File size: 3,473 Bytes
b148970 53717ea b148970 53717ea b148970 2d9be3b b148970 2d9be3b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | ---
dataset_info:
features:
- name: repo
dtype: string
- name: docfile_name
dtype: string
- name: doc_type
dtype: string
- name: intent
dtype: string
- name: license
dtype: string
- name: path_to_docfile
dtype: string
- name: relevant_code_files
sequence: string
- name: relevant_code_dir
dtype: string
- name: target_text
dtype: string
- name: relevant_code_context
dtype: string
splits:
- name: test
num_bytes: 227163668
num_examples: 216
download_size: 30374553
dataset_size: 227163668
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# BenchName (Module summarization)
This is the benchmark for Module summarization task as part of the
[BenchName benchmark](https://huggingface.co/collections/icmlbenchname/icml-25-benchname-679b57e38a389f054cdf33e9).
The current version includes 216 manually curated text files describing different documentation of open-source permissive Python projects.
The model is required to generate such description, given the relevant context code and the intent behind the documentation.
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):
```
from datasets import load_dataset
dataset = load_dataset("JetBrains-Research/lca-module-summarization")
```
## Datapoint Structure
Each example has the following fields:
| **Field** | **Description** |
|:---------------------------:|:----------------------------------------:|
| `repo` | Name of the repository |
| `target_text` | Text of the target documentation file |
| `docfile_name` | Name of the file with target documentation |
| `intent` | One sentence description of what is expected in the documentation |
| `license` | License of the target repository |
| `relevant_code_files` | Paths to relevant code files (files that are mentioned in target documentation) |
| `relevant_code_dir` | Paths to relevant code directories (directories that are mentioned in target documentation) |
| `path_to_docfile` | Path to file with documentation (path to the documentation file in source repository) |
| `relevant_code_context` | Relevant code context collected from relevant code files and directories |
## Metric
To compare the predicted documentation and the ground truth documentation, we introduce the new metric based on LLM as an assessor. Our approach involves feeding the LLM with relevant code and two versions of documentation: the ground truth and the model-generated text. The LLM evaluates which documentation better explains and fits the code. To mitigate variance and potential ordering effects in model responses, we calculate the probability that the generated documentation is superior by averaging the results of two queries with the different order.
For more details about metric implementation, please refer to [our GitHub repository](https://anonymous.4open.science/r/icml-benchname-2025/).
|