updated readme

README.md

@@ -285,7 +285,7 @@ pipeline_tag: zero-shot-classification
 
 # Model Card for DeBERTa-v3-base-tasksource-nli
 
-This is [DeBERTa-v3-base](https://hf.co/microsoft/deberta-v3-base) fine-tuned with multi-task learning on 600 tasks of the [tasksource collection](https://github.com/sileod/tasksource/).
+This is [DeBERTa-v3-base](https://hf.co/microsoft/deberta-v3-base) fine-tuned with multi-task learning on 600 tasks.
 This checkpoint has strong zero-shot validation performance on many tasks (e.g. 70% on WNLI), and can be used for:
 - Zero-shot entailment-based classification pipeline (similar to bart-mnli), see [ZS].
 - Natural language inference, and many other tasks with tasksource-adapters, see [TA]
@@ -299,40 +299,3 @@ classifier = pipeline("zero-shot-classification",model="Azma-AI/deberta-base-mul
 text = "one day I will see the world"
 candidate_labels = ['travel', 'cooking', 'dancing']
 classifier(text, candidate_labels)
-```
-
-
-## Evaluation
-This model ranked 1st among all models with the microsoft/deberta-v3-base architecture according to the IBM model recycling evaluation.
-https://ibm.github.io/model-recycling/
-
-### Software and training details
-
-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.
-
-
-https://github.com/sileod/tasksource/ \
-https://github.com/sileod/tasknet/ \
-Training code: https://colab.research.google.com/drive/1iB4Oxl9_B5W3ZDzXoWJN-olUbqLBxgQS?usp=sharing
-
-# Citation
-
-More details on this [article:](https://arxiv.org/abs/2301.05948) 
-```
-@article{sileo2023tasksource,
-  title={tasksource: Structured Dataset Preprocessing Annotations for Frictionless Extreme Multi-Task Learning and Evaluation},
-  author={Sileo, Damien},
-  url= {https://arxiv.org/abs/2301.05948},
-  journal={arXiv preprint arXiv:2301.05948},
-  year={2023}
-}
-```
-
-
-# Model Card Contact
-
-damien.sileo@inria.fr
-
-
-</details>