Fusion NER Models

Here you can find NER models for Fusion project!

Table of content:

1. NER-Models
2. Results
3. Hebrew NLP models
4. Footnotes

NER Models:

Here you can find a description on each of our models. Each row contains the model nickname, training description, model path (LINK), source dataset (with LINK), base model and entity types.

Results

We test our models on the IAHALT test set. We also check another models, such as DictaBert and HeBert. This is the performence results:

According to the results, we recommend to use DICTA_Small_Smart model.

Hebrew NLP models

You can find in the table Hebrew NLP models:

Footnotes

[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 "הארי פוטר ורון וויזלי" would tagged as SINGLE entity. That problem prevent the model to extract entities correctly.

[4] Classic:

The classic NER types:

MIT License