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whooray/
koen_punctuation

Name: koen_punctuation
Availability: InStock

generated_from_trainer

custom_code

Alibaba-NLP/gte-multilingual-mlm-base

finetune

apache-2.0

autotrain_compatible

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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

python

from spokentxt_punctuation_restoration import PunctuationModel

model = PunctuationModel(model_name = "whooray/koen_punctuation", device = "cpu") # device = cuda:0
model("안녕하세요")
#'안녕하세요.'
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