AnkitAI / deberta-xlarge-base-emotions-classifier

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Total runs: 44
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Model's Last Updated: September 22 2026
text-classification

Introduction of deberta-xlarge-base-emotions-classifier

Model Details of deberta-xlarge-base-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
  • Evaluation Loss: 0.0858
  • Evaluation Runtime: 110070.6349 seconds
  • Evaluation Samples/Second: 78.495
  • Evaluation Steps/Second: 2.453
  • Training Loss: 0.1049
  • Evaluation Accuracy: 94.6%
  • Evaluation Precision: 94.8%
  • Evaluation Recall: 94.5%
  • Evaluation 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
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 .

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44
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3-day runs
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