Update README.md

README.md

@@ -105,37 +105,37 @@ outputs = model.batch_predict_with_embeds(texts, entity_embeddings, labels)
 
 Below you can see the table with benchmarking results on various named entity recognition datasets:
 
-| Dataset                | Score  |
-|------------------------|--------|
-| ACE 2004              | 29.9%  |
-| ACE 2005              | 25.8%  |
-| AnatEM                | 39.2%  |
-| Broad Tweet Corpus    | 70.8%  |
-| CoNLL 2003            | 62.5%  |
-| FabNER                | 22.7%  |
-| FindVehicle           | 40.7%  |
-| GENIA_NER             | 47.7%  |
-| HarveyNER             | 14.8%  |
-| MultiNERD             | 64.4%  |
-| Ontonotes             | 32.8%  |
-| PolyglotNER           | 45.2%  |
-| TweetNER7             | 37.8%  |
-| WikiANN en            | 53.3%  |
-| WikiNeural            | 77.2%  |
-| bc2gm                 | 51.8%  |
-| bc4chemd              | 49.3%  |
-| bc5cdr                | 64.6%  |
-| ncbi                  | 58.9%  |
-| **Average**           | **46.8%** |
-|                        |        |
-| CrossNER_AI           | 54.6%  |
-| CrossNER_literature   | 59.7%  |
-| CrossNER_music        | 69.7%  |
-| CrossNER_politics     | 73.2%  |
-| CrossNER_science      | 62.7%  |
-| mit-movie             | 47.9%  |
-| mit-restaurant        | 40.0%  |
-| **Average (zero-shot benchmark)** | **58.3%** |
+| Dataset                 | Score  |
+|-------------------------|--------|
+| ACE 2004               | 29.5%  |
+| ACE 2005               | 25.5%  |
+| AnatEM                 | 39.9%  |
+| Broad Tweet Corpus     | 70.9%  |
+| CoNLL 2003             | 65.8%  |
+| FabNER                 | 22.8%  |
+| FindVehicle            | 41.8%  |
+| GENIA_NER              | 46.8%  |
+| HarveyNER              | 15.2%  |
+| MultiNERD              | 70.9%  |
+| Ontonotes              | 34.9%  |
+| PolyglotNER            | 47.6%  |
+| TweetNER7              | 38.2%  |
+| WikiANN en             | 54.2%  |
+| WikiNeural             | 81.6%  |
+| bc2gm                  | 50.7%  |
+| bc4chemd               | 49.6%  |
+| bc5cdr                 | 65.0%  |
+| ncbi                   | 58.9%  |
+| **Average**            | **47.9%** |
+|                         |        |
+| CrossNER_AI            | 57.4%  |
+| CrossNER_literature    | 59.4%  |
+| CrossNER_music         | 71.1%  |
+| CrossNER_politics      | 73.8%  |
+| CrossNER_science       | 65.5%  |
+| mit-movie              | 48.6%  |
+| mit-restaurant         | 39.7%  |
+| **Average (zero-shot benchmark)** | **59.4%** |
 
 
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