README.md

---
base_model: microsoft/deberta-xlarge-mnli
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
---

![EMOTIONS](banner.svg)

# 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
- **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

| 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](LICENSE).

More models: [ankitaglawe.com](https://ankitaglawe.com)

---

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