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https://github.com/proycon/deepfrog

An NLP-suite powered by deep learning
https://github.com/proycon/deepfrog

deep-learning deep-neural-networks dutch folia frog nlp transformers

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An NLP-suite powered by deep learning

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README 

        


  




# DeepFrog - NLP Suite



[![Language Machines Badge](http://applejack.science.ru.nl/lamabadge.php/deepfrog)](http://applejack.science.ru.nl/languagemachines/)

[![Project Status: Suspended -  Initial development has started, but there has not yet been a stable, usable release; work has been stopped for the time being but the author(s) intend on resuming work.](https://www.repostatus.org/badges/latest/suspended.svg)](https://www.repostatus.org/#suspended)



## Introduction



DeepFrog aims to be a (partial) successor of the Dutch-NLP suite [Frog](https://languagemachines.github.io/frog).

Whereas the various NLP modules in Frog wre built on k-NN classifiers, DeepFrog builds on deep learning techniques and

can use a variety of neural transformers.



Our deliverables are multi-faceted:



1. Fine-tuned neural network **models** for Dutch NLP that can be compared with [Frog](https://languagemachines.github.io/frog) and are

    directly usable with [Huggingface's Transformers](https://github.com/huggingface/transformers) library for Python (or

    [rust-bert](https://github.com/guillaume-be/rust-bert) for Rust).

2. **Training pipelines** for the above models (see ``training``).

3. A **software tool** that integrates multiple models (not just limited to dutch!) and provides a single pipeline solution for end-users.

    * with full support for [FoLiA XML](https://proycon.github.io/folia) input/output.

    * usage is not limited to the models we provide



## Installation



DeepFrog and all its dependencies are included as an extra in [LaMachine](https://proycon.github.io/LaMachine), which is the easiest

way to install it. Within lamachine, do:



  ``lamachine-add deepfrog && lamachine-update``



Otherwise, simply install DeepFrog using Rust's package manager:



```

cargo install deepfrog

```



No cargo/rust on your system yet? Do ``sudo apt install cargo`` on Debian/ubuntu based systems, ``brew install rust`` on mac, or use [rustup](https://rustup.rs/).



In order to run the DeepFrog command-line-tool, you need to have the C++ library [libtorch](https://pytorch.org/) installed.

Download it from https://pytorch.org/ ; make sure you select *package: libtorch* there! You don't need the rest of

pytorch.



To use our models directly with the [Huggingface's Transformers](https://github.com/huggingface/transformers) library

for Python, you merely need that library, models will be automatically downloaded and installed as you invoke them. The

DeepFrog command-line-tool is not used in this workflow.



## Models



We aim to make available various models for Dutch NLP.



#### RobBERT v1 Part-of-Speech (CGN tagset) for Dutch



Model page with instructions: https://huggingface.co/proycon/robbert-pos-cased-deepfrog-nld



Uses pre-trained model [RobBERT](https://people.cs.kuleuven.be/~pieter.delobelle/robbert/) (a Roberta model), fine-tuned on

part-of-speech tags with the full corpus as also used by Frog. Uses the tag set of Corpus Gesproken Nederlands (CGN), this

corpus constitutes a subset of the training data.



Test Evaluation:



```

f1 = 0.9708171206225681

loss = 0.07882563415198372

precision = 0.9708171206225681

recall = 0.9708171206225681

```



#### RobBERT v2 Part-of-Speech (CGN tagset) for Dutch



Model page with instructions: https://huggingface.co/proycon/robbert2-pos-cased-deepfrog-nld



Uses pre-trained model [RobBERT v2](https://people.cs.kuleuven.be/~pieter.delobelle/robbert/) (a Roberta model), fine-tuned on

part-of-speech tags with the full corpus as also used by Frog. Uses the tag set of Corpus Gesproken Nederlands (CGN), this

corpus constitutes a subset of the training data.



```

f1 = 0.9664560038891591

loss = 0.09085878504153627

precision = 0.9659863945578231

recall = 0.9669260700389105

```



#### BERT Part-of-Speech (CGN tagset) for Dutch



Model page with instructions: https://huggingface.co/proycon/bert-pos-cased-deepfrog-nld



Uses pre-trained model [BERTje](https://github.com/wietsedv/bertje) (a BERT model), fine-tuned on

part-of-speech tags with the full corpus as also used by Frog. Uses the tag set of Corpus Gesproken Nederlands (CGN), this

corpus constitutes a subset of the training data.



Test Evaluation:



```

f1 = 0.9737354085603113

loss = 0.0647074995296342

precision = 0.9737354085603113

recall = 0.9737354085603113

```



#### RobBERT SoNaR1 Named Entities for Dutch



Model page with instructions: https://huggingface.co/proycon/robbert-ner-cased-sonar1-nld



Uses pre-trained model [RobBERT](https://people.cs.kuleuven.be/~pieter.delobelle/robbert/) (a Roberta model), fine-tuned on

Named Entities from the SoNaR1 corpus (as also used by Frog). Provides basic PER,LOC,ORG,PRO,EVE,MISC tags.



Test Evaluation (**note:** *this is a simple token-based evaluation rather than entity based!*)



```

f1 = 0.9170731707317074

loss = 0.023864904676364467

precision = 0.9306930693069307

recall = 0.9038461538461539

```



**Note:** the tokenisation in this model is English rather than Dutch



#### RobBERT v2 SoNaR1 Named Entities for Dutch



Model page with instructions: https://huggingface.co/proycon/robbert2-ner-cased-sonar1-nld



Uses pre-trained model [RobBERT (v2)](https://people.cs.kuleuven.be/~pieter.delobelle/robbert/) (a Roberta model), fine-tuned on

Named Entities from the SoNaR1 corpus (as also used by Frog). Provides basic PER,LOC,ORG,PRO,EVE,MISC tags.



```

f1 = 0.8878048780487806

loss = 0.03555946223787032

precision = 0.900990099009901

recall = 0.875

```



#### BERT SoNaR1 Named Entities for Dutch



Model page with instructions: https://huggingface.co/proycon/bert-ner-cased-sonar1-nld



Uses pre-trained model [BERTje](https://github.com/wietsedv/bertje) (a BERT model), fine-tuned on

Named Entities from the SoNaR1 corpus (as also used by Frog). Provides basic PER,LOC,ORG,PRO,EVE,MISC tags.



Test Evaluation (**note:** *this is a simple token-based evaluation rather than entity based!*)



```

f1 = 0.9519230769230769

loss = 0.02323892477299803

precision = 0.9519230769230769

recall = 0.9519230769230769

```