Update README.md

README.md

@@ -229,15 +229,7 @@ library_name: transformers
 
 DeBERTa-v3-base fine-tuned with multi-task learning on 560 tasks of the [tasksource collection](https://github.com/sileod/tasksource/)
 This checkpoint has strong zero-shot validation performance on many tasks (e.g. 70% on WNLI), and can be used for zero-shot NLI pipeline (similar to bart-mnli but better).
-You can also further fine-tune this model to use it for any classification or multiple-choice task.
-
-This is the shared model with the MNLI classifier on top. Its encoder was trained on many datasets including bigbench, Anthropic rlhf, anli... alongside many NLI and classification tasks with 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 64k. The model was trained for 45k steps with a batch size of 384, and a peak learning rate of 2e-5.
-
-The list of tasks is available in model config.
-
-tasksource training code: https://colab.research.google.com/drive/1iB4Oxl9_B5W3ZDzXoWJN-olUbqLBxgQS?usp=sharing
+You can also load other tasks (see next paragraph) or further fine-tune the encoder for new classification, token or multiple-choice.
 
 # Tasksource-adapters: 1 line access to 500 tasks 
 
@@ -265,11 +257,17 @@ Results:
 
 For more information, see: [Model Recycling](https://ibm.github.io/model-recycling/)
 
-### Software
+### Software and training details
 https://github.com/sileod/tasksource/ \
 https://github.com/sileod/tasknet/ \
 Training took 7 days on RTX6000 24GB gpu.
+Training code: https://colab.research.google.com/drive/1iB4Oxl9_B5W3ZDzXoWJN-olUbqLBxgQS?usp=sharing
+
+This is the shared model with the MNLI classifier on top. Its encoder was trained on many datasets including bigbench, Anthropic rlhf, anli... alongside many NLI and classification tasks with 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 64k. The model was trained for 45k steps with a batch size of 384, and a peak learning rate of 2e-5.
 
+The list of tasks is available in model config.
 
 # Citation