--- 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:** - [GitHub Repo](https://github.com/oliverguhr/fullstop-deep-punctuation-prediction) - [Associated Paper](https://www.researchgate.net/profile/Oliver-Guhr/publication/355038679_FullStop_Multilingual_Deep_Models_for_Punctuation_Prediction/links/615a0ce3a6fae644fbd08724/FullStop-Multilingual-Deep-Models-for-Punctuation-Prediction.pdf) # 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)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). 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](https://www.researchgate.net/profile/Oliver-Guhr/publication/355038679_FullStop_Multilingual_Deep_Models_for_Punctuation_Prediction/links/615a0ce3a6fae644fbd08724/FullStop-Multilingual-Deep-Models-for-Punctuation-Prediction.pdf): > 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](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **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:** ```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 ```python from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("oliverguhr/fullstop-punctuation-multilingual-base") model = AutoModelForTokenClassification.from_pretrained("oliverguhr/fullstop-punctuation-multilingual-base") ```