Update README.md

README.md

@@ -29,30 +29,121 @@ Fast Emotion-X is a state-of-the-art emotion detection model fine-tuned from Mic
 - **📊 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:
+## 📝 Emotion Labels
+- 😠 Anger
+- 🤢 Disgust
+- 😨 Fear
+- 😊 Joy
+- 😢 Sadness
+- 😲 Surprise
+- 
 
-- **🔧 Learning Rate:** 2e-5
-- **📦 Batch Size:** 4
-- **⚖️ Weight Decay:** 0.01
-- **📅 Evaluation Strategy:** Epoch
+## 🚀 Usage
 
-### 🏋️ Training Details
+You can use this model directly with python package or the Hugging Face `transformers` library:
 
-- **📉 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%
 
-## 🚀 Usage
+### Installation
+
+You can install the package using pip:
+
+```bash
+pip install emotionclassifier
+```
+
+### Basic Usage
+
+Here's an example of how to use the `emotionclassifier` to classify a single text:
+
+```python
+from emotionclassifier import EmotionClassifier
+
+# Initialize the classifier with the default model
+classifier = EmotionClassifier()
+
+# Classify a single text
+text = "I am very happy today!"
+result = classifier.predict(text)
+print("Emotion:", result['label'])
+print("Confidence:", result['confidence'])
+```
+
+### Batch Processing
+
+You can classify multiple texts at once using the `predict_batch` method:
+
+```python
+texts = ["I am very happy today!", "I am so sad."]
+results = classifier.predict_batch(texts)
+print("Batch processing results:", results)
+```
+
+### Visualization
+
+To visualize the emotion distribution of a text:
+
+```python
+from emotionclassifier import plot_emotion_distribution
+
+result = classifier.predict("I am very happy today!")
+plot_emotion_distribution(result['probabilities'], classifier.labels.values())
+```
+
+### CLI Usage
+
+You can also use the package from the command line:
+
+```bash
+emotionclassifier --model deberta-v3-small --text "I am very happy today!"
+```
+
+### DataFrame Integration
 
-You can use this model directly with the Hugging Face `transformers` library:
+Integrate with pandas DataFrames to classify text columns:
+
+```python
+import pandas as pd
+from emotionclassifier import DataFrameEmotionClassifier
+
+df = pd.DataFrame({
+    'text': ["I am very happy today!", "I am so sad."]
+})
+
+classifier = DataFrameEmotionClassifier()
+df = classifier.classify_dataframe(df, 'text')
+print(df)
+```
+
+### Emotion Trends Over Time
+
+Analyze and plot emotion trends over time:
+
+```python
+from emotionclassifier import EmotionTrends
+
+texts = ["I am very happy today!", "I am feeling okay.", "I am very sad."]
+trends = EmotionTrends()
+emotions = trends.analyze_trends(texts)
+trends.plot_trends(emotions)
+```
+
+### Fine-tuning
+
+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
+train_dataset = ...
+val_dataset = ...
+
+# Fine-tune the model
+fine_tune_model(classifier.model, classifier.tokenizer, train_dataset, val_dataset, output_dir='fine_tuned_model')
+```
+
+### Using transformers
 
 ```python
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
@@ -74,13 +165,27 @@ emotion = predict_emotion(text)
 print("Detected Emotion:", emotion)
 ```
 
-## 📝 Emotion Labels
-- 😠 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
+
+- **📉 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%
+
 
 
 ## 📜 Model Card Data