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.gitignore
vendored
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*~
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*.swp
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*.bak
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*.pyc
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*.o
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.DS_Store
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.token
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README.md
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README.md
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Wikipedia discover date
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======================
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Guess the masked date in an wikipedia article.
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Only dates 1835-1935 are considered.
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You should predict only one masked date in an article described as [DATEPREDICT].
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[DATEMASK] are other masked dates. The predicted date should be in fractional year format.
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Fractional year format:
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Year is normalized as follows:
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'''
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days_in_year = 366 if is_leap else 365
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normalized = d.year + ((day_of_year-1) / days_in_year)
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'''
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Directory structure
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-------------------
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* `README.md` — this file
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* `config.txt` — configuration file
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* `train/` — directory with training data
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* `train/in.tsv` — input data for the train set
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* `train/expected.tsv` — expected (reference) data for the train set
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* `dev-0/` — directory with dev (test) data
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* `dev-0/in.tsv` — input data for the dev set
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* `dev-0/expected.tsv` — expected (reference) data for the dev set
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* `test-A` — directory with test data
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* `test-A/in.tsv` — input data for the test set
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* `test-A/expected.tsv` — expected (reference) data for the test set
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config.txt
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config.txt
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--metric RMSE-Against-Interval --metric MAE-Against-Interval --precision 2 --in-header in-header.tsv --out-header out-header.tsv
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dev-0/expected.tsv
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dev-0/in.tsv.xz
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in-header.tsv
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in-header.tsv
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title text
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out-header.tsv
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fractional_year
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test-A/in.tsv.xz
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train/expected.tsv
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train/expected.tsv
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train/in.tsv.xz
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