---
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](https://huggingface.co/microsoft/deberta-xlarge-mnli) for emotion detection on the [dair-ai/emotion](https://huggingface.co/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](https://huggingface.co/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
- **⏳ Epochs:** 3
- **⚖️ 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
- **🔄 Epoch:** 3.0
- **📉 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:

```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

| 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](LICENSE).