In addition to recognizing Cyrillic characters and punctuation, this tokenizer is aware of special tokens such as <s>, </s>, <pad>, and <unk>.

As this is a non-standard tokenizer for transformers, load it not via transformers.AutoTokenizer.from_pretrained, but somewhat like this:

python

import charactertokenizer

...
tokenizer = charactertokenizer.CharacterTokenizer.from_pretrained('igorktech/CharPicoSatirik-m')

To observe tokenization, use this code snippet:

python

prompt = '<s>Hello World\n'
encoded_prompt = tokenizer.encode(prompt, return_tensors='pt')
print('Tokenized prompt:', ' | '.join(tokenizer.decode([t]) for t in encoded_prompt[0]))

You will see a list of tokens separated by the | symbol:

Tokenized prompt: <s> | H | e | l | l | o | | W | o | r | l | d |

Tokenizer created by Koziev.

Model description

Llama2 architecture based.

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0005
train_batch_size: 4
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 250
num_epochs: 2
mixed_precision_training: Native AMP

Training results

Framework versions

Transformers 4.36.0.dev0
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0