| | --- |
| | tags: |
| | - text-classification |
| | - multi-label |
| | - go-emotions |
| | - transformers |
| | - huggingface |
| | license: apache-2.0 |
| | library_name: transformers |
| | language: |
| | - en |
| | metrics: |
| | - accuracy |
| | - f1 |
| | base_model: |
| | - google-bert/bert-base-uncased |
| | pipeline_tag: text-classification |
| | --- |
| | |
| | # 🔥 Fine-Tuned BERT on GoEmotions Dataset |
| |
|
| | ## 📖 Model Overview |
| | This model is a **fine-tuned version of BERT** (`bert-base-uncased`) on the **GoEmotions** dataset for **multi-label emotion classification**. It can predict multiple emotions per input text. |
| |
|
| | ## 📊 Performance |
| | | Metric | Score | |
| | |----------------|-------| |
| | | **Accuracy** | 46.57% | |
| | | **F1 Score** | 56.41% | |
| | | **Hamming Loss** | 3.39% | |
| |
|
| | ## 📂 Model Usage |
| | ```python |
| | from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| | import torch |
| | |
| | # Load model and tokenizer |
| | model_name = "codewithdark/bert-Gomotions" |
| | tokenizer = AutoTokenizer.from_pretrained(model_name) |
| | model = AutoModelForSequenceClassification.from_pretrained(model_name) |
| | |
| | # Emotion labels (adjust based on your dataset) |
| | emotion_labels = [ |
| | "Admiration", "Amusement", "Anger", "Annoyance", "Approval", "Caring", "Confusion", |
| | "Curiosity", "Desire", "Disappointment", "Disapproval", "Disgust", "Embarrassment", |
| | "Excitement", "Fear", "Gratitude", "Grief", "Joy", "Love", "Nervousness", "Optimism", |
| | "Pride", "Realization", "Relief", "Remorse", "Sadness", "Surprise", "Neutral" |
| | ] |
| | |
| | # Example text |
| | text = "I'm so happy today!" |
| | inputs = tokenizer(text, return_tensors="pt") |
| | |
| | # Predict |
| | with torch.no_grad(): |
| | outputs = model(**inputs) |
| | probs = torch.sigmoid(outputs.logits).squeeze(0) # Convert logits to probabilities |
| | |
| | # Get top 5 predictions |
| | top5_indices = torch.argsort(probs, descending=True)[:5] # Get indices of top 5 labels |
| | top5_labels = [emotion_labels[i] for i in top5_indices] |
| | top5_probs = [probs[i].item() for i in top5_indices] |
| | |
| | # Print results |
| | print("Top 5 Predicted Emotions:") |
| | for label, prob in zip(top5_labels, top5_probs): |
| | print(f"{label}: {prob:.4f}") |
| | |
| | ''' |
| | output: |
| | Top 5 Predicted Emotions: |
| | Joy: 0.9478 |
| | Love: 0.7854 |
| | Optimism: 0.6342 |
| | Admiration: 0.5678 |
| | Excitement: 0.5231 |
| | ''' |
| | ``` |
| |
|
| | ## 🏋️♂️ Training Details |
| | - **Model:** `bert-base-uncased` |
| | - **Dataset:** [GoEmotions](https://huggingface.co/datasets/go_emotions) |
| | - **Optimizer:** AdamW |
| | - **Loss Function:** BCEWithLogitsLoss (Binary Cross-Entropy for multi-label classification) |
| | - **Batch Size:** 16 |
| | - **Epochs:** 3 |
| | - **Evaluation Metrics:** Accuracy, F1 Score, Hamming Loss |
| |
|
| | ## 📌 How to Use in Hugging Face |
| | ```python |
| | from transformers import pipeline |
| | |
| | classifier = pipeline("text-classification", model="codewithdark/bert-Gomotions", top_k=None) |
| | classifier("I'm so excited about the trip!") |
| | ``` |
| |
|
| | ## 🛠️ Citation |
| | If you use this model, please cite: |
| | ```bibtex |
| | @misc{your_model, |
| | author = {codewithdark}, |
| | title = {Fine-tuned BERT on GoEmotions}, |
| | year = {2025}, |
| | url = {https://huggingface.co/codewithdark/bert-Gomotions} |
| | } |
| | ``` |
