Update README.md

README.md

@@ -351,14 +351,15 @@ This model ranked 1st among all models with the microsoft/deberta-v3-base archit
 https://ibm.github.io/model-recycling/
 
 ### Software and training details
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+The model was trained on 600 tasks for 200k steps with a batch size of 384 and a peak learning rate of 2e-5. Training took 12 days on Nvidia A30 24GB gpu.
+This is the shared model with the MNLI classifier on top. Each task had a specific CLS embedding, which is dropped 10% of the time to facilitate model use without it. All multiple-choice model used the same classification layers. For classification tasks, models shared weights if their labels matched.
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 https://github.com/sileod/tasksource/ \
 https://github.com/sileod/tasknet/ \
 Training code: https://colab.research.google.com/drive/1iB4Oxl9_B5W3ZDzXoWJN-olUbqLBxgQS?usp=sharing
 
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-This is the shared model with the MNLI classifier on top. Each task had a specific CLS embedding, which is dropped 10% of the time to facilitate model use without it. All multiple-choice model used the same classification layers. For classification tasks, models shared weights if their labels matched.
-The number of examples per task was capped to 64k. The model was trained for 200k steps with a batch size of 384, and a peak learning rate of 2e-5. Training took 12 days on Nvidia A30 24GB gpu.
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 # Citation
 
 More details on this [article:](https://arxiv.org/abs/2301.05948)