Update README.md

README.md

@@ -29,6 +29,28 @@ Here you can find our NER models:
 
 For example, the text "הארי פוטר ורון וויזלי" would tagged as **SINGLE** entity. That problem prevent the model to extract entities correctly.
 
+# Results
+We test our models on the **IAHALT test set**. We also check another models, such as [DictaBert](https://huggingface.co/dicta-il/dictabert) and [HeBert](https://huggingface.co/avichr/heBERT). This is the performence results:
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+| name | precision | recall | f1 | time |
+| :---: | :---: | :---: | :---: | :---: |
+| [**IAHALT_and_NEMO_PP**](https://huggingface.co/FusioNER/IAHALT_and_NEMO_and_PP) | 0.714 | 0.353 | 0.461 | 83.128 |
+| [**HeBert**](https://huggingface.co/avichr/heBERT) | 0.574 | 0.474 | 0.494 | 86.483 |
+| [**NEMO**](https://huggingface.co/FusioNER/Nemo) | 0.553 | 0.51 | 0.525 | 81.422 |
+| **IAHALT_and_NEMO**](https://huggingface.co/FusioNER/IAHALT_and_NEMO) | 0.692 | 0.678 | 0.684 | 83.702 |
+| [**Vitaly**](https://huggingface.co/FusioNER/Vitaly_NER) | 0.883 | 0.794 | 0.836 | 83.773 |
+| [**DictaBert**](https://huggingface.co/dicta-il/dictabert) | 0.916 | 0.834 | 0.872 | 70.465 |
+| [**DICTA_large**](https://huggingface.co/dicta-il/dictabert-large) | 0.917 | 0.845 | 0.879 | 206.251 |
+| [**Name-Sentences**](https://huggingface.co/FusioNER/Name-Sentences) | 0.895 | 0.865 | 0.879 | 82.674 |
+| [**Basic**](FusioNER/Basic_IAHALT) | 0.897 | 0.866 | 0.881 | 84.479 |
+| [**Smart_Injection**](https://huggingface.co/FusioNER/Smart_Injection) | 0.898 | 0.867 | 0.881 | 82.253 |
+| [**DICTA_Basic**](https://huggingface.co/FusioNER/Dicta_Small_Basic) | 0.903 | 0.875 | 0.888 | 69.419 |
+| [**DICTA_Large_Smart**](https://huggingface.co/FusioNER/Dicta_Large_Smart) | 0.904 | 0.875 | 0.889 | 204.324 |
+| [**DICTA_Small_Smart**](https://huggingface.co/FusioNER/Dicta_Small_Smart) | 0.904 | 0.875 | 0.889 | 70.29 |
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 # Hebrew NLP models
 You can find in the table Hebrew NLP models: