Add NetEaseCrowd dataset

[shenxiangzhuang]

Checklist

• I have read the CONTRIBUTING document
• I hereby agree to the terms of the CLA available at: https://yandex.ru/legal/cla/?lang=en
• My change requires a change to the documentation
• I have updated the documentation accordingly
• I have added tests to cover my changes
• All new and existing tests passed

Dataset info

Adding our open-source dataset, NetEaseCrowd(https://github.com/fuxiAIlab/NetEaseCrowd-Dataset).

NetEaseCrowd is a large-scale dataset for long-term and online crowdsourcing truth inference, which contains about 2,400 workers, 1,000,000 tasks, and 6,000,000 annotations collected over 6 months. We believe that this dataset could be an invaluable asset to the Toloka/crowd-kit community by providing a new benchmark for crowdsourcing-related research and development.

[codecov-commenter]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 92.96%. Comparing base (07c4240) to head (08440a2).
Report is 34 commits behind head on main.

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Additional details and impacted files

@@            Coverage Diff             @@
##             main     #101      +/-   ##
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+ Coverage   92.80%   92.96%   +0.15%     
==========================================
  Files          47       47              
  Lines        2070     2216     +146     
==========================================
+ Hits         1921     2060     +139     
- Misses        149      156       +7     

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[pilot7747]

Hi @shenxiangzhuang! Thank you for contributing this dataset. Lgtm

[shenxiangzhuang]

Besides the CI test, I also tested to use this dataset do categorical aggregation and it works well:

from crowdkit.aggregation import DawidSkene
from crowdkit.datasets import load_dataset

df, gt = load_dataset('netease_crowd')

ds = DawidSkene(10)
result = ds.fit_predict(df)

print(len(result))
# 999799

[dustalov]

Thank you for a very well-done PR! I noticed a small imperfection in the dataset metadata. Could you please check my suggestion?

[shenxiangzhuang]

Thank you for a very well-done PR! I noticed a small imperfection in the dataset metadata. Could you please check my suggestion?

Thanks a lot for your carefully review!

[dustalov]

Great job, thank you again!