readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: BERiT
    results: []
  ---
  
  
  # BERiT
  
  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the [Tanakh dataset](https://huggingface.co/datasets/gngpostalsrvc/Tanakh).
  It achieves the following results on the evaluation set:
  - Loss: 3.9931
  
  ## Model description
  
  BERiT is a masked-language model for Biblical Hebrew, a low-resource ancient language preserved primarily in the text of the Hebrew Bible. Building on the work of [Sennrich and Zhang (2019)](https://arxiv.org/abs/1905.11901) and [Wodiak (2021)](https://arxiv.org/abs/2110.01938) on low-resource machine translation, it employs a modified version of the encoder block from Wodiak’s Seq2Seq model. Accordingly, BERiT is much smaller than models designed for modern languages like English. It features a single attention block with four attention heads, smaller embedding and feedforward dimensions (256 and 1024), a smaller max input length (128), and an aggressive dropout rate (.5) at both the attention and feedforward layers. 
  
  The BERiT tokenizer performs character level byte-pair encoding using a 2000 word base vocabulary, which has been enriched with common grammatical morphemes.  
  
  ## How to Use
  
  ```
  from transformers import RobertaModel, RobertaTokenizerFast
  
  BERiT_tokenizer = RobertaTokenizerFast.from_pretrained('gngpostalsrvc/BERiT')
  BERiT = RobertaModel.from_pretrained('gngpostalsrvc/BERiT')
  ```
  
  ## Training procedure
  
  BERiT was trained on the Tanakh dataset for 150 epochs using a Tesla T4 GPU. Further training did not yield significant improvements in performance. 
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0005
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 150
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.0
  - Tokenizers 0.13.2
  