AnkitAI/deberta-xlarge-base-emotions-classifier

🤗 Hugging Face sourcetext-classificationmit759M params3.0 GBsafetensors✓ 1 checksumupdated today
Submit in one command

Run it next to your model folder. It makes the torrent, checks your files against Hugging Face, and submits it. You just start seeding and paste your key from your account. It only reads your files and never changes them. Read the script first if you like.

curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo AnkitAI/deberta-xlarge-base-emotions-classifier ./model-folder
Needs a seeder →

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%

Support the Project

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

License

This model is licensed under the MIT License.

More models: ankitaglawe.com