inference: false
license: apache-2.0
language:
- de
datasets:
- DEplain/DEplain-APA-doc
metrics:
- sari
- bleu
- bertscore
library_name: transformers
pipeline_tag: text2text-generation
tags:
- text simplification
- plain language
- easy-to-read language
- document simplification
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." 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
We reused the codes from 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.
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.