Update README.md

README.md

@@ -19,7 +19,6 @@ Here you can find our NER models:
 | **DICTA_smart** | Training the [DICTA](https://huggingface.co/dicta-il/dictabert) model on IAHALT + Name-Sentences[1] + Entity-Injection[2]] [dataset](https://huggingface.co/datasets/FusioNER/Smart_Injection) | [https://huggingface.co/FusioNER/DICTA_Smart](https://huggingface.co/FusioNER/DICTA_Smart) | IAHALT | [FusioNER/Smart_Injection](https://huggingface.co/datasets/FusioNER/Smart_Injection) | [DICTA](https://huggingface.co/dicta-il/dictabert) |
 
 
-
 [1] **Name-Sentences**: Adding to the corpus sentences that contain only the entity we want the network to learn.
 
 [2] **Entity-Injection**: Replace a tagged entity in the original corpus with a new entity. By using, this method, the model can learn new entities (not labels!) which the model not extracted before.
@@ -28,5 +27,11 @@ 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.
 
+# Hebrew NLP models
+| Model name | Link | Creator |
+|:-----------|:-----|:--------|
+| HeNLP/HeRo | [https://huggingface.co/HeNLP/HeRo](HeNLP/HeRo) | Vitaly Shalumov and Harel Haskey |
+| dicta-il/dictabert | [https://huggingface.co/dicta-il/dictabert](https://huggingface.co/dicta-il/dictabert) | Shaltiel Shmidman and Avi Shmidman and Moshe Koppel |
+| avichr/heBERT | [https://huggingface.co/avichr/heBERT](https://huggingface.co/avichr/heBERT) | Avihay Chriqui and Inbal Yahav |
 
 **MIT License**