Fusion NER Models
Here you can find our NER models:
| model name | model description | model path | datasets | link to dataset | base model |
|---|---|---|---|---|---|
| Basic | Basic training on IAHALT | https://huggingface.co/FusioNER/Basic_IAHALT | IAHALT | FusioNER/Basic | HeRo |
| Vitaly | Vitaly training on IAHALT (with BI-BI problem[3]) | https://huggingface.co/FusioNER/Vitaly_NER | IAHALT | FusioNER/Vitaly | HeRo |
| Name-Sentences | Training on IAHALT + Name-Sentences[1] | https://huggingface.co/FusioNER/Name-Sentences | IAHALT | FusioNER/Name_Sentences | HeRo |
| Entity-Injection | Training on IAHALT + Entity-Injection[2] | https://huggingface.co/FusioNER/Entity-Injection | IAHALT | FusioNER/Entity_Injection | HeRo |
| Smart_Injection | Training on IAHALT + Name-Sentences[1] + Entity-Injection[2] | https://huggingface.co/FusioNER/Smart_Injection | IAHALT | FusioNER/Smart_Injection | HeRo |
| NEMO | Basic training on NEMO dataset | https://huggingface.co/FusioNER/Nemo | NEMO | FusioNER/NEMO | HeRo |
| IAHALT_and_NEMO | Basic training on IAHALT + NEMO | https://huggingface.co/FusioNER/IAHALT_and_NEMO | IAHALT + NEMO | FusioNER/IAHALT_and_NEMO | HeRo |
| IAHALT_and_NEMO_PP | Training on IAHALT + NEMO + Name-Sentences[1] + Entity-Injection[2] | https://huggingface.co/FusioNER/IAHALT_and_NEMO_and_PP | IAHALT + NEMO | FusioNER/IAHALT_and_NEMO_PP | HeRo |
| Animals | Training on IAHALT + Entity-Injection[2] (of animals names as PER entities) | https://huggingface.co/FusioNER/Animals | IAHALT | FusioNER/Animals | HeRo |
| PRS-Injection | Training on IAHALT + Entity-Injection[2] (of PRS names as PER entities) | https://huggingface.co/FusioNER/PRS-Injection | IAHALT | FusioNER/PRS_locations | HeRo |
| DICTA_basic | Training the DICTA model on the basic IAHALT dataset | https://huggingface.co/FusioNER/DICTA_basic | IAHALT | FusioNER/Basic | DICTA |
| DICTA_smart | Training the DICTA model on IAHALT + Name-Sentences[1] + Entity-Injection[2]] dataset | https://huggingface.co/FusioNER/DICTA_Smart | IAHALT | FusioNER/Smart_Injection | DICTA |
[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.
[3] BI-BI Problem: Building training corpus when entities from the same type appear in sequence, labeled as continuations of one another.
For example, the text "讛讗专讬 驻讜讟专 讜专讜谉 讜讜讬讝诇讬" would tagged as SINGLE entity. That problem prevent the model to extract entities correctly.
Hebrew NLP models
You can find in the table Hebrew NLP models:
| Model name | Link | Creator |
|---|---|---|
| HeNLP/HeRo | https://huggingface.co/HeNLP/HeRo | Vitaly Shalumov and Harel Haskey |
| dicta-il/dictabert | https://huggingface.co/dicta-il/dictabert | Shaltiel Shmidman and Avi Shmidman and Moshe Koppel |
| dicta-il/dictabert-large | https://huggingface.co/dicta-il/dictabert-large | Shaltiel Shmidman and Avi Shmidman and Moshe Koppel |
| avichr/heBERT | https://huggingface.co/avichr/heBERT | Avihay Chriqui and Inbal Yahav |
MIT License