Instructions to use dicta-il/dictabert-large-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dicta-il/dictabert-large-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="dicta-il/dictabert-large-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("dicta-il/dictabert-large-ner") model = AutoModelForTokenClassification.from_pretrained("dicta-il/dictabert-large-ner") - Notebooks
- Google Colab
- Kaggle
Quality difference in NER between dicta-il/dictabert-large-ner and dicta-il/dictabert-joint
#1
by MaayanHive - opened
Hi,
Will using this model yield better results over the joint model?
We wonder if we should consider using only the lex of dictabert-joint and discard its NER.
Thanks
Hi Maayan,
You can read more about the model and it's scores here: https://arxiv.org/abs/2403.06970
The dictabert-large models give a performance boost and yield better results than the dictabert-base models. This dedicated NER model performs marginally better than the joint model - while the joint model has the advantage of performing all the tagging in one go.