Large Pre-Trained Models with Extra-Large Vocabularies: A Contrastive Analysis of Hebrew BERT Models and a New One to Outperform Them All
Paper • 2211.15199 • Published
How to use dicta-il/alephbertgimmel-small with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("fill-mask", model="dicta-il/alephbertgimmel-small") # Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("dicta-il/alephbertgimmel-small")
model = AutoModelForMaskedLM.from_pretrained("dicta-il/alephbertgimmel-small", device_map="auto")YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
AlephBertGimmel - Modern Hebrew pretrained BERT model with a 128K token vocabulary.
NOTE: This model was only trained with sequences of up to 128 tokens.
When using AlephBertGimmel, please reference:
Eylon Guetta, Avi Shmidman, Shaltiel Shmidman, Cheyn Shmuel Shmidman, Joshua Guedalia, Moshe Koppel, Dan Bareket, Amit Seker and Reut Tsarfaty, "Large Pre-Trained Models with Extra-Large Vocabularies: A Contrastive Analysis of Hebrew BERT Models and a New One to Outperform Them All", Nov 2022 arXiv:2211.15199