language:
- en
- de
- fr
- it
- nl
tags:
- punctuation prediction
- punctuation
datasets: wmt/europarl
license: mit
widget:
- text: Ho sentito che ti sei laureata il che mi fa molto piacere
example_title: Italian
- text: Tous les matins vers quatre heures mon père ouvrait la porte de ma chambre
example_title: French
- text: Ist das eine Frage Frau Müller
example_title: German
- text: My name is Clara and I live in Berkeley California
example_title: English
metrics:
- f1
Model Card for fullstop-punctuation-multilingual-base
Model Details
Model Description
The goal of this task consists in training NLP models that can predict the end of sentence (EOS) and punctuation marks on automatically generated or transcribed texts.
- Developed by: Oliver Guhr
- Shared by [Optional]: Oliver Guhr
- Model type: Token Classification
- Language(s) (NLP): English, German, French, Italian, Dutch
- License: MIT
- Parent Model: xlm-roberta-base
- Resources for more information:
Uses
Direct Use
This model can be used for the task of Token Classification
Downstream Use [Optional]
More information needed.
Out-of-Scope Use
The model should not be used to intentionally create hostile or alienating environments for people.
Bias, Risks, and Limitations
Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
Training Details
Training Data
The model authors note in the associated paper:
The task consists in predicting EOS and punctua- tion marks on unpunctuated lowercased text. The organizers of the SeppNLG shared task provided 470 MB of English, German, French, and Italian text. This data set consists of a training and a de- velopment set.
Training Procedure
Preprocessing
More information needed
Speeds, Sizes, Times
More information needed
Evaluation
Testing Data, Factors & Metrics
Testing Data
More information needed
Factors
More information needed
Metrics
More information needed
Results
Classification report over all languages
precision recall f1-score support
0 0.99 0.99 0.99 47903344
. 0.94 0.95 0.95 2798780
, 0.85 0.84 0.85 3451618
? 0.88 0.85 0.87 88876
- 0.61 0.32 0.42 157863
: 0.72 0.52 0.60 103789
accuracy 0.98 54504270
macro avg 0.83 0.75 0.78 54504270
weighted avg 0.98 0.98 0.98 54504270
Model Examination
More information needed
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: More information needed
- Hours used: More information needed
- Cloud Provider: More information needed
- Compute Region: More information needed
- Carbon Emitted: More information needed
Technical Specifications [optional]
Model Architecture and Objective
More information needed
Compute Infrastructure
More information needed
Hardware
More information needed
Software
More information needed.
Citation
BibTeX:
@article{guhr-EtAl:2021:fullstop,
title={FullStop: Multilingual Deep Models for Punctuation Prediction},
author = {Guhr, Oliver and Schumann, Anne-Kathrin and Bahrmann, Frank and Böhme, Hans Joachim},
booktitle = {Proceedings of the Swiss Text Analytics Conference 2021},
month = {June},
year = {2021},
address = {Winterthur, Switzerland},
publisher = {CEUR Workshop Proceedings},
url = {http://ceur-ws.org/Vol-2957/sepp_paper4.pdf}
}
Glossary [optional]
More information needed
More Information [optional]
More information needed
Model Card Authors [optional]
Oliver Guhr in collaboration with Ezi Ozoani and the Hugging Face team
Model Card Contact
More information needed
How to Get Started with the Model
Use the code below to get started with the model.
Click to expand
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("oliverguhr/fullstop-punctuation-multilingual-base")
model = AutoModelForTokenClassification.from_pretrained("oliverguhr/fullstop-punctuation-multilingual-base")