README.md

---
inference: false
language:
- nl
metrics:
- sari
- bleu
pipeline_tag: text2text-generation
tags:
- sentence_simplification
- simplification
- text2text
---

## Model Details

# simplify_dutch

This is the source code for my thesis on "Controllable Sentence Simplification in Dutch" 
in the Masters of AI at KU Leuven. The full code can be found at: https://github.com/tsei902/simplify_dutch

# Data
The origin of the datasets in resources/datasets is: 
1) Wikilarge, available under: https://github.com/XingxingZhang/dress
The wikilarge data is limited the first 10000 rows. 

2) ASSET, available under: https://github.com/facebookresearch
Which both have been translated to Dutch. 

# Model
The Dutch T5 model t5-base-dutch from Hugging Face has been adopted and trained on the task 
of sentence simplification. 
The folder /saved model contains the final trained model on 10000 rows of data, as stated in the Thesis. 

# Sequence: 
1) TRAINING DATA needs preprocessing with preprocessor.py
2) Generation can be done with generate_on_pretrained.py with a prior adjustment of 
3) The generation parameters in model.simplify() where the decoding method needs to be chosen (Greedy decoding, Top-p & top-k, or Beam search)
4) Manual scoring of a generated text is possible with evaluate.py

# Further remarks: 
1) The folder resources/processed data contains the training set with the prepended control tokens
2) The folder resources/DUMPS contains the Word embeddings from Fares et al. (2017) have been used. The data is available under: http://vectors.nlpl.eu/repository. (Fares, M., Kutuzov, A., Oepen, S., & Velldal, E. (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources. Proceedings of the 21st Nordic Conference on Computational Linguistics, Gothenburg, Sweden.)
3) The folder resources/outputs/final_decoder_outputs contains the final generated text per decoding strategy (Greedy decoding, Top-p & top-k, or Beam search) for both the full test set and the sample dataset
4) The folder translations contains sampled text (106 and 84 rows) from the original English datasets (WIKILarge and ASSET), a machine-translated version as well as the human translated references. 

# Credits
The preprocessor.py and the utils.py contain code that has been adapted from https://github.com/KimChengSHEANG/TS_T5 (Sheang, K. C., & Saggion, H. (2021). Controllable Sentence Simplification with a Unified Text-to-Text Transfer Transformer.INLG 2021 International Conference on Natural Language Generation, Aberdeen, Scotland, UK.)
The preprocessor.py has been adapted to the usage of Dutch.


### Model Description

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- **Developed by:** Theresa Seidl
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** Dutsch
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** https://huggingface.co/yhavinga/t5-base-dutch

### Model Sources [optional]

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- **Repository:** https://github.com/tsei902/simplify_dutch
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

#
## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

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#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Metrics

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### Results

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#### Summary



### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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## Citation [optional]

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