Update README.md

README.md

@@ -78,51 +78,40 @@ model = GLiNER.from_pretrained("knowledgator/gliner-qwen-1.5B-v1.0",
                                                 max_len = 2048).to('cuda:0')
 ```
 
-If you have a large amount of entities and want to pre-embed them, please, refer to the following code snippet:
-
-```python
-labels = ["your entities"]
-texts = ["your texts"]
-
-entity_embeddings = model.encode_labels(labels, batch_size = 8)
-
-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:
 
-| 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%  |
-| **Average**             | **47.0%** |
-|                         |        |
-| 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%** |
+| Dataset                     | Score  |
+|-----------------------------|--------|
+| ACE 2004                    | 29.8%  |
+| ACE 2005                    | 26.8%  |
+| AnatEM                      | 43.7%  |
+| Broad Tweet Corpus          | 68.3%  |
+| CoNLL 2003                  | 67.5%  |
+| FabNER                      | 24.9%  |
+| FindVehicle                 | 33.2%  |
+| GENIA_NER                   | 58.8%  |
+| HarveyNER                   | 19.5%  |
+| MultiNERD                   | 65.1%  |
+| Ontonotes                   | 39.9%  |
+| PolyglotNER                 | 45.8%  |
+| TweetNER7                   | 37.0%  |
+| WikiANN en                  | 56.0%  |
+| WikiNeural                  | 78.3%  |
+| bc2gm                       | 58.1%  |
+| bc4chemd                    | 65.7%  |
+| bc5cdr                      | 72.3%  |
+| ncbi                        | 63.3%  |
+| **Average**                 | **50.2%** |
+|                             |        |
+| CrossNER_AI                 | 58.3%  |
+| CrossNER_literature         | 64.4%  |
+| CrossNER_music              | 71.5%  |
+| CrossNER_politics           | 70.5%  |
+| CrossNER_science            | 65.1%  |
+| mit-movie                   | 47.5%  |
+| mit-restaurant              | 33.1%  |
+| **Average (zero-shot benchmark)** | **58.6%** |
 
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