Update README.md

README.md

@@ -277,13 +277,17 @@ trained on all three datasets, outperforms classifiers that were trained on just
 
 Additionally, we evaluated the X-GENRE classifier on a multilingual X-GINCO dataset that comprises samples
 of texts from the MaCoCu web corpora (http://hdl.handle.net/11356/1969).
-The X-GINCO dataset comprises 790 instances in 10 languages - 
+The X-GINCO dataset comprises 790 manually-annotated instances in 10 languages - 
 Albanian, Croatian, Catalan, Greek, Icelandic, Macedonian, Maltese, Slovenian, Turkish, and Ukrainian.
 To evaluate the performance on genre labels, the dataset is balanced by labels,
 and the vague label "Other" is not included.
 Additionally, instances that were predicted with a confidence score below 0.80 were not included in the test dataset.
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 The evaluation shows high cross-lingual performance of the model,
 even when applied to languages that are not related to the training languages (English and Slovenian) and when applied on non-Latin scripts.
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 The outlier is Maltese, on which classifier does not perform well - 
 we presume that this is due to the fact that Maltese is not included in the pretraining data of the XLM-RoBERTa model.