transformers pytorch mbart text2text-generation text simplification plain language easy-to-read language document simplification de dataset:DEplain/DEplain-APA-doc arxiv:2305.18939 license:apache-2.0 autotrain_compatible region:us
---
datasets:
- DEplain/DEplain-APA-doc
language:
- de
library_name: transformers
license: apache-2.0
metrics:
- sari
- bleu
- bertscore
pipeline_tag: text2text-generation
tags:
- text simplification
- plain language
- easy-to-read language
- document simplification
inference: false
---

# DEplain German Text Simplification

This model belongs to the experiments done at the work of Stodden, Momen, Kallmeyer (2023). ["DEplain: A German Parallel Corpus with Intralingual Translations into Plain Language for Sentence and Document Simplification."](https://arxiv.org/abs/2305.18939) In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Toronto, Canada. Association for Computational Linguistics. 
Detailed documentation can be found on this GitHub repository [https://github.com/rstodden/DEPlain](https://github.com/rstodden/DEPlain)

We reused the codes from [https://github.com/a-rios/ats-models](https://github.com/a-rios/ats-models) to do our experiments.

### Model Description

The model is a finetuned checkpoint of the pre-trained LongmBART model based on `mbart-large-cc25`. With a trimmed vocabulary to the most frequent 30k words in the German language. 

The model was finetuned towards the task of German text simplification of documents.

The finetuning dataset included manually aligned sentences from the datasets `DEplain-APA-doc` only.

### Model Usage

This model can't be used in the HuggingFace interface or via the .from_pretrained method currently. As it's a finetuning of a custom model (LongMBart), which hasn't been registered on HF yet.
You can find this custom model codes at: [https://github.com/a-rios/ats-models](https://github.com/a-rios/ats-models)

To test this model checkpoint, you need to clone the checkpoint repository as follows:

```
  # Make sure you have git-lfs installed (https://git-lfs.com)
  git lfs install
  git clone https://huggingface.co/DEplain/trimmed_longmbart_docs_apa
  
  # if you want to clone without large files – just their pointers
  # prepend your git clone with the following env var:
  GIT_LFS_SKIP_SMUDGE=1
```

Then set up the conda environment via:
```
  conda env create -f environment.yaml
```

Then follow the procedure in the notebook `generation.ipynb`.