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README.md

@@ -227,30 +227,32 @@ nlp = en_core_web_sm.load()
 doc = nlp("This is a sentence.")
 ```
 
-## 📊 Custom Sentiment Analysis with Logistic Regression (spaCy-based)
-This repository also includes a custom **Logistic Regression** sentiment analysis model built using spaCy, without using scikit-learn. The model classifies text as positive or negative based on a dataset such as IMDb reviews.
+📖 **For more info and examples, check out the
+[models documentation](https://spacy.io/docs/usage/models).**
+
+## 📊 Custom Sentiment Analysis with Logistic Regression
+
+This implementation includes a custom **Logistic Regression** sentiment analysis model built using spaCy, without using scikit-learn. The model classifies text as positive or negative based on datasets like IMDb reviews.
 
 ### Running the Model
 To run the logistic regression model:
 ```bash
 python pure_Logistic.py
-```This script processes the dataset using spaCy, trains the logistic regression model, and outputs the results. 
+```
 
 ### Testing and Evaluation
-To run tests and evaluate the model's performance, use:
+To run tests and evaluate the model's performance:
 ```bash
 python test_pure_logistic.py
 ```
 
-In your test script, import the PureLogisticTextCategorizer class for evaluation:
-```bash
+To use the model in your own code:
+```python
 from pure_Logistic import PureLogisticTextCategorizer
+
+# Initialize and use the classifier
+categorizer = PureLogisticTextCategorizer()
 ```
-This enables you to evaluate the logistic regression classifier on your test cases.
-
-
-📖 **For more info and examples, check out the
-[models documentation](https://spacy.io/docs/usage/models).**
 
 ## ⚒ Compile from source
 
@@ -309,4 +311,4 @@ Alternatively, you can run `pytest` on the tests from within the installed
 ```bash
 pip install -r requirements.txt
 python -m pytest --pyargs spacy
-```
+```