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Hey HF. I just released a new reward modelling dataset: 
Hey HF. I just released a new reward modelling dataset: 
resource
https://huggingface.co/datasets/Avelina/UltraSteer-v0
dataset
Avelina/UltraSteer-v0
https://huggingface.co/datasets/Avelina/UltraSteer-v0
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UltraSteer-V0 is a massive collection of single- and multi-turn dialogue with fine-grained reward labels produced by Nvidia's 
UltraSteer-V0 is a massive collection of single- and multi-turn dialogue with fine-grained reward labels produced by Nvidia's 
resource
https://huggingface.co/nvidia/Llama2-13B-SteerLM-RM
model
nvidia/Llama2-13B-SteerLM-RM
https://huggingface.co/nvidia/Llama2-13B-SteerLM-RM
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 reward model. We have a total of 2.3M labelled sequences taken from high quality datasets with a total of 2.8M labelled turns each containing 9 attributes produced as is from the reward model.
 reward model. We have a total of 2.3M labelled sequences taken from high quality datasets with a total of 2.8M labelled turns each containing 9 attributes produced as is from the reward model.
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This is still very much an early version of the dataset (but it's fully usable!) and an updated version will be on the way with a full paper.
This is still very much an early version of the dataset (but it's fully usable!) and an updated version will be on the way with a full paper.
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I would really appreciate if people could take a look at the dataset and suggest any improvements (e.g. more data sources, different cleaning approaches, different label schema, etc) in the community section.
I would really appreciate if people could take a look at the dataset and suggest any improvements (e.g. more data sources, different cleaning approaches, different label schema, etc) in the community section.
Hey HF. I just released a new reward modelling dataset: https://huggingface.co/datasets/Avelina/UltraSteer-v0

UltraSteer-V0 is a massive collection of single- and multi-turn dialogue with fine-grained reward labels produced by Nvidia's https://huggingface.co/nvidia/Llama2-13B-SteerLM-RM reward model. We have a total of 2.3M labelled sequences taken from high quality datasets with a total of 2.8M labelled turns each containing 9 attributes produced as is from the reward model.

This is still very much an early version of the dataset (but it's fully usable!) and an updated version will be on the way with a full paper.

I would really appreciate if people could take a look at the dataset and suggest any improvements (e.g. more data sources, different cleaning approaches, different label schema, etc) in the community section.
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mmoy
/avatars/dcd27ab92a41245d7b96b84ed63ce3a1.svg
Mark Moyou
mmoy
user
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/2p_IJctIFETtjciHxNOpN.jpeg
Avelina Hadji-Kyriacou
Avelina
user