Update README.md

README.md

@@ -74,14 +74,19 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-This model is for reference-free quality estimation (QE) of machine translation (MT) systems.
+This model is for reference-free, long-context quality estimation (QE) of machine translation (MT) systems.
+It trained on a dataset of texts of up to 32 sentences (64 sentences for the source and target).
+Hence, this model is suitable for document-level quality estimation.
 
 ## Training and evaluation data
 
 The model is trained on the long-context dataset [ymoslem/wmt-da-human-evaluation-long-context](https://huggingface.co/datasets/ymoslem/wmt-da-human-evaluation-long-context).
+The used long-context / document-level dataset for Quality Estimation of Machine Translation is an augmented variant of the sentence-level WMT DA Human Evaluation dataset.
+In addition to individual sentences, it contains augmentations of 2, 4, 8, 16, and 32 sentences, among each language pair `lp` and `domain`.
+The `raw` column represents a weighted average of scores of augmented sentences using character lengths of `src` and `mt` as weights.
 
-* Training: 7.65 million long-context texts
-* Test: 59,235 long-context texts
+* Training data: 7.65 million long-context texts
+* Test data: 59,235 long-context texts
 
 ## Training procedure