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/
-You can fine-tune this model to use it for multiple-choice or any classification task (e.g. NLI) like any deberta model. 
+DeBERTa pretrained model jointly fine-tuned on 444 tasks of the [tasksource collection](https://github.com/sileod/tasksource/)
+You can fine-tune this model to use it for any classification or multiple-choice task, 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.
+The number of examples per task was capped to 64k. The model was trained for 20k steps with a batch size of 384, and a peak learning rate of 2e-5.
 
 The list of tasks is available in tasks.md