---
license: apache-2.0
language:
- de
pipeline_tag: text-generation
tags:
- german
- deutsch
- simplification
- vereinfachung
---
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This model was used in our experiments in our paper: [Elaborative Simplification for German-Language Texts](https://aclanthology.org/2024.sigdial-1.3).
We have uploaded this model for transparency and replicability of our experiments.
If however you are interested in German text simplification in general, we recommend [our more recent model](https://huggingface.co/hiig-piai/simba_best_092024).

We fine-tuned [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) with a set of ca. 2000 newspaper articles which have been simplified by the Austrian Press Agency. 
This model was trained with the standard and the B1 level texts.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** Freya Hewett, Hadi Asghari
- **Model type:** simplification model, text generation
- **Language(s) (NLP):** German
- **License:** Apache 2.0
- **Finetuned from model:** meta-llama/Meta-Llama-3-8B-Instruct

### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** [GermanElabSimplification](https://github.com/fhewett/GermanElabSimplification/tree/main)
- **Paper:** [Elaborative Simplification for German-Language Texts](https://aclanthology.org/2024.sigdial-1.3)

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

This model works best for simplifying German-language newspaper articles (news items, not commentaries or editorials). It may work for other types of texts.

### Downstream Use

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
We have fine-tuned using only newspaper articles. We have not yet performed extensive out-of-domain testing, but believe that the model's capabilities could be improved by fine-tuning on more diverse data.

<!-- ### Out-of-Scope Use -->

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

As with most text generation models, the model sometimes produces information that is incorrect. 

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Please check manually that your output text corresponds to the input text, as factual inconsistencies may have arisen.

## How to Get Started with the Model

To load the model using transformers:

```
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
device = "cuda"
tokenizer = AutoTokenizer.from_pretrained("frhew/sigdial_ft_b1")
model = AutoModelForCausalLM.from_pretrained("frhew/sigdial_ft_b1", torch_dtype=torch.float16).to(device)
```

We used the following prompt at inference to test our model:

```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
Du bist ein hilfreicher Assistent und hilfst dem User, Texte besser zu verstehen.<|eot_id|><|start_header_id|>user<|end_header_id|>
Kannst du bitte den folgenden Text zusammenfassen und sprachlich auf ein B1-Niveau in Deutsch vereinfachen? Schreibe maximal 5 Sätze.
{input_text}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

A sample of the data used to train our model can be found [here](https://github.com/fhewett/apa-rst/tree/main/original_texts).

#### Training Hyperparameters

<!--- **Training regime:** Our training script can be found [here](https://github.com/fhewett/simba/blob/main/models/train_simba.py). -->
<!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

<!-- #### Speeds, Sizes, Times [optional]  -->

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

## Evaluation

The right hand side shows the results of the manual evaluation, done on the outputs from each model for 35 texts. M.P. stands for meaning preservation, S for simplification, C for coherence, F for factuality; the score represents the percentage of *yes* answers.
More details on the evaluation can be found in the paper. For all metrics, higher is better.

| **Model** | **Prompt** | **Test set** | **SARI** | **FRE** | **M.P.** | **S** | **C** | **F** | **Avg.** |
|--------------------|---------------------|-----------------------|------------------------------|-----------------------------|------------------------------|---------------------------|---------------------------|---------------------------|------------------------------|
| Baseline           | Basic        | A2                    | 41.2                         | 59.4                        | .89                 | .38                       | .96             | .84             | .77                |
| FT-A2              | Basic        | A2                    | 44.0               | 70.6               | .49                          | .82              | .56                       | .64                       | .63                          |
| Baseline           | Basic        | B1                    | 42.3                         | 56.8                        | .85                | .4                        | .9               | .9               | .76                 |
| FT-B1              | Basic        | B1                    | 42.4                | 60.0               | .75                          | .55             | .6                        | .75                       | .66                          |


#### Summary 


## Citation

**BibTeX:**

@inproceedings{hewett-etal-2024-elaborative,
    title = "Elaborative Simplification for {G}erman-Language Texts",
    author = "Hewett, Freya  and
      Asghari, Hadi  and
      Stede, Manfred",
    editor = "Kawahara, Tatsuya  and
      Demberg, Vera  and
      Ultes, Stefan  and
      Inoue, Koji  and
      Mehri, Shikib  and
      Howcroft, David  and
      Komatani, Kazunori",
    booktitle = "Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
    month = sep,
    year = "2024",
    address = "Kyoto, Japan",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.sigdial-1.3",
    doi = "10.18653/v1/2024.sigdial-1.3",
    pages = "29--39"}

**APA:**

Freya Hewett, Hadi Asghari, and Manfred Stede. 2024. Elaborative Simplification for German-Language Texts. In Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 29–39, Kyoto, Japan. Association for Computational Linguistics.

## Model Card Contact

frhew