Datasetpublic

muhammadravi251001/idk-mrc-nli

You can download this Dataset just like this (if you only need: premise, hypothesis, and label column): from datasets import load_dataset, Dataset, DatasetDict import pandas as pd data_files = {"train": "data_nli_train_df.csv", "validation": "data_nli_val_df.csv", "test": "data_nli_test_df.csv"} dataset = load_dataset("muhammadravi251001/idk-mrc-nli", data_files=data_files) selected_columns = ["premise", "hypothesis", "label"] # selected_columns =… See the full description on the dataset page: https://huggingface.co/datasets/muhammadravi251001/idk-mrc-nli.

sourceHugging Faceopenrailupdated 3y agoView on Hugging Face

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You can download this Dataset just like this (if you only need: premise, hypothesis, and label column):

from datasets import load_dataset, Dataset, DatasetDict
import pandas as pd

data_files = {"train": "data_nli_train_df.csv", 
              "validation": "data_nli_val_df.csv", 
              "test": "data_nli_test_df.csv"}

dataset = load_dataset("muhammadravi251001/idk-mrc-nli", data_files=data_files)

selected_columns = ["premise", "hypothesis", "label"]
# selected_columns = dataset.column_names['train'] # Uncomment this line to retrieve all of the columns

df_train = pd.DataFrame(dataset["train"])
df_train = df_train[selected_columns]

df_val = pd.DataFrame(dataset["validation"])
df_val = df_val[selected_columns]

df_test = pd.DataFrame(dataset["test"])
df_test = df_test[selected_columns]

train_dataset = Dataset.from_dict(df_train)
validation_dataset = Dataset.from_dict(df_val)
test_dataset = Dataset.from_dict(df_test)

dataset = DatasetDict({"train": train_dataset, "validation": validation_dataset, "test": test_dataset})
dataset

This is some modification from IDK-MRC dataset to IDK-MRC-NLI dataset. By convert QAS dataset to NLI dataset. You can find the original IDK-MRC in this link: https://huggingface.co/datasets/rifkiaputri/idk-mrc.

Citation Information

bibtex

@inproceedings{putri-oh-2022-idk,
    title = "{IDK}-{MRC}: Unanswerable Questions for {I}ndonesian Machine Reading Comprehension",
    author = "Putri, Rifki Afina  and
      Oh, Alice",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.emnlp-main.465",
    pages = "6918--6933",
}