Update parquet files

.gitattributes

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-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zstandard filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
-# Audio files - uncompressed
-*.pcm filter=lfs diff=lfs merge=lfs -text
-*.sam filter=lfs diff=lfs merge=lfs -text
-*.raw filter=lfs diff=lfs merge=lfs -text
-# Audio files - compressed
-*.aac filter=lfs diff=lfs merge=lfs -text
-*.flac filter=lfs diff=lfs merge=lfs -text
-*.mp3 filter=lfs diff=lfs merge=lfs -text
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README.md

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----
-annotations_creators: []
-language:
-- code
-license:
-- other
-multilinguality:
-- monolingual
-size_categories:
-- unknown
-task_categories:
-- text-generation
-task_ids:
-- language-modeling
-pretty_name: github-jupyter-text-code-pairs
----
-
-This is a parsed version of [github-jupyter-parsed](https://huggingface.co/datasets/codeparrot/github-jupyter-parsed), with markdown and code pairs. We provide the preprocessing script in [preprocessing.py](https://huggingface.co/datasets/codeparrot/github-jupyter-parsed-v2/blob/main/preprocessing.py). The data is deduplicated and consists of 451662 examples. 
-
-For similar datasets with text and Python code, there is [CoNaLa](https://huggingface.co/datasets/neulab/conala) benchmark from StackOverflow, with some samples curated by annotators.

dataset_infos.json

@@ -1,52 +0,0 @@
-{"loubnabnl--github_jupyter_parsed_2": {
-    "description": "",
-    "citation": "",
-    "homepage": "",
-    "license": "",
-    "features": {
-        "markdown": {
-            "dtype": "string",
-            "id": null,
-            "_type": "Value"
-        },
-        "code": {
-            "dtype": "string",
-            "id": null,
-            "_type": "Value"
-        },
-        "path": {
-            "dtype": "string",
-            "id": null,
-            "_type": "Value"
-        },
-        "repo_name": {
-            "dtype": "string",
-            "id": null,
-            "_type": "Value"
-        },
-        "license": {
-            "dtype": "string",
-            "id": null,
-            "_type": "Value"
-        }
-    },
-    "post_processed": null,
-    "supervised_keys": null,
-    "task_templates": null,
-    "builder_name": null,
-    "config_name": null,
-    "version": null,
-    "splits": {
-        "train": {
-            "name": "train",
-            "num_bytes": 367027734.92262346,
-            "num_examples": 451662,
-            "dataset_name": "github_jupyter_parsed_2"
-        }
-    },
-    "download_checksums": null,
-    "download_size": 181325865,
-    "post_processing_size": null,
-    "dataset_size": 367027734.92262346,
-    "size_in_bytes": 548353599.9226234
-}}

data/train-00000-of-00001-81d7ff8514320810.parquet → loubnabnl--github_jupyter_parsed_2/parquet-train.parquet

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-size 181325865
+oid sha256:5ec2af2ce3c13b2d3b1f8d7d82da6fd6b58434711366f3f562bd38a8e1609f23
+size 185260303

preprocessing.py

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-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")