|
--- |
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dataset_info: |
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features: |
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- name: lang |
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dtype: string |
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- name: example_id |
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dtype: string |
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- name: query |
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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: 4193271 |
|
num_examples: 40548 |
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download_size: 2118715 |
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dataset_size: 4193271 |
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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-* |
|
--- |
|
|
|
# mkqa filtered version |
|
|
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For a better dataset description, please visit the official site of the source dataset: [LINK](https://huggingface.co/datasets/mkqa) <br> |
|
<br> |
|
**This dataset was prepared by converting mkqa dataset**. |
|
|
|
**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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mkqa = load_dataset("mkqa", split="train").to_pandas() |
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needed_langs = ["en", "ar", "de", "es", "vi", "zh_cn"] |
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rows = [] |
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for i, row in tqdm(mkqa.iterrows(), total=mkqa.shape[0]): |
|
for lang in needed_langs: |
|
rows.append([lang, row["example_id"], row["queries"][lang], row["answers"][lang][0]["text"]]) |
|
|
|
filtered_dataset = pd.DataFrame(rows, columns=["lang", "example_id", "query", "answer"]) |
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filtered_dataset.dropna(inplace=True) |
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filtered_dataset.reset_index(drop=True, inplace=True) |
|
``` |
|
|
|
**How to download** |
|
|
|
``` |
|
from datasets import load_dataset |
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data = load_dataset("dkoterwa/oasst1_filtered_retrieval") |
|
``` |
