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--- |
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dataset_info: |
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features: |
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- name: context |
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dtype: string |
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- name: question |
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dtype: string |
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- name: id |
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dtype: string |
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- name: lang |
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dtype: string |
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- name: answer |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 46553350 |
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num_examples: 41019 |
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download_size: 24529122 |
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dataset_size: 46553350 |
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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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--- |
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# mlqa filtered version |
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|
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For a better dataset description, please visit the official site of the source dataset: [LINK](https://huggingface.co/datasets/mlqa) <br> |
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<br> |
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**This dataset was prepared by converting mlqa dataset**. I've concatenated versions of the dataset for languages of interest and retrieved a text answers from "answers" column. |
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|
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**I additionaly share the code which I used to convert the original dataset to make everything more clear** |
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``` |
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def download_mlqa(subset_name): |
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dataset_valid = load_dataset("mlqa", subset_name, split="validation").to_pandas() |
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dataset_test = load_dataset("mlqa", subset_name, split="test").to_pandas() |
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full_dataset = pd.concat([dataset_valid, dataset_test]) |
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full_dataset.reset_index(drop=True, inplace=True) |
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return full_dataset |
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|
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needed_langs = ["mlqa.en.en", "mlqa.de.de", "mlqa.ar.ar", "mlqa.es.es", "mlqa.vi.vi", "mlqa.zh.zh"] |
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datasets = [] |
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for lang in tqdm(needed_langs): |
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dataset = download_mlqa(lang) |
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dataset["lang"] = lang.split(".")[2] |
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datasets.append(dataset) |
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|
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full_mlqa = pd.concat(datasets) |
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full_mlqa.reset_index(drop=True, inplace=True) |
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full_mlqa["answer"] = [answer_dict["text"][0] for answer_dict in full_mlqa["answers"]] |
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full_mlqa.drop("answers", axis=1, inplace=True) |
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``` |
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|
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**How to download** |
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|
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``` |
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from datasets import load_dataset |
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data = load_dataset("dkoterwa/mlqa_filtered") |
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``` |