Datasets:
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README.md
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- 1M<n<10M
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---
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# C StackOverflow Vector Dataset
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##
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This
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Each Hugging Face dataset repository contains
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It is
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See the Stack2Graph repository for more details:
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[https://github.com/tha-atlas/Stack2Graph](https://github.com/tha-atlas/Stack2Graph)
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##
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```text
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dataset_manifest.json
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```
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- `dataset_manifest.json`: language-scoped manifest
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-
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It contains the structured records needed to rebuild the Stack2Graph Qdrant collection for that language.
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- this repository contains only the C shard
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- the archive may contain both metadata-only and retrieval-ready vector rows depending on the export mode
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##
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2. Load all Parquet shards for this repository into your vector indexing pipeline.
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3. Rebuild or restore the Qdrant collection `stackoverflow_c_vector`.
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4. Preserve attribution and license metadata during downstream export.
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`python -m experiment.load_hf_datasets_into_services --skip-kg` to download dataset artifacts
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and prepare the vector database service state automatically.
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Typical workflow:
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1. Clone and configure
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1. Start required local services:
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```bash
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docker compose up -d
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```
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```bash
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python -m experiment.
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```
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##
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- Embeddings and sparse representations depend on the configured export pipeline and model versions.
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- As with community-generated data, content may include noise, bias, and temporal drift.
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##
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##
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This dataset
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##
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If you use this dataset, cite the Stack2Graph
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- Stack2Graph: A Structured Knowledge Representation of Stack Overflow Data for Retrieval-based Question Answering
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---
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# C StackOverflow Vector Dataset
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## Summary
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This repository contains the C shard of the Stack2Graph vector retrieval dataset.
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Each Hugging Face dataset repository contains one programming-language shard and is intended to restore or rebuild the Qdrant collection `stackoverflow_c_vector`.
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The dataset is the vector counterpart to the Stack2Graph RDF knowledge graph. It is designed for hybrid retrieval, graph entry-point finding, and retrieval-augmented generation experiments over Stack Overflow content.
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Stack2Graph source:
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[https://github.com/tha-atlas/Stack2Graph](https://github.com/tha-atlas/Stack2Graph)
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## Repository Layout
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```text
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README.md
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dataset_manifest.json
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qdrant_snapshots/
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collections_manifest.json
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stackoverflow_c_vector.tar.xz
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stackoverflow_c_vector.snapshot.xz.part_000
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...
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question_metadata/
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r0_00000.parquet
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chunk_records/
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r0_00000.parquet
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question_records/
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r0_00000.parquet
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```
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- `dataset_manifest.json`: language-scoped manifest listing the files in this shard.
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- `qdrant_snapshots/collections_manifest.json`: language-scoped Qdrant snapshot manifest.
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- `qdrant_snapshots/stackoverflow_c_vector.*`: optional Qdrant restore artifact files for `stackoverflow_c_vector`.
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- `question_metadata/*.parquet`: question-level metadata used by parent-child chunk retrieval.
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- `chunk_records/*.parquet`: chunk-level vector rows when parent-child indexing is enabled.
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- `question_records/*.parquet`: question-level vector rows when non-chunked export is used.
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The Hugging Face upload is one repository per language. During upload, the local leading language directory is removed, so local files such as `c/chunk_records/r0_00000.parquet` appear in this repository as `chunk_records/r0_00000.parquet`.
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## Data Model
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Rows are derived from Stack Overflow questions tagged for C.
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The current Stack2Graph vector pipeline uses:
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- dense vectors from `Qwen/Qwen3-Embedding-8B`
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- 4096-dimensional dense embeddings with last-token pooling
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- instruction-aware query embedding for retrieval
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- sparse lexical vectors from `BAAI/bge-m3`
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- unified question text containing title, tags, cleaned problem text, and code
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- optional parent-child indexing where chunk hits collapse back to parent question IDs
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When Qdrant snapshots are included, they are the fastest restore path. The Parquet files remain the portable fallback for rebuilding the collection.
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## Coverage
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This repository contains only the C shard. A Stack Overflow question can appear in more than one language shard when it has multiple programming-language tags.
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The dataset is not a complete Stack Overflow mirror. Full question and answer graph context lives in the corresponding Stack2Graph knowledge graph artifacts; the vector dataset stores retrieval rows, vectors, sparse weights, and routing metadata.
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## Recommended Use
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Use this dataset for:
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- semantic and hybrid retrieval
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- Qdrant restore or ingestion tests
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- Stack2Graph RAG experiments
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- graph entry-point retrieval before QLever graph expansion
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- vector database benchmarking and diagnostics
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This dataset is not intended as a standalone supervised training dataset.
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## Restore With Stack2Graph
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You do not need to regenerate embeddings to use this dataset.
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Typical workflow:
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1. Clone Stack2Graph and configure `.env` with service paths and HF token.
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2. Start local services:
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```bash
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docker compose up -d
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```
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3. Download and restore vector artifacts:
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```bash
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python -m experiment.sources.hf --skip-kg
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```
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The loader restores Qdrant snapshots when present and falls back to Parquet ingestion when snapshots are unavailable.
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## Manual Use
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For manual use, inspect `dataset_manifest.json`, then either:
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- restore the Qdrant snapshot artifacts under `qdrant_snapshots/`, or
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- ingest the listed Parquet shards into a compatible vector database.
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The target Qdrant collection name is:
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```text
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stackoverflow_c_vector
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```
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## Quality Notes
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- Embedding quality depends on the configured Stack2Graph export pipeline and model versions.
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- Sparse text can be generated from the same unified text as dense embeddings or from a lexical variant, depending on export configuration.
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- Community-generated Stack Overflow content may contain errors, outdated information, bias, or incomplete answers.
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- Rebuilding from Parquet may produce operational differences if Qdrant collection settings differ from the original export.
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## License And Attribution
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This dataset is derived from Stack Overflow content and is distributed under `CC-BY-SA-4.0`.
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Preserve required attribution and license notices when redistributing derived artifacts.
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## Citation
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If you use this dataset, cite the Stack2Graph project and preserve Stack Overflow attribution requirements:
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- Stack2Graph: A Structured Knowledge Representation of Stack Overflow Data for Retrieval-based Question Answering
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| 50 |
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| 51 |
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|
| 52 |
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| 53 |
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| 54 |
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| 55 |
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| 56 |
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| 57 |
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| 58 |
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| 59 |
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| 60 |
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| 61 |
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| 63 |
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|
| 64 |
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| 65 |
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| 66 |
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| 67 |
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| 68 |
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| 69 |
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| 70 |
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| 71 |
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| 72 |
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| 73 |
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| 74 |
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| 75 |
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| 76 |
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| 77 |
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| 78 |
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| 79 |
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| 80 |
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| 81 |
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| 82 |
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| 83 |
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|
| 84 |
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| 85 |
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| 86 |
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| 87 |
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| 88 |
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| 89 |
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| 90 |
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| 91 |
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| 92 |
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| 93 |
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| 94 |
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| 95 |
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| 96 |
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| 97 |
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| 98 |
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| 99 |
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| 100 |
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| 101 |
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| 102 |
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| 103 |
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| 104 |
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|
| 105 |
}
|
| 106 |
],
|
| 107 |
"language": "c",
|
| 108 |
"source": "vector_dataset_language_folder",
|
| 109 |
+
"total_rows": 940143
|
| 110 |
}
|
question_metadata/r0_00000.parquet
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:b1be01ed2ba079e059b508e8faf8f271920a0bb2ddb190d2ac1ce0bfc52f7524
|
| 3 |
+
size 15600813
|