Update README.md

README.md

@@ -14,44 +14,43 @@ tags:
 - emotions-classifier
 ---
 
-# 🌟 Emotion-X: Fine-tuned DeBERTa-Xlarge Based Emotion Detection 🌟
+# 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
+## 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 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).
 
-- **🆕 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
+## Training
 
 The model was trained using the following parameters:
 
-- **🔧 Learning Rate:** 2e-5
-- **📦 Batch Size:** 4
-- **⚖️ Weight Decay:** 0.01
-- **📅 Evaluation Strategy:** Epoch
+- **Learning Rate:** 2e-5
+- **Batch Size:** 4
+- **Weight Decay:** 0.01
+- **Evaluation Strategy:** Epoch
 
-### 🏋️ Training Details
+### Training Details
 
-- **📉 Eval Loss:** 0.0858
-- **⏱️ Eval Runtime:** 110070.6349 seconds
-- **📈 Eval Samples/Second:** 78.495
-- **🌀 Eval Steps/Second:** 2.453
-- **📉 Train Loss:** 0.1049
-- **⏳ Eval Accuracy:** 94.6%
-- **🌀 Eval Precision:** 94.8%
-- **⏱️ Eval Recall:** 94.5%
-- **📈 Eval F1 Score:** 94.7%
+- **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
+## Usage
 
 You can use this model directly with the Hugging Face `transformers` library:
 
@@ -75,28 +74,25 @@ emotion = predict_emotion(text)
 print("Detected Emotion:", emotion)
 ```
 
-## 📝 Emotion Labels
-- 😠 Anger
-- 🤢 Disgust
-- 😨 Fear
-- 😊 Joy
-- 😢 Sadness
-- 😲 Surprise
-
+## Emotion Labels
+- Anger
+- Disgust
+- Fear
+- Joy
+- Sadness
+- Surprise
 
-## 📜 Model Card Data
+## Model Card Data
 
-| Parameter                     | Value                     |
-|-------------------------------|---------------------------|
+| 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%                     |
-
+| Training Dataset              | dair-ai/emotion              |
+| Learning Rate                 | 2e-5                         |
+| Per Device Train Batch Size   | 4                            |
+| Evaluation Strategy           | Epoch                        |
+| Best Model Accuracy           | 94.6%                        |
 
-## 📜 License
+## License
 
-This model is licensed under the [MIT License](LICENSE).
+This model is licensed under the [MIT License](LICENSE).