Update README.md

README.md

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 ## Model Description
 We introduce Dragon-multiturn, a retriever specifically designed for the conversational QA scenario. It can handle conversational query which combine dialogue history with the current query. It is built on top of the [Dragon](https://huggingface.co/facebook/dragon-plus-query-encoder) retriever. The details of Dragon-multiturn can be found in [here](https://arxiv.org/abs/2401.10225). **Please note that Dragon-multiturn is a dual encoder consisting of a query encoder and a context encoder. This repository is only for the query encoder of Dragon-multiturn for getting the query embeddings, and you also need the context encoder to get context embeddings, which can be found [here](https://huggingface.co/nvidia/dragon-multiturn-context-encoder). Both query encoder and context encoder share the same tokenizer.**
 
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 ## Other Resources
 [Llama3-ChatQA-1.5-8B](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-8B)   [Llama3-ChatQA-1.5-70B](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-70B)   [Evaluation Data](https://huggingface.co/datasets/nvidia/ChatRAG-Bench)   [Training Data](https://huggingface.co/datasets/nvidia/ChatQA-Training-Data)   [Website](https://chatqa-project.github.io/)   [Paper](https://arxiv.org/abs/2401.10225)
 
@@ -125,6 +126,10 @@ similarities = query_emb.matmul(ctx_emb.transpose(0, 1)) # (1, num_ctx)
 ranked_results = torch.argsort(similarities, dim=-1, descending=True) # (1, num_ctx)
 ```
 
+## Evaluations on Multi-Turn QA Retrieval Benchmark
+**(UPDATE!!)** We evaluate multi-turn QA retrieval on five datasets: Doc2Dial, QuAC, QReCC, TopiOCQA, and INSCIT, which can be found in the [ChatRAG Bench](https://huggingface.co/datasets/nvidia/ChatRAG-Bench). The evaluation scripts can be found [here](https://huggingface.co/nvidia/dragon-multiturn-query-encoder/tree/main/evaluation).
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 ## License
 Dragon-multiturn is built on top of [Dragon](https://arxiv.org/abs/2302.07452). We refer users to the original license of the Dragon model. Dragon-multiturn is also subject to the [Terms of Use](https://openai.com/policies/terms-of-use).