metadata
language:
- he
tags:
- language model
license: apache-2.0
datasets:
- oscar
- wikipedia
- twitter
AlephBERT
Hebrew Language Model
State-of-the-art language model for Hebrew. Based on BERT.
How to use
from transformers import BertModel, BertTokenizerFast
alephbert_tokenizer = BertTokenizerFast.from_pretrained('onlplab/alephbert-base')
alephbert = BertModel.from_pretrained('onlplab/alephbert-base')
# if not finetuning - disable dropout
alephbert.eval()
Training data
- OSCAR (10G text, 20M sentences)
- Wikipedia dump (0.6G text, 3M sentences)
- Tweets (7G text, 70M sentences)
Training procedure
Trained on a DGX machine (8 V100 GPUs) using the standard huggingface training procedure.
To optimize training time we split the data into 4 sections based on max number of tokens:
- num tokens < 32 (70M sentences)
- 32 <= num tokens < 64 (12M sentences)
- 64 <= num tokens < 128 (10M sentences)
- 128 <= num tokens < 512 (70M sentences)
Each section was first trained for 5 epochs with an initial learning rate set to 1e-4. Then each second was trained for another 5 epochs with an initial learning rate set to 1e-5, for a total of 10 epochs.
Total training time was 8 days.