Update README.md

README.md

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 TAPEX Zero is a novel approach for instruction tuning that leverages synthetic tasks to improve the performance of language models on unseen tasks. The proposed approach utilizes symbolic tasks for instruction tuning, which has demonstrated its effectiveness in enhancing the performance of language models on unseen tasks, as exemplified by the case study on zero-shot table reasoning.
 
-## Intended uses
-
-TAPEX Zero can be used for fine-tuning language models on tasks with instructions, especially when there is limited labeled data available. It can also be used for zero-shot learning, where the model can generalize to unseen tasks without any labeled data.
-
 ## Training data
 
 The model is trained on a variety of datasets that include symbolic tasks such as SQL queries. The training data also includes generic datasets to ensure that the model maintains its original performance on those datasets.
@@ -22,4 +18,4 @@ As with any machine learning model, TAPEX Zero may have limitations and biases b
 
 ## Code base
 
-The code used to train TAPEX Zero is available at https://github.com/openai/symbolic-tasks-instruction-tuning
+The code used to train TAPEX Zero is available at https://github.com/sail-sg/symbolic-instruction-tuning