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---
dataset_info:
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features:
- name: after
dtype: string
- name: before
dtype: string
- name: diff
dtype: string
- name: instruction
dtype: string
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- config_name: go
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dtype: string
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- name: instruction
dtype: string
- name: license
dtype: string
- name: repos
dtype: string
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configs:
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data_files:
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path: c/train-*
- config_name: c++
data_files:
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path: c++/train-*
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data_files:
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path: go/train-*
- config_name: java
data_files:
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path: java/train-*
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data_files:
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path: javascript/train-*
- config_name: php
data_files:
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path: php/train-*
- config_name: python
data_files:
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path: python/train-*
- config_name: ruby
data_files:
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path: ruby/train-*
- config_name: rust
data_files:
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path: rust/train-*
- config_name: scala
data_files:
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path: scala/train-*
- config_name: shell
data_files:
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path: shell/train-*
- config_name: swift
data_files:
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path: swift/train-*
- config_name: typescript
data_files:
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path: typescript/train-*
---
This is a dataset built from [CommitPackFT](https://huggingface.co/datasets/bigcode/commitpackft), providing ~1500 commits with diffs for several programming languages:
- Python
- JavaScript
- TypeScript
- Go
- Ruby
- Java
- PHP
- C
- C++
- Rust
- Swift
- Scala
- Bash
The goal of this dataset is to evaluate the ability of models to retrieve a diff given its instruction.
### Code To Produce Dataset
Below is the code to reproduce this dataset:
```py
import datasets
from tqdm import tqdm
import difflib
outrepo = "cassanof/CodeEditSearch"
LANGS = ["python", "javascript", "go", "ruby", "java", "php", "c", "c++", "rust", "swift",
"typescript", "scala", "kotlin", "r", "perl", "haskell", "lua", "shell", "dart", "julia"]
processed = []
def get_udiff(a, b):
a = a.splitlines()
b = b.splitlines()
diff = difflib.unified_diff(a, b, lineterm="")
return "\n".join(diff)
for lang in tqdm(LANGS):
print(f"Processing {lang}")
ds = datasets.load_dataset("bigcode/commitpackft", lang, split="train")
ds = ds.shuffle(seed=42)
print(f"{lang}: {len(ds)}")
ds = ds.filter(lambda x: len(
x["new_contents"] + x["old_contents"]) < 2500, num_proc=8)
ds = ds.filter(lambda x: len(x["new_contents"].strip()) > 0 and len(
x["old_contents"].strip()) > 0, num_proc=8)
if len(ds) < 2000:
print(f"Skipping {lang} due to insufficient data")
continue
print(f"{lang} after: {len(ds)}")
ds = ds.select(range(2000))
diffs = [get_udiff(a, b)
for a, b in zip(ds["old_contents"], ds["new_contents"])]
ds = {
"after": ds["new_contents"],
"before": ds["old_contents"],
"diff": diffs,
"instruction": ds["message"],
}
ds = datasets.Dataset.from_dict(ds)
ds = ds.filter(lambda x: len(x["diff"].splitlines()) > 10, num_proc=8)
print(f" ******* Final {lang}: {len(ds)} *******")
ds.push_to_hub(outrepo, lang)
processed.append(lang)
print(processed)
``` |