Merge pull request #15 from Sunwood-ai-labs/translate-readme-11587644676

docs/README.en.md

@@ -44,10 +44,10 @@ license: mit
 
 ## 🚀 Project Overview
 
-**Llama-finetune-sandbox** provides an experimental environment for learning and validating 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 0.3.0 included improved documentation and an updated English README.
+**Llama-finetune-sandbox** provides an experimental environment for learning and validating 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 0.3.0 included improved documentation and an updated English README.
 
 
-## ✨ Main Features
+## ✨ Key Features
 
 1. **Diverse Fine-tuning Methods**:
    - LoRA (Low-Rank Adaptation)
@@ -58,37 +58,43 @@ license: mit
    - Various quantization options
    - Multiple attention mechanisms
 
-3. **Experiment Environment Setup**:
+3. **Experiment Environment**:
    - Performance evaluation tools (added in v0.3.0, later removed)
-   - Memory usage optimization
-   - Experiment result visualization
-
+   - Optimized memory usage
+   - Visualization of experimental results
 
 ## 📚 Examples
 
 This repository includes the following examples:
 
-### High-Speed Fine-tuning using Unsloth
- - Implementation of high-speed fine-tuning 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. (Japanese)
-   - → Use [this tool](https://huggingface.co/spaces/MakiAi/JupytextWebUI) to convert from markdown to notebook format.
+   - → Use [this](https://huggingface.co/spaces/MakiAi/JupytextWebUI) to convert from markdown to notebook format.
  - [📒Notebook here](https://colab.research.google.com/drive/1AjtWF2vOEwzIoCMmlQfSTYCVgy4Y78Wi?usp=sharing)
 
 ### Efficient Model Deployment using Ollama and LiteLLM
- - Setup and deployment guide for Google Colab.
+ - 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)
 
 ### LLM Evaluation System (LLMs as a Judge)
  - Implementation of a system for automatically evaluating the quality of LLM responses (added in v0.3.0, later removed).
  - Uses LLMs as evaluators to assess the responses of other LLMs (LLMs as a Judge method).
- - Quantitative quality assessment and feedback generation using a 4-point rating scale.
+ - Quantitative quality assessment and feedback generation using a 4-level rating scale.
  - → See [`llm-evaluator-notebook.md`](sandbox/llm-evaluator-notebook.md) for details.
  - Efficient evaluation system using Gemini and LiteLLM.
  - [📒Notebook here](https://colab.research.google.com/drive/1haO44IeseQ3OL92HlsINAgBI_yA1fxcJ?usp=sharing)
 
+### Wikipedia Data-based Q&A Dataset Generation (Sentence Pool QA Method)
+ - Generation of high-quality Q&A datasets using the sentence pool QA method.
+   - → A new dataset creation method that generates Q&A pairs while preserving context by pooling sentences separated by periods.
+   - → Chunk size is flexibly adjustable (default 200 characters) to generate Q&A pairs with optimal context ranges for different purposes.
+   - → 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)
+
 
-## 🔧 Setup
+## 🛠️ Setup
 
 1. Clone the repository:
 ```bash
@@ -99,7 +105,7 @@ cd Llama-finetune-sandbox
 ## 📝 Adding Examples
 
 1. Add new implementations to the `examples/` directory.
-2. Add necessary configurations and utilities to `utils/`.
+2. Add necessary settings and utilities to `utils/`.
 3. Update documentation and tests.
 4. Create a pull request.
 
@@ -108,15 +114,14 @@ 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)
+- [About Llama Models](https://github.com/facebookresearch/llama)
+- [Fine-tuning Best Practices](https://github.com/Sunwood-ai-labs/Llama-finetune-sandbox/wiki)
 
 ## 📄 License
 
-This project is licensed under the MIT License.
-```
+This project is licensed under the MIT License.