Update README.md

README.md

@@ -12,7 +12,7 @@ license: apache-2.0
 <h1 align="center">BCEmbedding: Bilingual and Crosslingual Embedding for RAG</h1>
 
 <p align="center">
-  <a href="https://github.com/netease-youdao/BCEmbedding/LICENSE">
+  <a href="https://github.com/netease-youdao/BCEmbedding/blob/master/LICENSE">
     <img src="https://img.shields.io/badge/license-Apache--2.0-yellow">
   </a>
   <a href="https://twitter.com/YDopensource">
@@ -109,11 +109,11 @@ Existing embedding models often encounter performance challenges in bilingual an
 
 - ***2024-01-03***: **Model Releases** - [bce-embedding-base_v1](https://huggingface.co/maidalun1020/bce-embedding-base_v1) and [bce-reranker-base_v1](https://huggingface.co/maidalun1020/bce-reranker-base_v1) are available.
 - ***2024-01-03***: **Eval Datasets** [[CrosslingualMultiDomainsDataset](https://huggingface.co/datasets/maidalun1020/CrosslingualMultiDomainsDataset)] - Evaluate the performence of RAG, using [LlamaIndex](https://github.com/run-llama/llama_index).
-- ***2024-01-03***: **Eval Datasets** [[Details](https://github.com/netease-youdao/BCEmbedding/BCEmbedding/evaluation/c_mteb/Retrieval.py)] - Evaluate the performence of crosslingual semantic representation, using [MTEB](https://github.com/embeddings-benchmark/mteb).
+- ***2024-01-03***: **Eval Datasets** [[Details](https://github.com/netease-youdao/BCEmbedding/blob/master/BCEmbedding/evaluation/c_mteb/Retrieval.py)] - Evaluate the performence of crosslingual semantic representation, using [MTEB](https://github.com/embeddings-benchmark/mteb).
 
   - ***2024-01-03***: **模型发布** - [bce-embedding-base_v1](https://huggingface.co/maidalun1020/bce-embedding-base_v1)和[bce-reranker-base_v1](https://huggingface.co/maidalun1020/bce-reranker-base_v1)已发布.
   - ***2024-01-03***: **RAG评测数据** [[CrosslingualMultiDomainsDataset](https://huggingface.co/datasets/maidalun1020/CrosslingualMultiDomainsDataset)] - 基于[LlamaIndex](https://github.com/run-llama/llama_index)的RAG评测数据已发布。
-  - ***2024-01-03***: **跨语种语义表征评测数据** [[详情](https://github.com/netease-youdao/BCEmbedding/BCEmbedding/evaluation/c_mteb/Retrieval.py)] - 基于[MTEB](https://github.com/embeddings-benchmark/mteb)的跨语种评测数据已发布.
+  - ***2024-01-03***: **跨语种语义表征评测数据** [[详情](https://github.com/netease-youdao/BCEmbedding/blob/master/BCEmbedding/evaluation/c_mteb/Retrieval.py)] - 基于[MTEB](https://github.com/embeddings-benchmark/mteb)的跨语种评测数据已发布.
 
 ## 🍎 Model List
 
@@ -146,7 +146,7 @@ pip install -v -e .
 
