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---
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](scripts/README.md) for details on running the crawler, dataset generation, and RAG indexing.
## PyTorch Dataset Example
```python
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
```python
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])
``` |