Update README.md

README.md

@@ -146,11 +146,16 @@ pretty_name: A/B Test Supertext vs DeepL
 size_categories:
 - 1K<n<10K
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-# Dataset Card for Dataset Name
+# A/B Test Supertext vs DeepL
 
 We release all evaluation data and scripts for further analysis and reproduction of the accompanying paper: [A comparison of translation performance between DeepL and Supertext](https://arxiv.org/abs/2502.02577).
 The data consists of document-level translations by Supertext and DeepL as well as accompanying ratings by professional translators. Please find more details in the paper.
 
+``` python
+# for each language pair, there is a separate subset
+data = load_dataset("Supertext/mt-doclevel-ab-test", "en-deCH")
+```
+
 ## Dataset Details
 
 As strong machine translation (MT) systems are increasingly based on large language models (LLMs), reliable quality benchmarking requires methods that capture their ability to leverage extended context. This study compares two commercial MT systems -- DeepL and Supertext -- by assessing their performance on unsegmented texts. We evaluate translation quality across four language directions with professional translators assessing segments with full document-level context. While segment-level assessments indicate no strong preference between the systems in most cases, document-level analysis reveals a preference for Supertext in three out of four language directions, suggesting superior consistency across longer texts. We advocate for more context-sensitive evaluation methodologies to ensure that MT quality assessments reflect real-world usability.