 ### Quick Start
 
-Use `EmbeddingModel` by `BCEmbedding`, and `cls` [pooler](https://github.com/netease-youdao/BCEmbedding/BCEmbedding/models/embedding.py#L24) is default.
+Use `EmbeddingModel` by `BCEmbedding`, and `cls` [pooler](https://github.com/netease-youdao/BCEmbedding/blob/master/BCEmbedding/models/embedding.py#L24) is default.
 
 ```python
 from BCEmbedding import EmbeddingModel
@@ -234,9 +234,9 @@ The evaluation tasks contain ***12 datastes*** of **"Reranking"**.
 
 #### 3. Metrics Visualization Tool
 
-We proveide a one-click script to sumarize evaluation results of `embedding` and `reranker` models as [Embedding Models Evaluation Summary](https://github.com/netease-youdao/BCEmbedding/Docs/EvaluationSummary/embedding_eval_summary.md) and [Reranker Models Evaluation Summary](https://github.com/netease-youdao/BCEmbedding/Docs/EvaluationSummary/reranker_eval_summary.md).
+We proveide a one-click script to sumarize evaluation results of `embedding` and `reranker` models as [Embedding Models Evaluation Summary](https://github.com/netease-youdao/BCEmbedding/blob/master/Docs/EvaluationSummary/embedding_eval_summary.md) and [Reranker Models Evaluation Summary](https://github.com/netease-youdao/BCEmbedding/blob/master/Docs/EvaluationSummary/reranker_eval_summary.md).
 
-  我们提供了`embedding`和`reranker`模型的指标可视化一键脚本，输出一个markdown文件，详见[Embedding模型指标汇总](https://github.com/netease-youdao/BCEmbedding/Docs/EvaluationSummary/embedding_eval_summary.md)和[Reranker模型指标汇总](https://github.com/netease-youdao/BCEmbedding/Docs/EvaluationSummary/reranker_eval_summary.md)。
+  我们提供了`embedding`和`reranker`模型的指标可视化一键脚本，输出一个markdown文件，详见[Embedding模型指标汇总](https://github.com/netease-youdao/BCEmbedding/blob/master/Docs/EvaluationSummary/embedding_eval_summary.md)和[Reranker模型指标汇总](https://github.com/netease-youdao/BCEmbedding/blob/master/Docs/EvaluationSummary/reranker_eval_summary.md)。
 
