Link to MultiNERD and warning

README.md

@@ -13,7 +13,7 @@ widget:
   example_title: "Spanish"
 - text: "Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris ."
   example_title: "English"
-- text: "Amelia Earthart a fait voler son monomoteur Lockheed Vega 5B à travers l'ocean Atlantique jusqu'à Paris ."
+- text: "Amelia Earthart a fait voler son monomoteur Lockheed Vega 5B à travers l' ocean Atlantique jusqu'à Paris ."
   example_title: "French"
 - text: "Amelia Earthart flog mit ihrer einmotorigen Lockheed Vega 5B über den Atlantik nach Paris ."
   example_title: "German"
@@ -60,7 +60,7 @@ metrics:
 
 # SpanMarker for 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 [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) as the underlying encoder. See [train.py](train.py) for the training script.
+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 [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) as the underlying encoder. See [train.py](train.py) for the training script.
 
 ## Metrics
 
@@ -117,4 +117,17 @@ model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-xlm-roberta-base-
 entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.")
 ```
 
-See the [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) repository for documentation and additional information on this library.
+
+**Warning**: This model works best when punctuation is separated from the prior words, so 
+```python
+# ✅
+model.predict("He plays J. Robert Oppenheimer , an American theoretical physicist .")
+# ❌
+model.predict("He plays J. Robert Oppenheimer, an American theoretical physicist.")
+
+# You can also supply a list of words directly: ✅
+model.predict(["He", "plays", "J.", "Robert", "Oppenheimer", ",", "an", "American", "theoretical", "physicist", "."])
+```
+The same may be beneficial for some languages, such as splitting `"l'ocean Atlantique"` into `"l' ocean Atlantique"`.
+
+See the [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) repository for documentation and additional information on this library.