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techiaith/fullstop-welsh-punctuation-prediction
fullstop-welsh-punctuation-prediction is a token classification model from techiaith. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.
This model predicts the punctuation of Welsh language texts. It has been created to restore punctuation of transcribed from speech recognition models such as https://huggingface.co/techiaith/wav2vec2-xlsr-ft-cy. The m…
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From the Hugging Face model README
This model predicts the punctuation of Welsh language texts. It has been created to restore punctuation of transcribed from speech recognition models such as https://huggingface.co/techiaith/wav2vec2-xlsr-ft-cy. The model restores the following punctuation markers: "." "," "?" "-" ":"
The model was trained on Welsh texts extracted from the Welsh Parliament / Senedd Record of Proceedings between 1999-2010 and 2016 to the present day. Please note that the training data consists of originally spoken and translated political speeches. Therefore the model might perform differently on texts from other domains.
Based on the work of https://github.com/oliverguhr/fullstop-deep-punctuation-prediction and softcatala/fullstop-catalan-punctuation-prediction
To get started install the deepmultilingualpunctuation package from pypi:
pip install deepmultilingualpunctuation
from deepmultilingualpunctuation import PunctuationModel
model = PunctuationModel("techiaith/fullstop-welsh-punctuation-prediction")
text = "A yw'r gweinidog yn cytuno bod angen gwell gwasanaethau yn ne ddwyrain Cymru"
result = model.restore_punctuation(text)
print(result)
output
[
{
"entity_group": "LABEL_0",
"score": 0.9999812841415405,
"word": "A yw'r gweinidog yn cytuno bod angen gwell gwasanaethau yn",
"start": 0,
"end": 58
},
{
"entity_group": "LABEL_4",
"score": 0.9787278771400452,
"word": "ne",
"start": 59,
"end": 61
},
{
"entity_group": "LABEL_0",
"score": 0.9999902248382568,
"word": "ddwyrain",
"start": 62,
"end": 70
},
{
"entity_group": "LABEL_3",
"score": 0.9484745860099792,
"word": "Cymru",
"start": 71,
"end": 76
}
]
A yw'r gweinidog yn cytuno bod angen gwell gwasanaethau yn ne-ddwyrain Cymru?
The model achieves the following F1 scores for the different punctuation markers:
| Label | Precision | Recall | f1-score | Support |
|---|---|---|---|---|
| 0 | 0.99 | 0.99 | 0.99 | 12124280 |
| . | 0.88 | 0.89 | 0.88 | 455896 |
| , | 0.84 | 0.82 | 0.83 | 771813 |
| ? | 0.92 | 0.88 | 0.90 | 54878 |
| - | 0.95 | 0.94 | 0.95 | 31545 |
| : | 0.91 | 0.87 | 0.89 | 39618 |
| accuracy | 0.98 | 13478030 | ||
| macro avg | 0.91 | 0.90 | 0.91 | 13478030 |
| weighted avg | 0.97 | 0.98 | 0.97 | 13478030 |