File size: 3,626 Bytes
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from datasets import DatasetDict, Features, Value, load_dataset
from huggingface_hub import HfApi, hf_hub_download
languages = [
"de",
"bn",
"it",
"pt",
"nl",
"cs",
"ro",
"bg",
"sr",
"fi",
"fa",
"hi",
"da",
"en",
"no",
"sv",
]
def apply_query_id(example, queries_dict):
title = example["title"]
corpus_id = example["_id"]
score = example["score"]
query_id = queries_dict[title]
return {"query-id": query_id, "corpus-id": corpus_id, "score": score}
for lang in languages:
ds_queries = load_dataset(
f"rasdani/cohere-wikipedia-2023-11-{lang}-queries", split="train"
)
ds_corpus = load_dataset(
f"rasdani/cohere-wikipedia-2023-11-{lang}-1.5k-articles", split="train"
)
sleep(5) # HF hub rate limit
queries = ds_queries.map(
lambda x: {"_id": "q" + x["_id"], "text": x["query"]},
remove_columns=ds_queries.column_names,
)
queries_dict = {row["title"]: "q" + row["_id"] for row in ds_queries}
corpus = ds_corpus.map(
lambda x: {"_id": x["_id"], "title": x["title"], "text": x["text"]},
remove_columns=ds_corpus.column_names,
)
qrels = ds_corpus.map(
lambda x: apply_query_id(x, queries_dict=queries_dict),
remove_columns=ds_corpus.column_names,
)
corpus_features = Features(
{"_id": Value("string"), "title": Value("string"), "text": Value("string")}
)
queries_features = Features({"_id": Value("string"), "text": Value("string")})
qrels_features = Features(
{
"query-id": Value("string"),
"corpus-id": Value("string"),
"score": Value("float32"),
}
)
corpus = corpus.cast(corpus_features)
queries = queries.cast(queries_features)
qrels = qrels.cast(qrels_features)
ds_dict = DatasetDict({"corpus": corpus, "queries": queries, "qrels": qrels})
# repo_id = f"ellamind/wikipedia-2023-11-retrieval-{lang}"
repo_id = "ellamind/wikipedia-2023-11-retrieval-multilingual"
# corpus.push_to_hub(repo_id, config_name="corpus", split="test")
# queries.push_to_hub(repo_id, config_name="queries", split="test")
# qrels.push_to_hub(repo_id, config_name="default", split="test")
corpus.push_to_hub(repo_id + "-corpus", config_name=lang, split="test")
queries.push_to_hub(repo_id + "-queries", config_name=lang, split="test")
qrels.push_to_hub(repo_id + "-qrels", config_name=lang, split="test")
# Download the README from the repository
sleep(5)
readme_path = hf_hub_download(
repo_id=repo_id, filename="README.md", repo_type="dataset"
)
with open(readme_path, "r") as f:
readme_content = f.read()
readme = """
This dataset is derived from Cohere's wikipedia-2023-11 dataset, which is in turn derived from `wikimedia/wikipedia`.
The dataset is licensed under the Creative Commons CC BY-SA 3.0 license.
"""
# Prepend the license key to the YAML header and append the custom README
if "- license: " not in readme_content and readme not in readme_content:
license = "cc-by-sa-3.0"
updated_readme = readme_content.replace(
"---\ndataset_info:", "---\nlicense: {license}\ndataset_info:"
).format(license=license)
updated_readme += readme
api = HfApi()
readme_bytes = updated_readme.encode("utf-8")
api.upload_file(
path_or_fileobj=readme_bytes,
path_in_repo="README.md",
repo_id=repo_id,
repo_type="dataset",
)
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