Update README.md

README.md

@@ -16,7 +16,7 @@ To train Aleph-Alpha-GermanWeb-Quality-Classifier-fastText, we used an LLM-as-a-
 
 For each document, we calculated a combined educational quality score by taking the minimum over the three criteria rated by the LLM-as-a-judge. We then used these educational quality scores as the training signal for the quality classification model. The Aleph-Alpha-GermanWeb-Quality-Classifier-fastText model was tasked with distinguishing between texts with educational quality scores of one or two (“low quality”) vs. four or five (“high quality”) given the document's text.
 
-We trained Aleph-Alpha-GermanWeb-Quality-Classifier-fastText using 185,403 documents in each class. We used 95% of the data (and the remaining 5% for validation) to train a fastText model to classify between high and low quality text data. It reached 92% precision and 91.5% recall on the validation set.
+We trained Aleph-Alpha-GermanWeb-Quality-Classifier-fastText using 185,403 documents in each class. We used 95% of the data (and the remaining 5% for validation) to train a fastText model to classify between high and low quality text data. It reached 77% precision and 77% recall on the validation set.
 
 Further details, including our LLM judging prompt, can be found in our accompanying paper (link to paper coming soon).