google/seq2seq

Prepare WMT'17 Datasets

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#21 建立於 2017年3月11日

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 (3 留言) (0 反應) (0 負責人)Python (5,587 star) (1,329 fork)batch import
datahelp wanted

描述

We should prepare datasets for All WMT'17 language pairs. This is also a change to try out google/sentencepiece as a preprocessor.

Each dataset should come in different configurations, i.e. different vocabulary sizes and also have a character-level version.

Together with the raw data files we also need the script that was used for the process.

貢獻者指南

技術棧
pythontensorflow
領域
datamachine learning
議題類型
feature
難度面向新貢獻者的預計實作難度，1 表示很小改動，5 表示專家級工作。
3
預計時間有經驗貢獻者完成調查、實作、測試並準備 pull request 的粗略時間範圍。
3-5 days
活動狀態議題目前的可參與程度：新鮮、活躍、陳舊、阻塞或等待維護者輸入。
stale
清晰度議題是否清楚說明預期改動、驗收標準和下一步。
clear
前置要求
PythonTensorFlowData preprocessingSentencePiece
新手友善度1-100 的估計分數，表示該議題對首次貢獻者的友善程度。
40
研究方向
Examine the existing data preparation scripts in the repository to understand the expected format. For each WMT'17 language pair, download the raw data, then apply sentencepiece tokenization with varying vocabulary sizes (e.g., 8k, 16k, 32k) and also create a character level version. The script should be reusable and saved alongside the processed data. Refer to the WMT17 task page for exact language pairs and data sources.