File size: 1,348 Bytes
bc4689e 87d04e0 bc4689e 87d04e0 bc4689e 87d04e0 bc4689e 87d04e0 bc4689e 87d04e0 bc4689e 2a78aea |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 |
---
dataset_info:
features:
- name: lang
dtype: string
- name: example_id
dtype: string
- name: query
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 4193271
num_examples: 40548
download_size: 2118715
dataset_size: 4193271
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# mkqa filtered version
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**
```
mkqa = load_dataset("mkqa", split="train").to_pandas()
needed_langs = ["en", "ar", "de", "es", "vi", "zh_cn"]
rows = []
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"])
filtered_dataset.dropna(inplace=True)
filtered_dataset.reset_index(drop=True, inplace=True)
```
**How to download**
```
from datasets import load_dataset
data = load_dataset("dkoterwa/oasst1_filtered_retrieval")
``` |