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--- |
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pretty_name: ".NET Runtime" |
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tags: |
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- raw-json |
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- parquet |
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- faiss-index |
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- text |
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- large-scale |
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- offline-processing |
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- github |
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- code |
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- datasets |
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license: mit |
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language: |
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- en |
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size_categories: |
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- 100K<n<1M |
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task_categories: |
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- text-classification |
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- text-retrieval |
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source_datasets: [] |
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annotations_creators: |
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- machine-generated |
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- human-verified |
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--- |
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# .NET Runtime Fine-Tuning Data and Index |
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This directory contains data for fine-tuning models and building RAGs for the dotnet/runtime repository. |
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## Overview |
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- **data/**: Contains all datasets and indexes. |
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- **raw/sample/**: Sample PRs and diffs collected from GitHub. |
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- **raw_data.tar**: Archive of collected PRs and diffs from GitHub. |
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- **samples/**: Json files with processed samples suitable for dataset generation. |
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- **processed/**: Parquet files for fine-tuning (e.g., `train.parquet`, `test.parquet`). |
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- **faiss/**: Vector indexes for RAG workflows. |
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- **scripts/**: Python and nodejs scripts for crawling, processing, and indexing. |
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## Data Structure |
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``` |
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data/ |
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├── raw/ |
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| ├── sample/ |
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│ │ ├── prs/ |
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│ │ ├── diffs/ |
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│ └── raw_data.tar |
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├── processed/ |
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│ ├── train.parquet |
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│ └── test.parquet |
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└── faiss/ |
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└── index.faiss |
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└── index.pkl |
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``` |
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## Generated dataset |
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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. |
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## Scripts |
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See [scripts/README.md](scripts/README.md) for details on running the crawler, dataset generation, and RAG indexing. |
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## PyTorch Dataset Example |
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```python |
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from datasets import load_dataset |
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# Load Parquet train/test splits |
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train = load_dataset("parquet", data_files="data/processed/train.parquet", split="train") |
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test = load_dataset("parquet", data_files="data/processed/test.parquet", split="train") |
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``` |
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## RAG Vector Search Example |
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```python |
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import faiss |
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import numpy as np |
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# Load FAISS index |
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index = faiss.read_index("data/faiss/index.faiss") |
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# Example query embedding (replace with your embedding) |
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query_embedding = ... |
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# Search |
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D, I = index.search(query_embedding.reshape(1, -1), k=5) |
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print("Top 5 similar PR indices:", I[0]) |
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``` |
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## Usage Examples |
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If you use this dataset, please refer to https://github.com/kotlarmilos/phi4-finetuned |
