Update README.md

README.md

@@ -29,7 +29,7 @@ Our model is capable of:
 
 # News
 
-- 2024-08-17: 👊 We open-sourced [cleaned version of training framework](https://github.com/RhapsodyAILab/MiniCPM-V-Embedding-v0-Train) for MiniCPM-Visual-Embedding, which supports **deepspeed zero stage 1,2** and **large batchsize** like `4096` for full-parameter training to turn VLMs into dense retrievers. We also developed methods to filter training datasets and generating queries using unlablled datasets. We supports **multi-nodes, multi-GPUs** high-efficiency **evaluation** on large retrieval datasets. With such efforts, we support up to `20B` VLM contrastive learning with `4096` batch size. We have tested that one can train a VLM dense retriever with only **1 GPU, but with batch size of `4096`**. 
+- 2024-08-17: 👊 We open-sourced [cleaned version of training codebase](https://github.com/RhapsodyAILab/MiniCPM-V-Embedding-v0-Train) for MiniCPM-Visual-Embedding, which supports **deepspeed zero stage 1,2** and **large batchsize** like `4096` for full-parameter training to turn VLMs into dense retrievers. We also developed methods to filter training datasets and generating queries using unlablled datasets. We supports **multi-nodes, multi-GPUs** high-efficiency **evaluation** on large retrieval datasets. With such efforts, we support up to `20B` VLM contrastive learning with `4096` batch size. We have tested that one can train a VLM dense retriever with only **1 GPU, but with batch size of `4096`**. 
 
 - 2024-07-14: 🤗 We released **online huggingface demo**! Try our [online demo](https://huggingface.co/spaces/bokesyo/minicpm-visual-embeeding-v0-demo)!