Update README.md

README.md

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 # Model Card for DeBERTa-v3-base-tasksource-nli
 
 DeBERTa pretrained model jointly fine-tuned on 444 tasks of the tasksource collection https://github.com/sileod/tasksource/
-This is the model with the MNLI classifier on top. Its encoder was trained on many datasets including bigbench, Anthropic/hh-rlhf... alongside many NLI and classification tasks with a SequenceClassification heads while using only one shared encoder.
+You can fine-tune this model to use it for multiple-choice or any classification task (e.g. NLI) like any deberta model. 
+This model has strong zero-shot validation performance on many tasks (e.g. 70% on WNLI).
+The untuned model CLS embedding also has strong linear probing performance (90% on MNLI), due to the multitask training.
 
+This is the shared model with the MNLI classifier on top. Its encoder was trained on many datasets including bigbench, Anthropic/hh-rlhf... alongside many NLI and classification tasks with a SequenceClassification heads while using only one shared encoder.
 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 64. The model was trained for 20k steps with a batch size of 384, a peak learning rate of 2e-5.
 
-You can fine-tune this model to use it for multiple-choice or any classification task (e.g. NLI) like any debertav2 model. 
-This model has strong zero-shot validation performance on many tasks (e.g. 70% on WNLI).
-The untuned model CLS embedding also has strong linear probing performance (90% on MNLI), due to the multitask training.
-
 The list of tasks is available in tasks.md
 
 code: https://colab.research.google.com/drive/1iB4Oxl9_B5W3ZDzXoWJN-olUbqLBxgQS?usp=sharing