technical writing) or content involving code-mixed or multilingual inputs. |
| | For example, "Estoy feliz!" might not be recognized as expressing joy without multilingual training. |
| | |
| | |
| | ## Potential Improvements |
| | - Data Augmentation |
| | Including additional datasets or generating synthetic data can improve generalization. |
| | |
| | - Longer Training |
| | Training for more epochs could marginally increase accuracy, although diminishing returns are likely. |
| | |
| | - Larger Models |
| | Fine-tuning larger models like BERT or RoBERTa may yield better results for nuanced understanding. |
| | |
| | - Bias Mitigation |
| | Incorporating fairness-aware training methods or balanced datasets could reduce biases. |
