README.md

---
language:
- nl
license: cc-by-nc-sa-4.0
tags:
- generated_from_trainer
- simplification
datasets:
- BramVanroy/chatgpt-dutch-simplification
metrics:
- rouge
- sari
task_categories:
- text2text-generation
task_ids:
- text-simplification
widget:
- example_title: Cooking
  text: Op bepaalde tijdstippen verlang ik naar de smaakvolle culinaire creaties welke
    door de ambachtelijke expertise van mijn grootmoeder zijn vervaardigd.
base_model: yhavinga/ul2-large-dutch
model-index:
- name: BramVanroy/ul2-large-dutch-simplification-mai-2023
  results:
  - task:
      type: text-simplification
      name: Text Simplification
    dataset:
      name: ChatGPT Dutch Simplification
      type: BramVanroy/chatgpt-dutch-simplification
    metrics:
    - type: rouge
      value: 41.3871
      name: Eval Rouge-1
    - type: rouge
      value: 19.6751
      name: Eval Rouge-2
    - type: rouge
      value: 36.0469
      name: Eval RougeL
    - type: rouge
      value: 36.1178
      name: Eval RougeLsum
    - type: sari
      value: 54.3588
      name: Eval SARI
    - type: rouge
      value: 43.8191
      name: Test Rouge-1
    - type: rouge
      value: 21.7783
      name: Test Rouge-2
    - type: rouge
      value: 39.3657
      name: Test RougeL
    - type: rouge
      value: 39.3751
      name: Test RougeLsum
    - type: sari
      value: 52.3752
      name: Test SARI
---

# ul2-large-dutch-simplification-mai-2023

This model is intended to simplify Dutch sentences.

This model is a fine-tuned version of [yhavinga/ul2-large-dutch](https://huggingface.co/yhavinga/ul2-large-dutch) on
the [BramVanroy/chatgpt-dutch-simplification](https://huggingface.co/datasets/BramVanroy/chatgpt-dutch-simplification)
dataset. 

The model was created in light of the master thesis of Charlotte Van de Velde in the Master of Science in Artificial
Intelligence (MAI) at KU Leuven in 2023. Charlotte is supervised by Vincent Vandeghinste and Bram Vanroy. 
Dataset creation by Charlotte, model training by Bram.

## Quick links

- [Repository](https://github.com/BramVanroy/mai-simplification-nl-2023#22-hyperparameter-sweep): includes training code and model creation log
- [Dataset](https://huggingface.co/datasets/BramVanroy/chatgpt-dutch-simplification): `BramVanroy/chatgpt-dutch-simplification`
- [Parent model](https://huggingface.co/yhavinga/ul2-large-dutch): this model was finetuned on `yhavinga/ul2-large-dutch`
- [Demo](https://huggingface.co/spaces/BramVanroy/mai-simplification-nl-2023-demo): shows the "base" model in action (don't rely on the "Hosted inference API" widget on this page, it does not work very well)

## Intended uses & limitations, and dataset

The model is intended for sentence-level simplification of Dutch. It might extend to document-level simplification
but most of the dataset is limited to sentences so document-level performance is not guaranteed.

The dataset has been generated automatically (cf.
[dataset description](https://huggingface.co/datasets/BramVanroy/chatgpt-dutch-simplification)) and has not been
manually verified. On top of that, this model has been fine-tuned and we did not scrutinize the parent model or its
training data. Output of the current model is therefore subject to unexpected results (as most if not all neural
networks).

Because the dataset was generated with ChatGPT, this model cannot be used for commercial purposes.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002927210895006501
- train_batch_size: 32
- optimizer: Adafactor
- num_epochs: 27

These hyperarameters were found through Bayesian hyperparameter search with `wandb`. This is described in the
[repository](https://github.com/BramVanroy/mai-simplification-nl-2023#22-hyperparameter-sweep).

### Training results

`eval` results are on the evaluation set, `predict` results are on the test set. These were achieved with
beam search (num_beams=3).

```json
{
    "eval_gen_len": 21.404761904761905,
    "eval_loss": 3.0882697105407715,
    "eval_rouge1": 41.3871,
    "eval_rouge2": 19.6751,
    "eval_rougeL": 36.0469,
    "eval_rougeLsum": 36.1178,
    "eval_sari": 54.3588,
  
    "predict_gen_len": 22.1484375,
    "predict_loss": 2.7822625637054443,
    "predict_rouge1": 43.8191,
    "predict_rouge2": 21.7783,
    "predict_rougeL": 39.3657,
    "predict_rougeLsum": 39.3751,
    "predict_sari": 52.3752
}
```

Note: the model seems to underperform compared to the
[base variant](https://huggingface.co/BramVanroy/ul2-small-dutch-simplification-mai-2023) of the model, achieving only
similar results with a much larger size. The reason for this may be found in the hyperparameters, where
this large model may have benefitted from a smaller learning rate in the optimisation space. In the hyperparameter 
search, the learning rate spectrum was set to 1e-03 to 1e-04 but this might be too large for this model and size. 

### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3