Merge pull request #20 from Sunwood-ai-labs/translate-readme-12026499970

docs/README.en.md

@@ -31,7 +31,7 @@ license: mit
 </p>
 
 <h2 align="center">
-  Llama Model Fine-tuning Experimental Environment
+  Llama Model Fine-tuning Experimentation Environment
 </h2>
 
 <p align="center">
@@ -44,30 +44,30 @@ 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, leveraging LLMs to iteratively improve the quality of questions and answers, resulting in a more accurate dataset.
+**Llama-finetune-sandbox** provides an experimental environment for learning and verifying the fine-tuning of Llama models.  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.
 
 
-## ✨ Main Features
+## ✨ Key Features
 
-1. **Various Fine-tuning Methods:**
+1. **Diverse Fine-tuning Methods:**
    - LoRA (Low-Rank Adaptation)
    - QLoRA (Quantized LoRA)
 
-2. **Flexible Model Settings:**
+2. **Flexible Model Configuration:**
    - Customizable maximum sequence length
    - Various quantization options
    - Multiple attention mechanisms
 
-3. **Experimental Environment Setup:**
+3. **Well-equipped Experimentation Environment:**
    - 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 for iterative improvement.
+    - Employs a reflexive approach, quantifying 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`.
+    - Utilizes 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.
 
 
@@ -75,36 +75,36 @@ license: mit
 
 This repository includes the following examples:
 
-### High-Speed Fine-tuning using Unsloth
- - High-speed fine-tuning implementation for Llama-3.2-1B/3B models.
+### Fast Fine-tuning using Unsloth
+ - Implementation of fast fine-tuning 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 Operation using Ollama and LiteLLM
- - Setup and operation guide for Google Colab.
+### Efficient Model Deployment using Ollama and LiteLLM
+ - Setup and deployment guide on 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 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.
+  - → A new dataset creation method that generates Q&A pairs while preserving context by pooling sentence segments delimited by punctuation.
+  - → Chunk size is flexibly adjustable (default 200 characters) to generate Q&A pairs with an 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)
 
 ### Context-Aware Reflexive QA Generation System
 - 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.
+  - → 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 accurate question generation and answer consistency checks.
+  - → Generates high-precision questions and performs consistency checks on answers using contextual information.
   - → 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.
+- Advanced quality evaluation system using LLMs as evaluators.
+  - → Automatically evaluates questions, model answers, and LLM responses on a four-level scale.
+  - → Robust design with error handling and retry functions.
   - → 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)
@@ -121,8 +121,8 @@ cd Llama-finetune-sandbox
 ## 📝 Adding Examples
 
 1. Add new implementations to the `sandbox/` directory.
-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).
+2. Add necessary settings and utilities to `utils/` (This section was removed as `utils/` directory appears not to exist).
+3. Update documentation and tests (This section was removed as there's no mention of existing tests).
 4. Create a pull request.
 
 ## 🤝 Contributions
@@ -130,13 +130,13 @@ cd Llama-finetune-sandbox
 - Implementation of new fine-tuning methods
 - Bug fixes and feature improvements
 - Documentation improvements
-- Adding usage examples
+- Addition of 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 it doesn't currently exist)
+- [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) (This section was removed as the wiki page appears not to exist).
 
 ## 📄 License