Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
parquet
Languages:
Icelandic
Size:
10K - 100K
License:
File size: 1,834 Bytes
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from datasets import Dataset, DatasetDict, load_dataset, concatenate_datasets
import pandas as pd
import os
def prepare_nqii(path, split="train"):
ds = load_dataset(path)
ds = ds[split]
# to dataframe
df = pd.DataFrame(ds)
df = df.drop(columns=["title"])
df["start"] = df["answers"].apply(lambda x: x["answer_start"])
df["answers"] = df["answers"].apply(lambda x: x["text"])
# change type
ds = Dataset.from_pandas(df)
return ds
def prepare_ruquad(path):
df = pd.read_json(path)
# filter
df = df[df["type"] == "ANSWERED_WITH_SPAN"]
# drop index
df = df.reset_index(drop=True)
df = df.rename(columns={"question_id":"id", "paragraph": "context", "span":"answers"})
df = df.drop(columns=[ "type", "answer_id", "article_id", "end", "source", "answer"])
df["start"] = df["start"].astype(int)
df["answers"] = df["answers"].apply(lambda x: [x])
df["start"] = df["start"].apply(lambda x: [x])
ds = Dataset.from_pandas(df)
return ds
def download_ruquad():
os.system("curl --remote-name-all https://repository.clarin.is/repository/xmlui/bitstream/handle/20.500.12537/310{/RUQuAD1.zip}")
os.system("unzip RUQuAD1.zip")
if __name__ == "__main__":
nqii_train = prepare_nqii("vesteinn/icelandic-qa-NQiI", split="train")
ruquad_train = prepare_ruquad("train.json")
train = concatenate_datasets([nqii_train, ruquad_train])
val = prepare_nqii("vesteinn/icelandic-qa-NQiI", split="validation")
nqi_test = prepare_nqii("vesteinn/icelandic-qa-NQiI", split="test")
ruquad_test = prepare_ruquad("test.json")
test = concatenate_datasets([nqi_test, ruquad_test])
dataset = DatasetDict({"train": train, "validation": val, "test": test})
print(dataset)
dataset.push_to_hub("Sigurdur/nqii_ruqad")
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