Update README.md

README.md

@@ -23,60 +23,5 @@ Commonsene Object Affordance Task [COAT]
   <a href="https://openreview.net/pdf?id=xYkdmEGhIM">OpenReview</a> | <a href="https://drive.google.com/drive/u/4/folders/1reH0JHhPM_tFzDMcAaJF0PycFMixfIbo">Datasets</a>
 </p>
 
-<p align="center">
-<img src="https://huggingface.co/datasets/Ayush8120/commonsense-embodied-affordance/blob/main/utility-intro(1).png" alt="Paper Summary Flowchart">
-<em>A 3 level framework adumbrating human commonsense style reasoning for estimating object affordance for various tasks</em>
-</p>
-
-### Experimental Setup:
-- Task List: [tasks](https://huggingface.co/datasets/Ayush8120/commonsense-embodied-affordance/blob/main/tasks.json)
-- Object List: [objects](https://huggingface.co/datasets/Ayush8120/commonsense-embodied-affordance/blob/main/concepts.json)
-- Utility List[^1]: [utilities](https://huggingface.co/datasets/Ayush8120/commonsense-embodied-affordance/blob/main/concepts.json)
-- Variables Used:
-  ```temperature```, ```mass```, ```material```, ```already-in-use```, ```condition```
-  
-### Utility Level Pruning:
-This gives us ```Utility```to``Object`` mappings also called ```utility objects```
-- GT Object-Utility Mappings : [utility-mappings](https://huggingface.co/datasets/Ayush8120/commonsense-embodied-affordance/blob/main/objects.json)
-
-### Task-u(Utility Level):
-Here we evaluate models on their ability to prune out appropriate objects on the basis of Utility. 
-- GT (Utility)-(Object) Mappings: [utility-objects](https://huggingface.co/datasets/Ayush8120/commonsense-embodied-affordance/blob/main/objects.json)
-- Task-u Dataset: [4 Variations](https://drive.google.com/drive/folders/1JJSIicKGp0a7ThsenKl0XWKsTtPL_b5z?usp=sharing)
-  
-### Task-0(Context Level):
-Here we evaluate models on their ability to prune out appropriate objects on the basis of Context. This gives us ```(Task,Utility)```to``Object`` mappings also called ```context objects```
-- GT (Task-Utility)-(Object) Mappings: [context-objects](https://huggingface.co/datasets/Ayush8120/commonsense-embodied-affordance/blob/main/oracle.json)
-- Task-0 Dataset: [4 Variations](https://drive.google.com/drive/folders/1reH0JHhPM_tFzDMcAaJF0PycFMixfIbo?usp=sharing)
-
-### Task-1(Physical State Level):
-Here we evaluate models on their ability to prune out the ```ideal``` configuration when presented with a number of ```context object``` configurations. (Something that is pretty obvious to humans)
-- All Possible Common Configurations: [possible configurations](https://huggingface.co/datasets/Ayush8120/commonsense-embodied-affordance/blob/main/task-1/possible_configurations_v1.json)
-- Ideal Configurations: [ideal configurations](https://huggingface.co/datasets/Ayush8120/commonsense-embodied-affordance/blob/main/task-1/pouch_config_oracle.json)
-- Commonsense Common Occurence Variables: [common variables values](https://huggingface.co/datasets/Ayush8120/commonsense-embodied-affordance/blob/main/task-1/common_var_responses.json)
-- Task-1 Dataset: [12 Variations](https://drive.google.com/drive/folders/1reH0JHhPM_tFzDMcAaJF0PycFMixfIbo?usp=sharing)
-
-### Task-2(Physical State Level):
-Here we evaluate models on their ability to prune out the most appropriate```sub-optimal``` configuration when presented with a number of sub-optimal configurations of ```context objects```. (Something that is pretty obvious to humans)
-- Suboptimal Configurations: [suboptimal configurations](https://huggingface.co/datasets/Ayush8120/commonsense-embodied-affordance/blob/main/task-2/pouch_suboptimal.json)
-- Human Preference Material Order: [material preference](https://huggingface.co/datasets/Ayush8120/commonsense-embodied-affordance/blob/main/task-2/material_preference.json)
-- Task-2 Dataset: [14 Variations](https://drive.google.com/drive/folders/1reH0JHhPM_tFzDMcAaJF0PycFMixfIbo?usp=sharing)
----------------------------------------------------------------------------------------------------------------
-
-### Finetuning Datasets 
-
-Please refer to [Appendix F.1](https://openreview.net/pdf?id=xYkdmEGhIM) for dataset details
-
-- Finetuning Dataset for Object Level Selection : [Google Drive Link](https://drive.google.com/drive/folders/1GtrGQxTTtYEczYK1ytB71Y2HGxM1TEu5?usp=drive_link)
-- Finetuning Dataset for Physical State Level Selection : [Google Drive Link](https://drive.google.com/drive/folders/1FiZc8u_G8wUrN4NroZmIgmcTe0jor72T?usp=drive_link)
-
-### Full Pipeline Evaluation Datasets
-
-Please refer to [Appendix F.2](https://openreview.net/pdf?id=xYkdmEGhIM) for dataset deatails
-
-- Ideal Object Choice Datasets : [Google Drive Link](https://drive.google.com/drive/folders/1SMM2TU1BKH32oKtfmW0gS3QfyUA68IZ0?usp=drive_link)
-- Moderate Object Choice Datasets : [Google Drive Link](https://drive.google.com/drive/folders/1SlZQBp4Iao3VHnmOFZMKfzn_LWOctnVE?usp=drive_link)
-
-
-[^1]: For the purpose of datasets, we've used `concept and utility` interchangeably.
+Please refer [here](https://github.com/Ayush8120/COAT) for the documentation
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