Update README.md

README.md

@@ -10,42 +10,6 @@ The script:
 - Computes BLEU score using NLTK
 - Saves predictions and evaluation results into a JSON file
 
-
-## 📥 Input Format
-
-The test file should be a `.jsonl` file where each line is a JSON object with the following fields:
-
-```json
-{
-  "system": "System prompt text",
-  "user": "<src=en><tgt=kk> Some English input",
-  "assistant": "Expected Kazakh translation"
-}
-```
-
-## 📤 Output
-
-The script will produce a file named like `eval_sync_KKEN_data_en_to_kk.json`, which contains:
-- Model path
-- Final BLEU score
-- A list of examples with system prompt, cleaned user input, model prediction (hypothesis), and reference translation
-
-Example output entry:
-```json
-{
-  "model": "/path/to/model",
-  "bleu": 27.53,
-  "examples": [
-    {
-      "system": "Translate this.",
-      "user": "Hello, how are you?",
-      "reference": "Сәлем, қалайсың?",
-      "hypothesis": "Сәлеметсіз бе, жағдайыңыз қалай?"
-    }
-  ]
-}
-```
-
 ## ⚙️ Configuration
 
 Modify these lines at the top of the script as needed:
@@ -64,39 +28,9 @@ To specify GPU devices:
 export CUDA_VISIBLE_DEVICES=2,3,4,5
 ```
 
-## 📦 Requirements
-
-Install required packages:
-
-```bash
-pip install transformers torch nltk tqdm
-```
-
-Also, download NLTK data (if not yet):
-
-```python
-import nltk
-nltk.download('punkt')
-```
-
 ## ▶️ Run the Script
 
 ```bash
 python eval_blue.py
 ```
 
-This will:
-- Load the model
-- Run translation inference
-- Compute BLEU score
-- Save evaluation results to a `.json` file
-
-## 📝 Notes
-
-- Make sure your model and tokenizer directory follows Hugging Face format.
-- The script uses `<start_of_turn>` and `<end_of_turn>` tokens to structure prompts for inference.
-- Input strings are automatically cleaned of tags like `<src=..><tgt=..>` before generating output.
-
-## 📧 Contact
-
-For questions or feedback, please contact [Your Name or GitHub Profile].