Update README.md

README.md

@@ -22,18 +22,6 @@ This dataset contains programs and proofs in [F* proof-oriented programming lang
 The data, proposed in [Towards Neural Synthesis for SMT-Assisted Proof-Oriented Programming](https://arxiv.org/pdf/2405.01787).
 is an archive of source code, build artifacts, and metadata assembled from eight different F⋆-based open source projects on GitHub.
 
-
-## Data Source
-The raw data in this project are collected from eight open-source F* reposiroties in GitHib 
-1. [FStar](https://github.com/FStarLang/FStar): The F⋆ compiler itself, including its standard libraries and examples.
-2. [Karamel](https://github.com/FStarLang/karamel): A transpiler from a subset of F⋆ called Low* to C, including libraries to work with a model of C types and control structures, e.g., for- and while-loops.
-3. [EverParse](https://github.com/project-everest/everparse): A parser generator for binary formats, used in various large scale systems, e.g., the Windows kernel.
-4. [HACL*](https://github.com/hacl-star/hacl-star): A library of verified cryptographic algorithms, including ValeCrypt, a library of verified assembly code, as well as EverCrypt, a cryptographic provider, including code deployed in Linux, Firefox, and Python.
-5. [Merkle-tree](https://github.com/hacl-star/merkle-tree): A verified, incremental Merkle tree, designed for use in Azure CCF, a confidential computing system.
-6. [Steel](https://github.com/FStarLang/steel): A concurrent separation logic library, with proofs of data structures and concurrency primitives.
-7. [miTLS-F*](https://github.com/project-everest/mitls-fstar): A partially verified reference implementation of the TLS protocol.
-8. [EverQuic-Crypto](https://github.com/project-everest/everquic-crypto): A verified implementation of header and packet protection for the QUIC protocol.
-
 ## Primary-Objective
 This dataset's primary objective is to train and evaluate Proof-oriented Programming with AI (PoPAI, in short). Given a specification of a program and proof is F*, 
 the objective of a AI model is to synthesize the implemantation (see [below](#usage) for details about the usage of this dataset, including the input and output). 
@@ -73,10 +61,10 @@ Each of the examples in this dataset are organized as dictionaries with the foll
 
 # Usage
 To use this dataset with [`datasets`](https://pypi.org/project/datasets/), 
-```py
+```python
 from datasets import load_dataset
 
-data = load_data("microsoft/FStarDataSet")
+data = load_dataset("microsoft/FStarDataSet")
 train_data = data["train"]
 eval_data = data["validation"]
 test_data = data["test"]
@@ -86,8 +74,15 @@ cross_project_test = test_data.filter(lambda x: x["isa_cross_project_example"] =
 ```
 
 ## Input
+The primary input for generating F* definition is **`source_type`**. 
+All other information in an example may be used directly or to derive an input except 
+**`source_definition`**, **`ideal_premises`**, and **`completed_definiton`**.
+
 
 ## Output
+The primary output is **`source_definition`**, which is the ground truth definition, that can be evaluated with the [proof checker](#evaluation-on-this-dataset). 
+The **`completed_definiton`** may be used as ground truth when a model is used as a text completion setting (though the evaluator does not support evaluation in this setting). 
+In addition, **`ideal_premises`** may be used for evaluating premise selection models. 
 
 # Evaluation on this dataset
 Generated F* definitions should be evaluated the proof checker tool from [https://github.com/FStarLang/fstar_dataset/releases/tag/eval-v1.0](https://github.com/FStarLang/fstar_dataset/releases/tag/eval-v1.0). 
@@ -97,4 +92,27 @@ Download the source code and the `helpers.zip` file from the release.
 The attached binaries in the evaluator (i.e., `fstar.exe` and `z3`) are built on 
 **`Ubuntu 20.04.6 LTS (GNU/Linux 5.4.0-189-generic x86_64)`**  **`gcc (Ubuntu 9.4.0-1ubuntu1~20.04.2)`**,  **`OCaml 4.12.0`**. 
 If any of the binaries do not work properly, build F* from [this commit (f3b4db2ebce90020acbbbe1b4ea0d05d3e69ad6c)](https://github.com/FStarLang/FStar/commit/f3b4db2ebce90020acbbbe1b4ea0d05d3e69ad6c) 
-from the [F* repository](https://github.com/FStarLang/FStar), using their [installation guide](https://github.com/FStarLang/FStar/blob/master/INSTALL.md).
+from the [F* repository](https://github.com/FStarLang/FStar), using their [installation guide](https://github.com/FStarLang/FStar/blob/master/INSTALL.md).
+
+
+# Data Source
+The raw data in this project are collected from eight open-source F* reposiroties in GitHib 
+1. [FStar](https://github.com/FStarLang/FStar): The F⋆ compiler itself, including its standard libraries and examples.
+2. [Karamel](https://github.com/FStarLang/karamel): A transpiler from a subset of F⋆ called Low* to C, including libraries to work with a model of C types and control structures, e.g., for- and while-loops.
+3. [EverParse](https://github.com/project-everest/everparse): A parser generator for binary formats, used in various large scale systems, e.g., the Windows kernel.
+4. [HACL*](https://github.com/hacl-star/hacl-star): A library of verified cryptographic algorithms, including ValeCrypt, a library of verified assembly code, as well as EverCrypt, a cryptographic provider, including code deployed in Linux, Firefox, and Python.
+5. [Merkle-tree](https://github.com/hacl-star/merkle-tree): A verified, incremental Merkle tree, designed for use in Azure CCF, a confidential computing system.
+6. [Steel](https://github.com/FStarLang/steel): A concurrent separation logic library, with proofs of data structures and concurrency primitives.
+7. [miTLS-F*](https://github.com/project-everest/mitls-fstar): A partially verified reference implementation of the TLS protocol.
+8. [EverQuic-Crypto](https://github.com/project-everest/everquic-crypto): A verified implementation of header and packet protection for the QUIC protocol.
+
+# Limitations
+**TDB**
+
+# Citation
+
+# Contributors
+1. [Saikat Chakraborty](https://huggingface.co/saikatc)
+2. [Gabriel Ebner]()
+3. [Nikhil Swamy]()
+4. [Siddharth Bhat]()