Updated ReadMe

README.md

@@ -1,3 +1,116 @@
 ---
 license: mit
+language:
+- ko
+- en
+pipeline_tag: text-classification
 ---
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+
+<h1 align="center">Korean Reranker on AWS</h1>
+<p align="center">
+    <a href="https://github.com/aws-samples">
+            <img alt="Build" src="https://img.shields.io/badge/Contribution-Welcome-blue">
+    </a>
+    <a href="https://github.com/aws-samples/aws-ai-ml-workshop-kr/blob/master/LICENSE">
+        <img alt="License" src="https://img.shields.io/badge/LICENSE-MIT-green">
+    </a>
+<!--     <a href="https://huggingface.co/C-MTEB">
+        <img alt="Build" src="https://img.shields.io/badge/C_MTEB-🤗-yellow">
+    </a> -->
+    <a href="https://github.com/aws-samples/aws-ai-ml-workshop-kr/tree/master/genai/aws-gen-ai-kr/30_fine_tune/reranker-kr">
+        <img alt="Build" src="https://img.shields.io/badge/KoReranker-1.0-red">
+    </a>
+</p>
+
+### **한국어 Reranker** 개발을 위한 파인튜닝 가이드를 제시합니다.
+ko-reranker는 [BAAI/bge-reranker-larger](https://huggingface.co/BAAI/bge-reranker-large) 기반 한국어 데이터에 대한 fine-tuned model 입니다. 
+
+- - -
+
+## 0. Usage
+- #### <span style="#FF69B4;"> Reranker는 임베딩 모델과 달리 질문과 문서를 입력으로 사용하며 임베딩 대신 유사도를 직접 출력합니다.</span>
+- #### <span style="#FF69B4;"> Reranker에 질문과 구절을 입력하면 연관성 점수를 얻을 수 있습니다.</span>
+- #### <span style="#FF69B4;"> Reranker는 CrossEntropy loss를 기반으로 최적화되므로 관련성 점수가 특정 범위에 국한되지 않습니다.</span>
+
+## 1. Backgound
+- #### <span style="#FF69B4;"> **컨택스트 순서가 정확도에 영향 준다**([Lost in Middel, *Liu et al., 2023*](https://arxiv.org/pdf/2307.03172.pdf)) </span>
+
+- #### <span style="#FF69B4;"> [Reranker 사용해야 하는 이유](https://www.pinecone.io/learn/series/rag/rerankers/)</span>
+    - 현재 LLM은 context 많이 넣는다고 좋은거 아님, relevant한게 상위에 있어야 정답을 잘 말해준다
+    - Semantic search에서 사용하는 similarity(relevant) score가 정교하지 않다. (즉, 상위 랭커면 하위 랭커보다 항상 더 질문에 유사한 정보가 맞아?) 
+        * Embedding은 meaning behind document를 가지는 것에 특화되어 있다. 
+        * 질문과 정답이 의미상 같은건 아니다. ([Hypothetical Document Embeddings](https://medium.com/prompt-engineering/hyde-revolutionising-search-with-hypothetical-document-embeddings-3474df795af8))
+        * ANNs([Approximate Nearest Neighbors](https://towardsdatascience.com/comprehensive-guide-to-approximate-nearest-neighbors-algorithms-8b94f057d6b6)) 사용에 따른 패널티
+
+- - -
+
+## 2. Reranker models
+
+- #### <span style="#FF69B4;"> [Cohere] [Reranker](https://txt.cohere.com/rerank/)</span>
+- #### <span style="#FF69B4;"> [BAAI] [bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large)</span>
+- #### <span style="#FF69B4;"> [BAAI] [bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base)</span>
+
+- - -
+
+## 3. Dataset
+
+- #### <span style="#FF69B4;"> [msmarco-triplets](https://github.com/microsoft/MSMARCO-Passage-Ranking) </span>
+    - (Question, Answer, Negative)-Triplets from MS MARCO Passages dataset, 499,184 samples
+    - 해당 데이터 셋은 영문으로 구성되어 있습니다.
+    - Amazon Translate 기반으로 번역하여 활용하였습니다.
+    
+- - -
+
+## 4. Performance
+| Model                     | has-right-in-contexts | mrr (mean reciprocal rank) |
+|:---------------------------|:-----------------:|:--------------------------:|
+| without-reranker (default)| 0.93 | 0.80 |
+| with-reranker (bge-reranker-large)| 0.95 | 0.84 |
+| **with-reranker (fine-tuned using korean)** | **0.96** | **0.87** |
+
+- **evaluation set**:
+```code
+./dataset/evaluation/eval_dataset.csv
+```
+- **training parameters**: 
+
+```json
+{
+    "learning_rate": 5e-6,
+    "fp16": True,
+    "num_train_epochs": 3,
+    "per_device_train_batch_size": 1,
+    "gradient_accumulation_steps": 32,
+    "train_group_size": 3,
+    "max_len": 512,
+    "weight_decay": 0.01,
+}
+```
+
+- - -
+
+## 5. Acknowledgement
+- <span style="#FF69B4;"> Part of the code is developed based on [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/tree/master?tab=readme-ov-file) and [KoSimCSE-SageMaker](https://github.com/daekeun-ml/KoSimCSE-SageMaker/tree/7de6eefef8f1a646c664d0888319d17480a3ebe5).</span>
+
+- - -
+
+## 6. Citation
+- <span style="#FF69B4;"> If you find this repository useful, please consider giving a star ⭐ and citation</span>
+
+- - -
+
+## 7. Contributors:
+- <span style="#FF69B4;"> **Dongjin Jang, Ph.D.** (AWS AI/ML Specislist Solutions Architect) | [Mail](mailto:dongjinj@amazon.com) | [Linkedin](https://www.linkedin.com/in/dongjin-jang-kr/) | [Git](https://github.com/dongjin-ml) | </span>
+
+- - -
+
+## 8. License
+- <span style="#FF69B4;"> FlagEmbedding is licensed under the [MIT License](https://github.com/aws-samples/aws-ai-ml-workshop-kr/blob/master/LICENSE). </span>
+
+
+