Dataset card: how it relates to the models' build

README.md

@@ -1581,7 +1581,7 @@ configs:
 
 **MillID** (MultiLingual Language IDentifier) is the dataset behind the MillID language identifiers ([MillID tiny v1.5](https://huggingface.co/baobabtech/millid-tiny-v1.5), [MillID medium v1](https://huggingface.co/baobabtech/millid-medium-v1), [MillID large v1.5](https://huggingface.co/baobabtech/millid-large-v1.5); compared with GlotLID and fastText on the [leaderboard](https://huggingface.co/baobabtech/millid)). Passages of text labelled with their language, for training and evaluating a language identifier: **1,641 language-script labels** (1,551 ISO 639-3 languages, 56 scripts), **2,390,082 passages**, plus the Universal Declaration of Human Rights in 429 languages as a human-labelled test set.
 
-The MillID models were trained and scored on a fuller build that also holds Save the Children's (23,104 passages) and Nal'ibali's (16,500 passages) text, which is not ours to redistribute. This dataset is that build without it, and nothing else changed: every passage keeps its split and every label its id, so `test` here is the models' test split without those passages.
+The MillID models were trained and scored on a fuller build that also holds Save the Children's (23,104 passages) and Nal'ibali's (16,500 passages) text, which is not ours to redistribute. This dataset is that build without it: every other passage keeps its split and every label its id, so `test` here is the models' test split without those passages.
 
 Built by [Baobab Tech](https://huggingface.co/baobabtech) to identify the language of real documents, so most of the text comes from PDFs, cut the way a page is read: short lines, sentences and paragraphs. Code: [baobab-tech/millid](https://github.com/baobab-tech/millid).