koen_punctuation

This model is a fine-tuned version of Alibaba-NLP/gte-multilingual-mlm-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0192
  • Accuracy: 0.9797
  • Precision O: 0.9916
  • Recall O: 0.9917
  • F1 O: 0.9917
  • Precision Comma: 0.8204
  • Recall Comma: 0.8329
  • F1 Comma: 0.8266
  • Precision Period: 0.9246
  • Recall Period: 0.9186
  • F1 Period: 0.9216
  • Precision Question: 0.8395
  • Recall Question: 0.8254
  • F1 Question: 0.8324
  • Precision Exclamation: 1.0
  • Recall Exclamation: 0.3846
  • F1 Exclamation: 0.5556
  • Precision Macro: 0.9152
  • Recall Macro: 0.7906
  • F1 Macro: 0.8256

Model description

Punctuation restoration for spoken language.

Install & Usage

pip install spokentxt-punctuation-restoration
from spokentxt_punctuation_restoration import PunctuationModel

model = PunctuationModel(model_name = "whooray/koen_punctuation", device = "cpu") # device = cuda:0
model("์•ˆ๋…•ํ•˜์„ธ์š”")
#'์•ˆ๋…•ํ•˜์„ธ์š”.'
model("Hello how are you")
#'Hello, how are you?'

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: 5e-05
  • train_batch_size: 32
  • eval_batch_size: 16
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • total_train_batch_size: 128
  • total_eval_batch_size: 64
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 100
  • num_epochs: 3

Framework versions

  • Transformers 4.49.0.dev0
  • Pytorch 2.6.0+cu124
  • Datasets 3.3.0
  • Tokenizers 0.21.0
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