Update README.md

README.md

@@ -91,38 +91,39 @@ outputs = model.batch_predict_with_embeds(texts, entity_embeddings, labels)
 
 ### Benchmarks
 Below you can see the table with benchmarking results on various named entity recognition datasets:
+Here’s the updated table with your provided data:
 
 | Dataset                 | Score  |
 |-------------------------|--------|
-| ACE 2004                | 26.8%  |
-| ACE 2005                | 29.2%  |
-| AnatEM                  | 25.3%  |
-| Broad Tweet Corpus      | 66.8%  |
-| CoNLL 2003              | 60.3%  |
-| FabNER                 | 21.2%  |
-| FindVehicle             | 28.3%  |
-| GENIA_NER               | 58.3%  |
-| HarveyNER               | 18.3%  |
-| MultiNERD               | 64.7%  |
-| Ontonotes               | 28.4%  |
-| PolyglotNER             | 45.3%  |
-| TweetNER7               | 35.9%  |
-| WikiANN en              | 53.6%  |
-| WikiNeural              | 73.4%  |
-| bc2gm                   | 63.2%  |
-| bc4chemd                | 56.8%  |
-| bc5cdr                  | 71.3%  |
-| ncbi                    | 64.9%  |
+| ACE 2004                | 32.9%  |
+| ACE 2005                | 30.1%  |
+| AnatEM                  | 39.6%  |
+| Broad Tweet Corpus      | 65.4%  |
+| CoNLL 2003              | 59.8%  |
+| FabNER                  | 26.2%  |
+| FindVehicle             | 30.2%  |
+| GENIA_NER               | 50.0%  |
+| HarveyNER               | 23.9%  |
+| MultiNERD               | 61.7%  |
+| Ontonotes               | 29.6%  |
+| PolyglotNER             | 40.9%  |
+| TweetNER7               | 36.6%  |
+| WikiANN en              | 54.3%  |
+| WikiNeural              | 74.0%  |
+| bc2gm                   | 54.9%  |
+| bc4chemd                | 62.3%  |
+| bc5cdr                  | 73.8%  |
+| ncbi                    | 65.4%  |
 | **Average**             | **48.0%** |
 |                         |        |
-| CrossNER_AI             | 56.7%  |
-| CrossNER_literature     | 61.5%  |
-| CrossNER_music          | 70.2%  |
-| CrossNER_politics       | 75.6%  |
-| CrossNER_science        | 66.8%  |
-| mit-movie               | 39.9%  |
-| mit-restaurant          | 41.7%  |
-| **Average (zero-shot benchmark)** | **58.9%** |
+| CrossNER_AI             | 57.4%  |
+| CrossNER_literature     | 65.9%  |
+| CrossNER_music          | 65.8%  |
+| CrossNER_politics       | 67.5%  |
+| CrossNER_science        | 66.3%  |
+| mit-movie               | 46.7%  |
+| mit-restaurant          | 32.6%  |
+| **Average (zero-shot benchmark)** | **58.5%** |
 
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