README.md

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 Google's BERT architecture (Devlin et al. 2018).

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

1. OSCAR (Ortiz, 2019) Hebrew section (10 GB text, 20 million sentences).
2. Hebrew dump of Wikipedia (650 MB text, 3 million sentences).
3. Hebrew Tweets collected from the Twitter sample stream (7 GB text, 70 million 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:

1. num tokens < 32 (70M sentences)
2. 32 <= num tokens < 64 (12M sentences)
3. 64 <= num tokens < 128 (10M sentences)
4. 128 <= num tokens < 512 (1.5M sentences)

Each section was first trained for 5 epochs with an initial learning rate set to 1e-4. Then each section 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.