README.md

metadata

pretty_name: .NET Runtime
tags:
  - raw-json
  - parquet
  - faiss-index
  - text
  - large-scale
  - offline-processing
  - github
  - code
  - datasets
license: mit
language:
  - en
size_categories:
  - 100K<n<1M
task_categories:
  - text-classification
  - text-retrieval
source_datasets: []
annotations_creators:
  - machine-generated
  - human-verified

.NET Runtime Fine-Tuning Data and Index

This directory contains data for fine-tuning models and building RAGs for the dotnet/runtime repository.

Overview

• data/: Contains all datasets and indexes.
  • raw/sample/: Sample PRs and diffs collected from GitHub.
  • raw_data.tar: Archive of collected PRs and diffs from GitHub.
  • samples/: Json files with processed samples suitable for dataset generation.
  • processed/: Parquet files for fine-tuning (e.g., train.parquet, test.parquet).
  • faiss/: Vector indexes for RAG workflows.
• scripts/: Python and nodejs scripts for crawling, processing, and indexing.

Data Structure

data/
├── raw/
|   ├── sample/
│   │   ├── prs/
│   │   ├── diffs/
│   └── raw_data.tar
├── processed/
│   ├── train.parquet
│   └── test.parquet
└── faiss/
    └── index.faiss
    └── index.pkl

Generated dataset

PR is considered as a timeline with events. Input is PR metadata (title, description, label) and commit n-1, with all events between n-1 and n. Completion is n. It is possible to filter by time, label, authors, etc.

Scripts

See scripts/README.md for details on running the crawler, dataset generation, and RAG indexing.

PyTorch Dataset Example

from datasets import load_dataset

# Load Parquet train/test splits
train = load_dataset("parquet", data_files="data/processed/train.parquet", split="train")
test = load_dataset("parquet", data_files="data/processed/test.parquet", split="train")

RAG Vector Search Example

import faiss
import numpy as np

# Load FAISS index
index = faiss.read_index("data/faiss/index.faiss")

# Example query embedding (replace with your embedding)
query_embedding = ...

# Search
D, I = index.search(query_embedding.reshape(1, -1), k=5)
print("Top 5 similar PR indices:", I[0])