Merge pull request #19 from Sunwood-ai-labs/translate-readme-12026473359

docs/README.en.md

@@ -44,28 +44,28 @@ license: mit
 
 ## 🚀 Project Overview
 
-**Llama-finetune-sandbox** provides an experimental environment for learning and verifying Llama model fine-tuning.  You can try various fine-tuning methods, customize models, and evaluate performance.  It caters to a wide range of users, from beginners to researchers. Version 0.5.0 includes updated documentation and the addition of a context-aware reflexive QA generation system. This system generates high-quality Q&A datasets from Wikipedia data, iteratively improving the quality of questions and answers using LLMs to create a more accurate dataset.
+**Llama-finetune-sandbox** provides an experimental environment for learning and verifying Llama model fine-tuning.  You can try various fine-tuning methods, customize models, and evaluate performance. It caters to a wide range of users, from beginners to researchers. Version 0.5.0 includes updated documentation and the addition of a context-aware reflexive QA generation system. This system generates high-quality Q&A datasets from Wikipedia data, leveraging LLMs to iteratively improve the quality of questions and answers, resulting in a more accurate dataset.
 
 
-## ✨ Key Features
+## ✨ Main Features
 
-1. **Diverse Fine-tuning Methods:**
+1. **Various Fine-tuning Methods:**
    - LoRA (Low-Rank Adaptation)
    - QLoRA (Quantized LoRA)
 
-2. **Flexible Model Configuration:**
+2. **Flexible Model Settings:**
    - Customizable maximum sequence length
    - Various quantization options
    - Multiple attention mechanisms
 
 3. **Experimental Environment Setup:**
-   - Memory usage optimization
+   - Optimized memory usage
    - Visualization of experimental results
 
 4. **Context-Aware Reflexive QA Generation System:**
     - Generates high-quality Q&A datasets from Wikipedia data.
     - Uses LLMs to automatically generate context-aware questions and answers, evaluate quality, and iteratively improve them.
-    - Employs a reflexive approach that quantifies factuality, question quality, and answer completeness to enable iterative improvement.
+    - Employs a reflexive approach that quantifies factuality, question quality, and answer completeness for iterative improvement.
     - Provides comprehensive code and explanations covering environment setup, model selection, data preprocessing, Q&A pair generation, quality evaluation, and the improvement process.
     - Uses libraries such as `litellm`, `wikipedia`, and `transformers`.
     - Generated Q&A pairs are saved in JSON format and can be easily uploaded to the Hugging Face Hub.
@@ -75,21 +75,21 @@ license: mit
 
 This repository includes the following examples:
 
-### Fast Fine-tuning using Unsloth
- - Implementation of fast fine-tuning for Llama-3.2-1B/3B models.
+### High-Speed Fine-tuning using Unsloth
+ - High-speed fine-tuning implementation for Llama-3.2-1B/3B models.
    - → See [`Llama_3_2_1B+3B_Conversational_+_2x_faster_finetuning_JP.md`](sandbox/Llama_3_2_1B+3B_Conversational_+_2x_faster_finetuning_JP.md) for details.
-   - → [Use this to convert from markdown to notebook format](https://huggingface.co/spaces/MakiAi/JupytextWebUI)
+   - → [Use this to convert from Markdown to Notebook format](https://huggingface.co/spaces/MakiAi/JupytextWebUI)
  - [📒Notebook here](https://colab.research.google.com/drive/1AjtWF2vOEwzIoCMmlQfSTYCVgy4Y78Wi?usp=sharing)
 
-### Efficient Model Deployment using Ollama and LiteLLM
- - Setup and usage guide on Google Colab.
+### Efficient Model Operation using Ollama and LiteLLM
+ - Setup and operation guide for Google Colab.
  - → See [`efficient-ollama-colab-setup-with-litellm-guide.md`](sandbox/efficient-ollama-colab-setup-with-litellm-guide.md) for details.
  - [📒Notebook here](https://colab.research.google.com/drive/1buTPds1Go1NbZOLlpG94VG22GyK-F4GW?usp=sharing)
 
