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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: BERiT |
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results: [] |
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--- |
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# BERiT |
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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). |
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It achieves the following results on the evaluation set: |
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- Loss: 3.9931 |
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## Model description |
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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. |
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The BERiT tokenizer performs character level byte-pair encoding using a 2000 word base vocabulary, which has been enriched with common grammatical morphemes. |
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## How to Use |
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``` |
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from transformers import RobertaModel, RobertaTokenizerFast |
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BERiT_tokenizer = RobertaTokenizerFast.from_pretrained('gngpostalsrvc/BERiT') |
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BERiT = RobertaModel.from_pretrained('gngpostalsrvc/BERiT') |
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``` |
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## Training procedure |
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BERiT was trained on the Tanakh dataset for 150 epochs using a Tesla T4 GPU. Further training did not yield significant improvements in performance. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0005 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 150 |
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### Framework versions |
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- Transformers 4.24.7 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.11.0 |
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- Tokenizers 0.13.3 |
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