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whooray
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koen_punctuation

Token Classification
Transformers
Safetensors
new
Generated from Trainer
custom_code
Model card Files Community
koen_punctuation
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---
library_name: transformers
license: apache-2.0
base_model: Alibaba-NLP/gte-multilingual-mlm-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: koen_punctuation
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# koen_punctuation

This model is a fine-tuned version of [Alibaba-NLP/gte-multilingual-mlm-base](https://huggingface.co/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
```bash
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("μ•ˆλ…•ν•˜μ„Έμš”")
#'μ•ˆλ…•ν•˜μ„Έμš”.'
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