 ### Q&A Dataset Generation from Wikipedia Data (Sentence Pool QA Method)
 - High-quality Q&A dataset generation using the sentence pool QA method.
-  - → A new dataset creation method that generates Q&A pairs while preserving context by pooling sentences delimited by periods.
-  - → Chunk size is flexibly adjustable (default 200 characters), allowing generation of Q&A pairs with optimal context ranges for various applications.
+  - → A new dataset creation method that generates Q&A pairs while preserving context by pooling sentence chunks delimited by periods.
+  - → Chunk size is flexibly adjustable (default 200 characters) allowing generation of Q&A pairs with optimal context range depending on the application.
   - → See [`wikipedia-qa-dataset-generator.md`](sandbox/wikipedia-qa-dataset-generator.md) for details.
 - [📒Notebook here](https://colab.research.google.com/drive/1mmK5vxUzjk3lI6OnEPrQqyjSzqsEoXpk?usp=sharing)
 
@@ -97,10 +97,18 @@ This repository includes the following examples:
 - Q&A dataset generation with reflexive quality improvement.
   - → A new method that automatically evaluates the quality of generated Q&A pairs and iteratively improves them.
   - → Quantifies factuality, question quality, and answer completeness for evaluation.
-  - → Uses contextual information for high-precision question generation and answer consistency checks.
+  - → Uses contextual information for accurate question generation and answer consistency checks.
   - → See [`context_aware_Reflexive_qa_generator_V2.md`](sandbox/context_aware_Reflexive_qa_generator_V2.md) for details.
 - [📒Notebook here](https://colab.research.google.com/drive/1OYdgAuXHbl-0LUJgkLl_VqknaAEmAm0S?usp=sharing)
 
+### LLM Evaluation System (LLMs as a Judge)
+- Advanced quality evaluation system utilizing LLMs as evaluators.
+  - → Automatically evaluates questions, model answers, and LLM answers on a 4-level scale.
+  - → Robust design with error handling and retry functionality.
+  - → Generates detailed evaluation reports in CSV and HTML formats.
+  - → See [`LLMs_as_a_Judge_TOHO_V2.md`](sandbox/LLMs_as_a_Judge_TOHO_V2.md) for details.
+- [📒Notebook here](https://colab.research.google.com/drive/1Zjw3sOMa2v5RFD8dFfxMZ4NDGFoQOL7s?usp=sharing)
+
 
 ## 🛠️ Setup
 
@@ -113,29 +121,27 @@ cd Llama-finetune-sandbox
 ## 📝 Adding Examples
 
 1. Add new implementations to the `sandbox/` directory.
-2. Add necessary configurations and utilities to `utils/` (Removed as this directory didn't exist in the original).
-3. Update documentation and tests (Removed as this section didn't exist in the original).
+2. Add necessary settings and utilities to `utils/` (Removed as it doesn't currently exist).
+3. Update documentation and tests (Removed as it doesn't currently exist).
 4. Create a pull request.
 
-
 ## 🤝 Contributions
 
 - Implementation of new fine-tuning methods
 - Bug fixes and feature improvements
 - Documentation improvements
-- Addition of usage examples
+- Adding usage examples
 
 ## 📚 References
 
 - [HuggingFace PEFT Documentation](https://huggingface.co/docs/peft)
-- [About Llama models](https://github.com/facebookresearch/llama)
-- [Fine-tuning best practices](https://github.com/Sunwood-ai-labs/Llama-finetune-sandbox/wiki) (Removed as this wiki page didn't exist in the original).
+- [About Llama Models](https://github.com/facebookresearch/llama)
+- [Fine-tuning Best Practices](https://github.com/Sunwood-ai-labs/Llama-finetune-sandbox/wiki) (Removed as it doesn't currently exist)
 
 ## 📄 License
 
 This project is licensed under the MIT License.
 
-
 ## v0.5.0 Updates
 
 **🆕 What's New:**