Update README.md

README.md

@@ -64,7 +64,10 @@ configs:
 ---
 
 ## ConvRAG Bench
-ConvRAG Bench is a benchmark for evaluating a model's conversational QA capability over documents or retrieved context. ConvRAG Bench are built on and derived from 10 existing datasets: Doc2Dial, QuAC, QReCC, TopioCQA, INSCIT, CoQA, HybriDialogue, DoQA, SQA, ConvFinQA. ConvRAG Bench covers a wide range of documents and question types, which require models to generate responses from long context, comprehend and reason over tables, conduct arithmetic calculations, and indicate when questions cannot be found within the context.
+ConvRAG Bench is a benchmark for evaluating a model's conversational QA capability over documents or retrieved context. ConvRAG Bench are built on and derived from 10 existing datasets: Doc2Dial, QuAC, QReCC, TopioCQA, INSCIT, CoQA, HybriDialogue, DoQA, SQA, ConvFinQA. ConvRAG Bench covers a wide range of documents and question types, which require models to generate responses from long context, comprehend and reason over tables, conduct arithmetic calculations, and indicate when questions cannot be found within the context. The details of this benchmark are described in [here](https://arxiv.org/abs/2401.10225).
+
+## Other Resources
+[ChatQA-1.5-8B](https://huggingface.co/nvidia/ChatQA-1.5-8B)   [ChatQA-1.5-70B](https://huggingface.co/nvidia/ChatQA-1.5-70B)   [Training Data](https://huggingface.co/datasets/nvidia/ChatQA-Training-Data)   [Retriever](https://huggingface.co/nvidia/dragon-multiturn-query-encoder)
 
 ## Benchmark Results
 
@@ -184,4 +187,4 @@ We would like to give credits to all the works constructing the datasets we use
   journal={Transactions of the Association for Computational Linguistics},
   year={2023}
 }
-</pre>
+</pre>