Update README.md

README.md

@@ -66,10 +66,15 @@ configs:
 ---
 
 
-# 🏟️ Long Code Arena (Project-Level Code Completion)
-This is the benchmark for Project-Level Code Completion task as part of [🏟️ Long Code Arena benchmark](https://huggingface.co/spaces/JetBrains-Research/long-code-arena).
+# 🏟️ Long Code Arena (Project-level code completion)
+This is the benchmark for Project-level code completion task as part of the [🏟️ Long Code Arena benchmark](https://huggingface.co/spaces/JetBrains-Research/long-code-arena).
+Each datapoint contains the file for completion, a list of lines to complete with their categories (see the categorization below), and a repository snapshot that can be used to build the context.
+All the repositories are published under permissive licenses (MIT, Apache-2.0, BSD-3-Clause, and BSD-2-Clause). The datapoints can be removed upon request.
+
+## How-to
+
+Load the data via [load_dataset](https://huggingface.co/docs/datasets/v2.14.3/en/package_reference/loading_methods#datasets.load_dataset):
 
-## How to load the dataset
 ```
 from datasets import load_dataset
 
@@ -82,27 +87,29 @@ config_names = [
 
 ds = load_dataset('JetBrains-Research/lca-code-completion', config_name, split='test')
 ```
-## Data Point Structure
+## Dataset Structure
+
+Datapoints in the dataset have the following structure:
 
-* `repo` – repository name in format `{GitHub_user_name}__{repository_name}`
-* `commit_hash` – commit hash
+* `repo` – repository name in the format `{GitHub_user_name}__{repository_name}`
+* `commit_hash` – commit hash of the repository
 * `completion_file` – dictionary with the completion file content in the following format:
-* `filename` – filepath to the completion file
-* `content` – content of the completion file
-* `completion_lines` – dictionary where keys are classes of lines and values are a list of integers (numbers of lines to complete). The classes are:
-  * `committed` – line contains at least one function or class that was declared in the committed files from `commit_hash`
-  * `inproject` – line contains at least one function or class that was declared in the project (excluding previous)
-  * `infile` – line contains at least one function or class that was declared in the completion file (excluding previous)
-  * `common` – line contains at least one function or class that was classified to be common, e.g., `main`, `get`, etc (excluding previous)
-  * `non_informative` – line that was classified to be non-informative, e.g. too short, contains comments, etc
-  * `random` – randomly sampled from the rest of the lines
-* `repo_snapshot` – dictionary with a snapshot of the repository before the commit. Has the same structure as `completion_file`, but filenames and contents are orginized as lists.
-* `completion_lines_raw` – the same as `completion_lines`, but before sampling.
+  * `filename` – path to the completion file
+  * `content` – content of the completion file
+* `completion_lines` – dictionary where the keys are categories of lines and values are a list of integers (numbers of lines to complete). The categories are:
+  * `committed` – line contains at least one function or class from the files that were added on the completion file commit
+  * `inproject` – line contains at least one function or class from the repository snapshot at the moment of completion
+  * `infile` – line contains at least one function or class from the completion file
+  * `common` – line contains at least one function or class with common names, e.g., `main`, `get`, etc.
+  * `non_informative` – line that was classified to be non-informative, e.g., too short, contains comments, etc.
+  * `random` – other lines.
+* `repo_snapshot` – dictionary with a snapshot of the repository before the commit. It has the same structure as `completion_file`, but filenames and contents are orginized as lists.
+* `completion_lines_raw` – same as `completion_lines`, but before sampling
 
 ## How we collected the data
 
 To collect the data, we cloned repositories from GitHub where the main language is Python.
-The completion file for each data point is a `.py` file that was added to the repository in a commit.
+The completion file for each datapoint is a `.py` file that was added to the repository in a commit.
 The state of the repository before this commit is the repo snapshot.
 
 The dataset configurations are based on the number of characters in `.py` files from the repository snapshot:
@@ -159,4 +166,4 @@ The dataset configurations are based on the number of characters in `.py` files
 
 
 ## Scores
-[HF Space](https://huggingface.co/spaces/JetBrains-Research/long-code-arena)
+You can find the results of running various models on this dataset in our [leaderboard](https://huggingface.co/spaces/JetBrains-Research/long-code-arena).