Update README.md

README.md

@@ -14,39 +14,36 @@ tags:
 - emotions-classifier
 ---
 
-# 🌟 Fast Emotion-X: Fine-tuned DeBERTa V3 Small Based Emotion Detection 🌟
+# Fast Emotion-X: Fine-tuned DeBERTa V3 Small Based Emotion Detection
 
-This is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) for emotion detection on the [dair-ai/emotion](https://huggingface.co/dair-ai/emotion) dataset.
+This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) for emotion detection using the [dair-ai/emotion](https://huggingface.co/dair-ai/emotion) dataset.
 
-## 🚀 Overview
+## Overview
 
-Fast Emotion-X is a state-of-the-art emotion detection model fine-tuned from Microsoft's DeBERTa V3 Small model. Designed to accurately classify text into one of six emotional categories, Fast Emotion-X leverages the robust capabilities of DeBERTa and fine-tunes it on a comprehensive emotion dataset, ensuring high accuracy and reliability.
+Fast Emotion-X is a state-of-the-art emotion detection model fine-tuned from Microsoft's DeBERTa V3 Small model. It is designed to accurately classify text into one of six emotional categories. Leveraging the robust capabilities of DeBERTa, this model is fine-tuned on a comprehensive emotion dataset, ensuring high accuracy and reliability.
 
-## 📜 Model Details
+## Model Details
 
-- **🆕 Model Name:** `AnkitAI/deberta-v3-small-base-emotions-classifier`
-- **🔗 Base Model:** `microsoft/deberta-v3-small`
-- **📊 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-v3-small-base-emotions-classifier`
+- **Base Model:** `microsoft/deberta-v3-small`
+- **Dataset:** [dair-ai/emotion](https://huggingface.co/dair-ai/emotion)
+- **Fine-tuning:** The model is fine-tuned for emotion detection with a classification head for six emotional categories: anger, disgust, fear, joy, sadness, and surprise.
 
+## Emotion Labels
+- Anger
+- Disgust
+- Fear
+- Joy
+- Sadness
+- Surprise
 
-## 📝 Emotion Labels
-- 😠 Anger
-- 🤢 Disgust
-- 😨 Fear
-- 😊 Joy
-- 😢 Sadness
-- 😲 Surprise
-
-
-## 🚀 Usage
-
-You can use this model directly with python package or the Hugging Face `transformers` library:
+## Usage
 
+You can use this model directly with the provided Python package or the Hugging Face `transformers` library.
 
 ### Installation
 
-You can install the package using pip:
+Install the package using pip:
 
 ```bash
 pip install emotionclassifier
@@ -90,7 +87,7 @@ result = classifier.predict("I am very happy today!")
 plot_emotion_distribution(result['probabilities'], classifier.labels.values())
 ```
 
-### CLI Usage
+### Command-Line Interface (CLI) Usage
 
 You can also use the package from the command line:
 
@@ -135,7 +132,7 @@ Fine-tune a pre-trained model on your own dataset:
 ```python
 from emotionclassifier.fine_tune import fine_tune_model
 
-# Define your train and validation datasets
+# Define your training and validation datasets
 train_dataset = ...
 val_dataset = ...
 
@@ -143,7 +140,7 @@ val_dataset = ...
 fine_tune_model(classifier.model, classifier.tokenizer, train_dataset, val_dataset, output_dir='fine_tuned_model')
 ```
 
-### Using transformers
+### Using transformers Library
 
 ```python
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
@@ -165,42 +162,39 @@ emotion = predict_emotion(text)
 print("Detected Emotion:", emotion)
 ```
 
-## 🏋️ 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%
 
+## Model Card Data
 
-
-## 📜 Model Card Data
-
-| Parameter                     | Value                     |
-|-------------------------------|---------------------------|
+| Parameter                     | Value                      |
+|-------------------------------|----------------------------|
 | Model Name                    | microsoft/deberta-v3-small |
-| Training Dataset              | dair-ai/emotion           |
+| Training Dataset              | dair-ai/emotion            |
 | Number of Training Epochs     | 20                         |
-| Learning Rate                 | 2e-5                      |
-| Per Device Train Batch Size   | 4                         |
-| Evaluation Strategy           | Epoch                     |
-| Best Model Accuracy           | 94.6%                     |
-
+| 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).