model documentation

README.md

@@ -5,9 +5,11 @@ language:
 - fr
 - it
 - nl
+
 tags:
 - punctuation prediction
 - punctuation
+
 datasets: wmt/europarl
 license: mit
 widget:
@@ -18,14 +20,103 @@ widget:
 - text: "Ist das eine Frage Frau Müller"
   example_title: "German"
 - text: "My name is Clara and I live in Berkeley California"
-  example_title: "English"  
+  example_title: "English" 
+
 metrics:
 - f1
 ---
 
-# Work in progress
 
-## Classification report over all languages 
+# 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
 
@@ -39,4 +130,94 @@ metrics:
     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>
+