diff --git a/README.md b/README.md
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+++ b/README.md
@@ -46,7 +46,7 @@ Thanks to Samuel Domdey and chrsiebert for their support in making this model av
 
 ## Appendix 📚
 
-Please find an overview of the datasets used for training below. All datasets contain English text. The table summarizes which emotions are available in each of the datasets.
+Please find an overview of the datasets used for training below. All datasets contain English text. The table summarizes which emotions are available in each of the datasets. 
 
 |Name|anger|disgust|fear|joy|neutral|sadness|surprise|
 |---|---|---|---|---|---|---|---|
@@ -57,4 +57,6 @@ Please find an overview of the datasets used for training below. All datasets co
 |MELD, Poria et al. (2019)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
 |SemEval-2018, EI-reg (Mohammad et al. 2018) |Yes|-|Yes|Yes|-|Yes|-|
 
-The datasets represent a diverse collection of text types. Specifically, they contain emotion labels for texts from Twitter, Reddit, student self-reports, and utterances from TV dialogues. As MELD (Multimodal EmotionLines Dataset) extends the popular EmotionLines dataset, EmotionLines itself is not included here.
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+The datasets represent a diverse collection of text types. Specifically, they contain emotion labels for texts from Twitter, Reddit, student self-reports, and utterances from TV dialogues. As MELD (Multimodal EmotionLines Dataset) extends the popular EmotionLines dataset, EmotionLines itself is not included here. 
+
+The model is trained on a balanced subset from the datasets listed above (2,811 observations per emotion, i.e., nearly 20k observations in total). The evaluation accuracy on a holdout test set is 66% (and significantly above the random-chance baseline of 1/7 = 14%).
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