| license: mit | |
| tags: | |
| - generated_from_trainer | |
| base_model: roberta-base | |
| 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 [Wdowiak (2021)](https://arxiv.org/abs/2110.01938) on low-resource machine translation, it employs a modified version of the encoder block from Wdowiak’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 4.24.7 | |
| - Pytorch 2.0.0+cu118 | |
| - Datasets 2.11.0 | |
| - Tokenizers 0.13.3 | |