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--- |
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dataset_info: |
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features: |
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- name: premise |
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dtype: string |
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- name: entailment |
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dtype: string |
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- name: contradiction |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 2022859.0657676577 |
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num_examples: 8142 |
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- name: validation |
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num_bytes: 224844.9342323422 |
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num_examples: 905 |
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download_size: 1572558 |
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dataset_size: 2247704 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: validation |
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path: data/validation-* |
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language: |
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- ko |
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pretty_name: k |
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size_categories: |
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- 1K<n<10K |
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--- |
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# KLUENLI for SimCSE Dataset |
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For a better dataset description, please visit: [LINK](https://klue-benchmark.com/) <br> |
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<br> |
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**This dataset was prepared by converting KLUENLI dataset** to use it for contrastive training (SimCSE). The code used to prepare the data is given below: |
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```py |
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import pandas as pd |
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from datasets import load_dataset, concatenate_datasets, Dataset |
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from torch.utils.data import random_split |
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class PrepTriplets: |
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@staticmethod |
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def make_dataset(): |
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train_dataset = load_dataset("klue", "nli", split="train") |
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val_dataset = load_dataset("klue", "nli", split="validation") |
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merged_dataset = concatenate_datasets([train_dataset, val_dataset]) |
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triplets_dataset = PrepTriplets._get_triplets(merged_dataset) |
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# Split back into train and validation |
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train_size = int(0.9 * len(triplets_dataset)) |
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val_size = len(triplets_dataset) - train_size |
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train_subset, val_subset = random_split( |
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triplets_dataset, [train_size, val_size] |
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) |
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# Convert Subset objects back to Dataset |
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train_dataset = triplets_dataset.select(train_subset.indices) |
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val_dataset = triplets_dataset.select(val_subset.indices) |
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return train_dataset, val_dataset |
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@staticmethod |
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def _get_triplets(dataset): |
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df = pd.DataFrame(dataset) |
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entailments = df[df["label"] == 0] |
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contradictions = df[df["label"] == 2] |
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triplets = [] |
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for premise in df["premise"].unique(): |
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entailment_hypothesis = entailments[entailments["premise"] == premise][ |
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"hypothesis" |
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].tolist() |
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contradiction_hypothesis = contradictions[ |
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contradictions["premise"] == premise |
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]["hypothesis"].tolist() |
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if entailment_hypothesis and contradiction_hypothesis: |
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triplets.append( |
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{ |
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"premise": premise, |
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"entailment": entailment_hypothesis[0], |
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"contradiction": contradiction_hypothesis[0], |
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} |
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) |
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triplets_dataset = Dataset.from_pandas(pd.DataFrame(triplets)) |
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return triplets_dataset |
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# Example usage: |
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# PrepTriplets.make_dataset() |
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``` |
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**How to download** |
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``` |
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from datasets import load_dataset |
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data = load_dataset("phnyxlab/klue-nli-simcse") |
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``` |
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**If you use this dataset for research, please cite this paper:** |
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``` |
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@misc{park2021klue, |
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title={KLUE: Korean Language Understanding Evaluation}, |
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author={Sungjoon Park and Jihyung Moon and Sungdong Kim and Won Ik Cho and Jiyoon Han and Jangwon Park and Chisung Song and Junseong Kim and Yongsook Song and Taehwan Oh and Joohong Lee and Juhyun Oh and Sungwon Lyu and Younghoon Jeong and Inkwon Lee and Sangwoo Seo and Dongjun Lee and Hyunwoo Kim and Myeonghwa Lee and Seongbo Jang and Seungwon Do and Sunkyoung Kim and Kyungtae Lim and Jongwon Lee and Kyumin Park and Jamin Shin and Seonghyun Kim and Lucy Park and Alice Oh and Jung-Woo Ha and Kyunghyun Cho}, |
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year={2021}, |
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eprint={2105.09680}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
