README.md

---
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.
 
<details>
<summary> Click to expand </summary>

```python
 from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("oliverguhr/fullstop-punctuation-multilingual-base")

model = AutoModelForTokenClassification.from_pretrained("oliverguhr/fullstop-punctuation-multilingual-base")
 ```
</details>