

# ul2-small-dutch-simplification-mai-2023

This model is intended to simplify Dutch sentences.

This model is a fine-tuned version of [yhavinga/ul2-small-dutch](https://huggingface.co/yhavinga/ul2-small-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-small-dutch): this model was finetuned on `yhavinga/ul2-small-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: 0.0006370158604635734
- train_batch_size: 20
- optimizer: Adafactor
- num_epochs: 37

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": 21.555555555555557,
    "eval_loss": 3.2290523052215576,
    "eval_rouge1": 40.9663,
    "eval_rouge2": 18.499,
    "eval_rougeL": 34.9342,
    "eval_rougeLsum": 34.9752,
    "eval_sari": 52.4509,
  
    "predict_gen_len": 21.796875,
    "predict_loss": 3.063812494277954,
    "predict_rouge1": 39.6138,
    "predict_rouge2": 17.1242,
    "predict_rougeL": 35.4629,
    "predict_rougeLsum": 35.3679,
    "predict_sari": 51.7538
}
```


### Framework versions

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

{"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."}], "model-index": [{"name": "BramVanroy/ul2-small-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": 40.9663, "name": "Eval Rouge-1"}, {"type": "rouge", "value": 18.499, "name": "Eval Rouge-2"}, {"type": "rouge", "value": 34.9342, "name": "Eval RougeL"}, {"type": "rouge", "value": 34.9752, "name": "Eval RougeLsum"}, {"type": "sari", "value": 52.4509, "name": "Eval SARI"}, {"type": "rouge", "value": 39.6138, "name": "Test Rouge-1"}, {"type": "rouge", "value": 17.1242, "name": "Test Rouge-2"}, {"type": "rouge", "value": 35.4629, "name": "Test RougeL"}, {"type": "rouge", "value": 35.3679, "name": "Test RougeLsum"}, {"type": "sari", "value": 51.7538, "name": "Test SARI"}]}]}]}
transformers pytorch safetensors t5 text2text-generation generated_from_trainer simplification nl dataset:BramVanroy/chatgpt-dutch-simplification license:cc-by-nc-sa-4.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #generated_from_trainer #simplification #nl #dataset-BramVanroy/chatgpt-dutch-simplification #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 



# ul2-small-dutch-simplification-mai-2023

This model is intended to simplify Dutch sentences.

This model is a fine-tuned version of yhavinga/ul2-small-dutch on
the 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: includes training code and model creation log
- Dataset: 'BramVanroy/chatgpt-dutch-simplification'
- Parent model: this model was finetuned on 'yhavinga/ul2-small-dutch'
- 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) 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: 0.0006370158604635734
- train_batch_size: 20
- optimizer: Adafactor
- num_epochs: 37

These hyperarameters were found through Bayesian hyperparameter search with 'wandb'. This is described in the
repository.

### 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).




### Framework versions

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

# ul2-small-dutch-simplification-mai-2023

This model is intended to simplify Dutch sentences.

This model is a fine-tuned version of yhavinga/ul2-small-dutch on
the 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: includes training code and model creation log
- Dataset: 'BramVanroy/chatgpt-dutch-simplification'
- Parent model: this model was finetuned on 'yhavinga/ul2-small-dutch'
- 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) 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: 0.0006370158604635734
- train_batch_size: 20
- optimizer: Adafactor
- num_epochs: 37

These hyperarameters were found through Bayesian hyperparameter search with 'wandb'. This is described in the
repository. ### 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). ### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #generated_from_trainer #simplification #nl #dataset-BramVanroy/chatgpt-dutch-simplification #license-cc-by-nc-sa-4.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # ul2-small-dutch-simplification-mai-2023

This model is intended to simplify Dutch sentences.

This model is a fine-tuned version of yhavinga/ul2-small-dutch on
the 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: includes training code and model creation log
- Dataset: 'BramVanroy/chatgpt-dutch-simplification'
- Parent model: this model was finetuned on 'yhavinga/ul2-small-dutch'
- 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) 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: 0.0006370158604635734
- train_batch_size: 20
- optimizer: Adafactor
- num_epochs: 37

These hyperarameters were found through Bayesian hyperparameter search with 'wandb'. This is described in the
repository. ### 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). ### Framework versions

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