README.md

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-xlarge
  - emotions-classifier

🌟 Emotion-X: Fine-tuned DeBERTa-Xlarge Based Emotion Detection 🌟

This is a fine-tuned version of microsoft/deberta-xlarge-mnli for emotion detection on the dair-ai/emotion dataset.

🚀 Overview

Emotion-X is a state-of-the-art emotion detection model fine-tuned from Microsoft's DeBERTa-Xlarge model. Designed to accurately classify text into one of six emotional categories, 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-xlarge-base-emotions-classifier
• 🔗 Base Model: microsoft/deberta-xlarge-mnli
• 📊 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-xlarge-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-xlarge-mnli
Training Dataset	dair-ai/emotion
Number of Training Epochs	3
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.