Datasets:
Tasks:
Text Generation
Sub-tasks:
language-modeling
Languages:
code
Multilinguality:
monolingual
Size Categories:
unknown
Tags:
License:
| from tqdm import tqdm | |
| from datasets import load_dataset, Dataset | |
| import hashlib | |
| import re | |
| import time | |
| from datasets import load_dataset | |
| PATTERN = re.compile(r"\s+") | |
| def parse_data(ds): | |
| """Parse data into markdown-code pairs""" | |
| markdowns = [] | |
| code_snippets = [] | |
| paths = [] | |
| repo_names = [] | |
| licenses = [] | |
| for i in tqdm(range(len(ds))): | |
| inner_markdowns = [] | |
| inner_code_snippets = [] | |
| types = ds[i]["types"] | |
| path = ds[i]["path"] | |
| repo = ds[i]["repo_name"] | |
| license = ds[i]["license"] | |
| if types[0] == "code": | |
| # drop first cell of code to have the notebook start with markdown | |
| cells = ds[i]["cells"][1:] | |
| types = types[1:] | |
| else: | |
| # drop first the two cells of markdown followed by code | |
| # the first markown cell of a notebook is often a long description of the whole notebook | |
| cells = ds[i]["cells"][2:] | |
| types = ds[i]["types"][2:] | |
| if len(cells) % 2 == 0: | |
| inner_markdowns = [cells[j] for j in range(len(cells)) if j % 2 == 0] | |
| inner_code_snippets = [cells[j+1] for j in range(len(cells) - 1) if j % 2 == 0] | |
| else: | |
| # delete last markdown cell that has no code next | |
| inner_markdowns = [cells[j] for j in range(len(cells) - 1) if j % 2 == 0] | |
| inner_code_snippets = [cells[j+1] for j in range(len(cells) - 2) if j % 2 == 0] | |
| markdowns.extend(inner_markdowns) | |
| code_snippets.extend(inner_code_snippets) | |
| paths.extend([path] * len(inner_markdowns)) | |
| repo_names.extend([repo] * len(inner_markdowns)) | |
| licenses.extend([license] * len(inner_markdowns)) | |
| return markdowns, code_snippets, paths, repo_names, licenses | |
| def get_hash(example): | |
| """Get hash of markdown + code""" | |
| text = example["markdown"] + example["code"] | |
| return {"hash": hashlib.md5(re.sub(PATTERN, "", text).encode("utf-8")).hexdigest()} | |
| def preprocess(example): | |
| """add hash column to dataset.""" | |
| results = dict() | |
| results.update(get_hash(example)) | |
| return results | |
| def check_uniques(example, uniques): | |
| """Check if current hash is still in set of unique hashes and remove if true.""" | |
| if example["hash"] in uniques: | |
| uniques.remove(example["hash"]) | |
| return True | |
| else: | |
| return False | |
| def filter(example, uniques): | |
| if not check_uniques(example, uniques): | |
| return False | |
| else: | |
| return True | |
| if __name__ == "__main__": | |
| ds = load_dataset("codeparrot/github-jupyter-parsed", split="train") | |
| print("Parsing data...") | |
| markdowns, code_snippets, paths, repo_names, licenses = parse_data(ds) | |
| data = {"markdown": markdowns, "code": code_snippets, "path": paths, "repo_name": repo_names, "license": licenses} | |
| parsed_data = Dataset.from_dict(data) | |
| print("Deduplication...") | |
| parsed_data = parsed_data.map(preprocess) | |
| # Deduplicate hashes | |
| uniques = set(parsed_data.unique("hash")) | |
| frac = len(uniques) / len(parsed_data) | |
| print(f"Fraction of duplicates: {1-frac:.2%}") | |
| ds_filter = parsed_data.filter(filter, fn_kwargs={"uniques": uniques}) | |
| ds_filter.push_to_hub("codeparrot/github-jupyter-text-code-pairs") | |