alakxender/bert-base-dv

BERT base for Dhivehi

Pretrained model on Dhivehi language using masked language modeling (MLM).

Tokenizer

The WordPiece tokenizer uses several components:

• Normalization: lowercase and then NFKD unicode normalization.
• Pretokenization: splits by whitespace and punctuation.
• Postprocessing: single sentences are output in format [CLS] sentence A [SEP] and pair sentences in format [CLS] sentence A [SEP] sentence B [SEP].

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 32
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2

Framework versions

• Transformers 4.41.2
• Pytorch 2.3.1+cu121
• Datasets 2.19.2
• Tokenizers 0.19.1