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bert-base-dv – AI Model by alakxender | AlphaNeural AI
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bert-base-dv
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transformers
safetensors
bert
fill-mask
dv
alakxender/haveeru-articles
autotrain_compatible
endpoints_compatible
us
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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
