Add links to lxyuan's uncased model

README.md

@@ -89,6 +89,9 @@ pinned: true
 
 This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for multilingual Named Entity Recognition trained on the [MultiNERD](https://huggingface.co/datasets/Babelscape/multinerd) dataset. In particular, this SpanMarker model uses [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) as the underlying encoder. See [train.py](train.py) for the training script.
 
+Is your data not (always) capitalized correctly? Then consider using this uncased variant of this model by [@lxyuan](https://huggingface.co/lxyuan) for better performance: 
+[lxyuan/span-marker-bert-base-multilingual-uncased-multinerd](https://huggingface.co/lxyuan/span-marker-bert-base-multilingual-uncased-multinerd).
+
 ## Metrics
 
 | **Language** | **Precision** | **Recall** | **F1**     |
@@ -275,6 +278,7 @@ The following hyperparameters were used during training:
 
 ## See also
 * [lxyuan/span-marker-bert-base-multilingual-cased-multinerd](https://huggingface.co/lxyuan/span-marker-bert-base-multilingual-cased-multinerd) is similar to this model, but trained on 3 epochs instead of 2. It reaches better performance on 7 out of the 10 languages.
+* [lxyuan/span-marker-bert-base-multilingual-uncased-multinerd](https://huggingface.co/lxyuan/span-marker-bert-base-multilingual-uncased-multinerd) is a strong uncased variant of this model, also trained on 3 epochs instead of 2.
 
 ## Contributions
 Many thanks to [Simone Tedeschi](https://huggingface.co/sted97) from [Babelscape](https://babelscape.com) for his insight when training this model and his involvement in the creation of the training dataset.