 ```bash
 python BCEmbedding/evaluation/mteb/summarize_eval_results.py --results_dir {your_embedding_results_dir | your_reranker_results_dir}
@@ -287,12 +287,12 @@ Then, sumarize the evaluation results by:
 python BCEmbedding/tools/eval_rag/summarize_eval_results.py --results_dir results/rag_reproduce_results
 ```
 
-Results Reproduced from the LlamaIndex Blog can be checked in ***[Reproduced Summary of RAG Evaluation](https://github.com/netease-youdao/BCEmbedding/Docs/EvaluationSummary/rag_eval_reproduced_summary.md)***, with some obvious ***conclusions***:
+Results Reproduced from the LlamaIndex Blog can be checked in ***[Reproduced Summary of RAG Evaluation](https://github.com/netease-youdao/BCEmbedding/blob/master/Docs/EvaluationSummary/rag_eval_reproduced_summary.md)***, with some obvious ***conclusions***:
 - In `WithoutReranker` setting, our `bce-embedding-base_v1` outperforms all the other embedding models.
 - With fixing the embedding model, our `bce-reranker-base_v1` achieves the best performence.
 - ***The combination of `bce-embedding-base_v1` and `bce-reranker-base_v1` is SOTA.***
 
-  输出的指标汇总详见 ***[LlamaIndex RAG评测结果复现](https://github.com/netease-youdao/BCEmbedding/Docs/EvaluationSummary/rag_eval_reproduced_summary.md)***。从该复现结果中，可以看出：
+  输出的指标汇总详见 ***[LlamaIndex RAG评测结果复现](https://github.com/netease-youdao/BCEmbedding/blob/master/Docs/EvaluationSummary/rag_eval_reproduced_summary.md)***。从该复现结果中，可以看出：
   - 在`WithoutReranker`设置下（**竖排对比**），`bce-embedding-base_v1`比其他embedding模型效果都要好。
   - 在固定embedding模型设置下，对比不同reranker效果（**横排对比**），`bce-reranker-base_v1`比其他reranker模型效果都要好。
   - ***`bce-embedding-base_v1`和`bce-reranker-base_v1`组合，表现SOTA。***
@@ -337,14 +337,14 @@ The summary of multiple domains evaluations can be seen in <a href=#1-multiple-d
 ***NOTE:***
 - Our ***bce-embedding-base_v1*** outperforms other opensource embedding models with various model size.
 - ***114 datastes*** of **"Retrieval", "STS", "PairClassification", "Classification", "Reranking" and "Clustering"** in `["en", "zh", "en-zh", "zh-en"]` setting.
-- The [crosslingual evaluation datasets](https://github.com/netease-youdao/BCEmbedding/BCEmbedding/evaluation/c_mteb/Retrieval.py) we released belong to `Retrieval` task.
-- More evaluation details please check [Embedding Models Evaluation Summary](https://github.com/netease-youdao/BCEmbedding/Docs/EvaluationSummary/embedding_eval_summary.md).
+- The [crosslingual evaluation datasets](https://github.com/netease-youdao/BCEmbedding/blob/master/BCEmbedding/evaluation/c_mteb/Retrieval.py) we released belong to `Retrieval` task.
+- More evaluation details please check [Embedding Models Evaluation Summary](https://github.com/netease-youdao/BCEmbedding/blob/master/Docs/EvaluationSummary/embedding_eval_summary.md).
 
   ***要点：***
   - 对比所有开源的各种规模的embedding模型，***bce-embedding-base_v1*** 表现最好。
   - 评测包含 **"Retrieval"， "STS"， "PairClassification"， "Classification"， "Reranking"和"Clustering"** 这六大类任务的共 ***114个数据集***。
-  - 我们开源的[跨语种语义表征评测数据](https://github.com/netease-youdao/BCEmbedding/BCEmbedding/evaluation/c_mteb/Retrieval.py)属于`Retrieval`任务。
-  - 更详细的评测结果详见[Embedding模型指标汇总](https://github.com/netease-youdao/BCEmbedding/Docs/EvaluationSummary/embedding_eval_summary.md)。
+  - 我们开源的[跨语种语义表征评测数据](https://github.com/netease-youdao/BCEmbedding/blob/master/BCEmbedding/evaluation/c_mteb/Retrieval.py)属于`Retrieval`任务。
+  - 更详细的评测结果详见[Embedding模型指标汇总](https://github.com/netease-youdao/BCEmbedding/blob/master/Docs/EvaluationSummary/embedding_eval_summary.md)。
 
 #### 2. Reranker Models
 
@@ -357,12 +357,12 @@ The summary of multiple domains evaluations can be seen in <a href=#1-multiple-d
 ***NOTE:***
 - Our ***bce-reranker-base_v1*** outperforms other opensource reranker models.
 - ***12 datastes*** of **"Reranking"** in `["en", "zh", "en-zh", "zh-en"]` setting.
-- More evaluation details please check [Reranker Models Evaluation Summary](https://github.com/netease-youdao/BCEmbedding/Docs/EvaluationSummary/reranker_eval_summary.md).
+- More evaluation details please check [Reranker Models Evaluation Summary](https://github.com/netease-youdao/BCEmbedding/blob/master/Docs/EvaluationSummary/reranker_eval_summary.md).
 
   ***要点：***
   - ***bce-reranker-base_v1*** 优于其他开源reranker模型。
   - 评测包含 **"Reranking"** 任务的 ***12个数据集***。
-  - 更详细的评测结果详见[Reranker模型指标汇总](https://github.com/netease-youdao/BCEmbedding/Docs/EvaluationSummary/reranker_eval_summary.md)
+  - 更详细的评测结果详见[Reranker模型指标汇总](https://github.com/netease-youdao/BCEmbedding/blob/master/Docs/EvaluationSummary/reranker_eval_summary.md)
 
 ### RAG Evaluations in LlamaIndex
 
@@ -401,7 +401,7 @@ Welcome to scan the QR code below and join the WeChat group.
 
   欢迎大家扫码加入官方微信交流群。
 
-<img src="https://github.com/netease-youdao/BCEmbedding/Docs/assets/Wechat.jpg" width="20%" height="auto">
+<img src="https://github.com/netease-youdao/BCEmbedding/blob/master/Docs/assets/Wechat.jpg" width="20%" height="auto">
 
 ## ✏️ Citation
 
@@ -420,7 +420,7 @@ If you use `BCEmbedding` in your research or project, please feel free to cite a
 
 ## 🔐 License
 
-`BCEmbedding` is licensed under [Apache 2.0 License](https://github.com/netease-youdao/BCEmbedding/LICENSE)
+`BCEmbedding` is licensed under [Apache 2.0 License](https://github.com/netease-youdao/BCEmbedding/blob/master/LICENSE)
 
 ## 🔗 Related Links