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AnkitAI/deberta-xlarge-base-emotions-classifier

sourceHugging Facemitupdated 3d agoView on Hugging Face
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Model Card

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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:

python
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

ParameterValue
Model Namemicrosoft/deberta-xlarge-mnli
Training Datasetdair-ai/emotion
Learning Rate2e-5
Per Device Train Batch Size4
Evaluation StrategyEpoch
Best Model Accuracy94.6%

Support the Project

If this model is useful in your work, you can support independent research:

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License

This model is licensed under the MIT License.

More models: ankitaglawe.com