Updated README.md

README.md

@@ -1,3 +1,127 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+dataset_info:
+  features:
+  - name: repo
+    dtype: string
+  - name: commit_hash
+    dtype: string
+  - name: completion_file
+    struct:
+    - name: filename
+      dtype: string
+    - name: content
+      dtype: string
+  - name: completion_lines
+    struct:
+    - name: infile
+      sequence: int32
+    - name: inproject
+      sequence: int32
+    - name: common
+      sequence: int32
+    - name: commited
+      sequence: int32
+    - name: non_informative
+      sequence: int32
+    - name: random
+      sequence: int32
+  - name: repo_snapshot
+    sequence:
+    - name: filename
+      dtype: string
+    - name: content
+      dtype: string
+  - name: completion_lines_raw
+    struct:
+    - name: commited
+      sequence: int64
+    - name: common
+      sequence: int64
+    - name: infile
+      sequence: int64
+    - name: inproject
+      sequence: int64
+    - name: non_informative
+      sequence: int64
+    - name: other
+      sequence: int64
+configs:
+- config_name: large_context
+  data_files:
+  - split: test
+    path: data/large_context/*
+- config_name: medium_context
+  data_files:
+  - split: test
+    path: data/medium_context/*
+- config_name: small_context
+  data_files:
+  - split: test
+    path: data/small_context/*
+- config_name: huge_context
+  data_files:
+  - split: test
+    path: data/huge_context/*
+---
+
+# LCA Project Level Code Completion
+## How to load the dataset
+```
+from datasets import load_dataset
+
+ds = load_dataset('JetBrains-Research/lca-codegen-huge', split='test')
+```
+## Data Point Structure
+
+* `repo` – repository name in format `{GitHub_user_name}__{repository_name}`
+* `commit_hash` – commit hash
+* `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.
+
+## 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 state of the repository before this commit is the repo snapshot.
+
+Huge dataset is defined by number of characters in `.py` files from the repository snapshot. This number larger then 768K.
+
+## Dataset Stats
+
+* Number of datapoints: 296
+* Number of repositories: 75
+* Number of commits: 252
+
+### Completion File
+* Number of lines, median: 313.5
+* Number of lines, min: 200
+* Number of lines, max: 1877
+
+### Repository Snapshot
+* `.py` files: <u>median 261</u>, from 47 to 5227
+* non `.py` files: <u>median 262</u>, from 24 to 7687
+* `.py` lines: <u>median 49811</u>
+* non `.py` lines: <u>median 60163</u>
+
+### Line Counts:
+* infile: 2608
+* inproject: 2901
+* common: 692
+* committed: 1019
+* non-informative: 1164
+* random: 1426
+* **total**: 9810
+
+## Scores
+[HF Space](https://huggingface.co/spaces/JetBrains-Research/long-code-arena)