無需真實數據的高效表格預訓練：TAPEX 概念與 Hugging Face 整合介紹
Original: Efficient Table Pre-training without Real Data: An Introduction to TAPEX
When working with structured data such as tables, traditional pre-trained models typically require crawling large amounts of real-world…
微軟提出的 TAPEX（Table Pre-training via Execution）是一種創新的表格預訓練方法，現已整合至 Hugging Face。它不依賴網路爬取的真實表格，而是利用隨機生成的 SQL 查詢及其執行結果（合成數據）來訓練 Seq2Seq 模型。這種「藉由執行來學習」的方式，顯著提升了模型對表格數據的推理能力，並在 WikiSQL 和 WikiTableQuestions 等基準測試中取得領先。
When working with structured data such as tables, traditional pre-trained models typically require crawling large amounts of real-world tables and related text from the web. However, this approach faces challenges including noisy data, privacy concerns, and difficulties in aligning tables with text. TAPEX (Table Pre-training via Execution), proposed by Microsoft Research Asia, breaks through these limitations. The core idea of TAPEX is "table pre-training via execution" — it requires no real-world table data whatsoever and relies entirely on synthetic data.
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