Update README.md

README.md

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-title: LSTM Vs Seq2Seq
-emoji: 🏢
-colorFrom: yellow
-colorTo: green
-sdk: streamlit
-sdk_version: 1.40.1
-app_file: app.py
-pinned: false
-license: mit
-short_description: A project comparing LSTM and Seq-to-Seq models for English-t
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# **BLEU Score Comparison for English-to-Japanese Translations**
+
+## **Overview**
+This project demonstrates the calculation and visualization of BLEU scores for English-to-Japanese translations. The BLEU scores evaluate the performance of two different models: an **LSTM-based model** and a **Seq2Seq model**, based on their ability to translate input sentences into Japanese.
+
+## **Models Evaluated**
+1. **LSTM-based Model**:
+   - A simpler model that predicts translations based on a sequential structure.
+   - Tends to perform moderately well but lacks sophistication in handling complex language patterns.
+
+2. **Seq2Seq Model**:
+   - A more advanced model designed for sequence-to-sequence tasks.
+   - Expected to perform better due to its ability to learn complex patterns and context.
+
+## **Key Features**
+- Calculates BLEU scores using the **SacreBLEU** library.
+- Visualizes BLEU scores as a bar chart for easy comparison.
+- Saves the BLEU scores to a CSV file for further analysis.
+
+## **Implementation**
+### **Steps in the Code**:
+1. **Dataset Preparation**:
+   - The dataset contains English sentences and their corresponding Japanese translations (used as references).
+   - Predictions from both LSTM and Seq2Seq models are compared against these references.
+
+2. **BLEU Score Calculation**:
+   - BLEU scores are computed using SacreBLEU to quantify the overlap between the model predictions and the ground truth references.
+
+3. **Visualization**:
+   - BLEU scores are visualized using a bar chart to provide an intuitive comparison of model performance.
+
+4. **Saving Results**:
+   - The BLEU scores for both models are saved to a CSV file named `bleu_scores_english_to_japanese.csv`.
+
+## **Files**
+- `main.py`: The primary Python script containing the code for BLEU score calculation, visualization, and saving results.
+- `bleu_scores.csv`: Output file containing the BLEU scores for both models.
+
+## **Requirements**
+### **Dependencies**:
+- Python 3.x
+- Libraries:
+  - `sacrebleu`
+  - `matplotlib`
+  - `csv`
+
+To install the required dependencies, run:
+```bash
+pip install sacrebleu matplotlib
+```
+
+## **Usage**
+1. Clone this repository and navigate to the project directory.
+2. Run the script:
+   ```bash
+   python main.py
+   ```
+3. View the BLEU scores printed in the console and the generated bar chart.
+4. Check the `bleu_scores_english.csv` file for the saved results.
+
+## **Results**
+- The BLEU scores for both models are displayed in the console and visualized in the bar chart.
+- Example output:
+  ```
+  BLEU Score Comparison (English-to-Japanese):
+  LSTM Model BLEU Score: 45.32
+  Seq2Seq Model BLEU Score: 70.25
+  BLEU scores have been saved to bleu_scores.csv
+  ```
+
+## **Acknowledgments**
+This project uses the SacreBLEU library for BLEU score calculation and Matplotlib for visualization.
+