Add training set info

README.md

@@ -42,6 +42,7 @@ metrics:
 # SpanMarker for uncased Named Entity Recognition
 
 This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for Named Entity Recognition. In particular, this SpanMarker model uses [bert-base-uncased](https://huggingface.co/bert-base-uncased) as the underlying encoder. See [train.py](train.py) for the training script.
+It is trained on [P3ps/Cross_ner](https://huggingface.co/datasets/P3ps/Cross_ner), which I believe is a variant of [DFKI-SLT/cross_ner](https://huggingface.co/datasets/DFKI-SLT/cross_ner) that marged the validation set into the training set and applied deduplication.
 
 Is your data always capitalized correctly? Then consider using the cased variant of this model instead for better performance: 
 [tomaarsen/span-marker-bert-base-cross-ner](https://huggingface.co/tomaarsen/span-marker-bert-base-cross-ner).