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README.md
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---
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dataset_size: 13914139
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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- split: validation
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path: data/validation-*
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---
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language:
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- en
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license: apache-2.0
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task_categories:
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- text-generation
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- fill-mask
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tags:
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- code
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- rust
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- hyperswitch
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- repo-specific-finetuning
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pretty_name: hyperswitch Code Corpus (Track A Split)
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size_categories:
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- n<1K
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# archit11/hyperswitch-code-corpus-track-a
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Repository-specific code corpus extracted from `hyperswitch` and split by file for training/evaluation.
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## What is in this dataset
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- Source corpus: `data/code_corpus_hyperswitch`
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- Total files: 300
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- Train files: 270
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- Validation files: 30
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- Test files: 0
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- File type filter: .rs
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- Split mode: `file` (file-level holdout)
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Each row has:
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- `file_name`: flattened source file name
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- `text`: full file contents
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## Training context
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This dataset was used for extended pretraining of:
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- Model repo: `https://huggingface.co/archit11/qwen2.5-coder-3b-hyperswitch-track-a-lora`
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- Base model: `/root/.cache/huggingface/hub/models--Qwen--Qwen2.5-Coder-3B/snapshots/09d9bc5d376b0cfa0100a0694ea7de7232525803`
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- Sequence curriculum: [768, 1024, 1536]
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- Learning rate: 0.001
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- Batch size: 1
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Evaluation from this run:
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- Baseline perplexity: 2.2832
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- Post-training perplexity: 1.5429
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## Load with datasets
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```python
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from datasets import load_dataset
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ds = load_dataset("archit11/hyperswitch-code-corpus-track-a")
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print(ds)
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print(ds["train"][0]["file_name"])
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```
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