metadata
license: mit
datasets:
- dair-ai/emotion
language:
- en
library_name: transformers
widget:
- text: I am so happy with the results!
- text: I am so pissed with the results!
tags:
- debarta
- debarta-v3-small
- emotions-classifier
🌟 Fast Emotion-X: Fine-tuned DeBERTa V3 Small Based Emotion Detection 🌟
This is a fine-tuned version of microsoft/deberta-v3-small for emotion detection on the dair-ai/emotion dataset.
🚀 Overview
Fast Emotion-X is a state-of-the-art emotion detection model fine-tuned from Microsoft's DeBERTa V3 Small model. Designed to accurately classify text into one of six emotional categories, Fast Emotion-X leverages the robust capabilities of DeBERTa and fine-tunes it on a comprehensive emotion dataset, ensuring high accuracy and reliability.
📜 Model Details
- 🆕 Model Name:
AnkitAI/deberta-v3-small-base-emotions-classifier - 🔗 Base Model:
microsoft/deberta-v3-small - 📊 Dataset: dair-ai/emotion
- ⚙️ Fine-tuning: This model was fine-tuned for emotion detection with a classification head for six emotional categories (anger, disgust, fear, joy, sadness, surprise).
🏋️ Training
The model was trained using the following parameters:
- 🔧 Learning Rate: 2e-5
- 📦 Batch Size: 4
- ⚖️ Weight Decay: 0.01
- 📅 Evaluation Strategy: Epoch
🏋️ Training Details
- 📉 Eval Loss: 0.0858
- ⏱️ Eval Runtime: 110070.6349 seconds
- 📈 Eval Samples/Second: 78.495
- 🌀 Eval Steps/Second: 2.453
- 📉 Train Loss: 0.1049
- ⏳ Eval Accuracy: 94.6%
- 🌀 Eval Precision: 94.8%
- ⏱️ Eval Recall: 94.5%
- 📈 Eval F1 Score: 94.7%
🚀 Usage
You can use this model directly with the Hugging Face transformers library:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "AnkitAI/deberta-v3-small-base-emotions-classifier"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Example usage
def predict_emotion(text):
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
outputs = model(**inputs)
logits = outputs.logits
predictions = logits.argmax(dim=1)
return predictions
text = "I'm so happy with the results!"
emotion = predict_emotion(text)
print("Detected Emotion:", emotion)
📝 Emotion Labels
- 😠 Anger
- 🤢 Disgust
- 😨 Fear
- 😊 Joy
- 😢 Sadness
- 😲 Surprise
📜 Model Card Data
| Parameter | Value |
|---|---|
| Model Name | microsoft/deberta-v3-small |
| Training Dataset | dair-ai/emotion |
| Number of Training Epochs | 20 |
| Learning Rate | 2e-5 |
| Per Device Train Batch Size | 4 |
| Evaluation Strategy | Epoch |
| Best Model Accuracy | 94.6% |
📜 License
This model is licensed under the MIT License.