Add metrics, widget examples & link to train.py

README.md

@@ -8,11 +8,59 @@ tags:
 - ner
 - named-entity-recognition
 pipeline_tag: token-classification
+widget:
+- text: "Amelia Earthart voló su Lockheed Vega 5B monomotor a través del Océano Atlántico hasta París ."
+  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 ."
+  example_title: "French"
+- text: "Amelia Earthart flog mit ihrer einmotorigen Lockheed Vega 5B über den Atlantik nach Paris ."
+  example_title: "German"
+- text: "Амелия Эртхарт перелетела на своем одномоторном самолете Lockheed Vega 5B через Атлантический океан в Париж ."
+  example_title: "Russian"
+- text: "Amelia Earthart vloog met haar één-motorige Lockheed Vega 5B over de Atlantische Oceaan naar Parijs ."
+  example_title: "Dutch"
+- text: "Amelia Earthart przeleciała swoim jednosilnikowym samolotem Lockheed Vega 5B przez Ocean Atlantycki do Paryża ."
+  example_title: "Polish"
+- text: "Amelia Earthart flaug eins hreyfils Lockheed Vega 5B yfir Atlantshafið til Parísar ."
+  example_title: "Icelandic"
+- text: "Η Amelia Earthart πέταξε το μονοκινητήριο Lockheed Vega 5B της πέρα ​​από τον Ατλαντικό Ωκεανό στο Παρίσι ."
+  example_title: "Greek"
+model-index:
+  - name: SpanMarker w. xlm-roberta-base on MultiNERD by Tom Aarsen
+    results:
+      - task:
+          type: token-classification
+          name: Named Entity Recognition
+        dataset:
+          type: Babelscape/multinerd
+          name: MultiNERD
+          split: test
+          revision: 2814b78e7af4b5a1f1886fe7ad49632de4d9dd25
+        metrics:
+          - type: f1
+            value: 0.91314
+            name: F1
+          - type: precision
+            value: 0.91994
+            name: Precision
+          - type: recall
+            value: 0.90643
+            name: Recall
+datasets:
+  - Babelscape/multinerd
+language:
+  - multilingual
+metrics:
+  - f1
+  - recall
+  - precision
 ---
 
 # 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. 
+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.
 
 ## Usage
 
@@ -28,7 +76,7 @@ You can then run inference with this model like so:
 from span_marker import SpanMarkerModel
 
 # Download from the 🤗 Hub
-model = SpanMarkerModel.from_pretrained("span_marker_model_name")
+model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-xlm-roberta-base-multinerd")
 # Run inference
 entities = model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.")
 ```