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--- |
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license: apache-2.0 |
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language: |
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- de |
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pipeline_tag: text-generation |
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tags: |
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- german |
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- deutsch |
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- simplification |
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- vereinfachung |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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This model was used in our experiments in our paper: [Elaborative Simplification for German-Language Texts](https://aclanthology.org/2024.sigdial-1.3). |
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We have uploaded this model for transparency and replicability of our experiments. |
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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). |
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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. |
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This model was trained with the standard and the B1 level texts. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** Freya Hewett, Hadi Asghari |
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- **Model type:** simplification model, text generation |
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- **Language(s) (NLP):** German |
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- **License:** Apache 2.0 |
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- **Finetuned from model:** meta-llama/Meta-Llama-3-8B-Instruct |
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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [GermanElabSimplification](https://github.com/fhewett/GermanElabSimplification/tree/main) |
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- **Paper:** [Elaborative Simplification for German-Language Texts](https://aclanthology.org/2024.sigdial-1.3) |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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This model works best for simplifying German-language newspaper articles (news items, not commentaries or editorials). It may work for other types of texts. |
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### Downstream Use |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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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. |
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<!-- ### Out-of-Scope Use --> |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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As with most text generation models, the model sometimes produces information that is incorrect. |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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Please check manually that your output text corresponds to the input text, as factual inconsistencies may have arisen. |
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## How to Get Started with the Model |
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To load the model using transformers: |
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``` |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import torch |
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device = "cuda" |
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tokenizer = AutoTokenizer.from_pretrained("frhew/sigdial_ft_b1") |
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model = AutoModelForCausalLM.from_pretrained("frhew/sigdial_ft_b1", torch_dtype=torch.float16).to(device) |
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``` |
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We used the following prompt at inference to test our model: |
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``` |
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<|begin_of_text|><|start_header_id|>system<|end_header_id|> |
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Du bist ein hilfreicher Assistent und hilfst dem User, Texte besser zu verstehen.<|eot_id|><|start_header_id|>user<|end_header_id|> |
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Kannst du bitte den folgenden Text zusammenfassen und sprachlich auf ein B1-Niveau in Deutsch vereinfachen? Schreibe maximal 5 Sätze. |
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{input_text}<|eot_id|><|start_header_id|>assistant<|end_header_id|> |
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``` |
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## Training Details |
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### Training Data |
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<!-- 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. --> |
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A sample of the data used to train our model can be found [here](https://github.com/fhewett/apa-rst/tree/main/original_texts). |
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#### Training Hyperparameters |
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<!--- **Training regime:** Our training script can be found [here](https://github.com/fhewett/simba/blob/main/models/train_simba.py). --> |
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<!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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<!-- #### Speeds, Sizes, Times [optional] --> |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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## Evaluation |
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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. |
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More details on the evaluation can be found in the paper. For all metrics, higher is better. |
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| **Model** | **Prompt** | **Test set** | **SARI** | **FRE** | **M.P.** | **S** | **C** | **F** | **Avg.** | |
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|--------------------|---------------------|-----------------------|------------------------------|-----------------------------|------------------------------|---------------------------|---------------------------|---------------------------|------------------------------| |
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| Baseline | Basic | A2 | 41.2 | 59.4 | .89 | .38 | .96 | .84 | .77 | |
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| FT-A2 | Basic | A2 | 44.0 | 70.6 | .49 | .82 | .56 | .64 | .63 | |
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| Baseline | Basic | B1 | 42.3 | 56.8 | .85 | .4 | .9 | .9 | .76 | |
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| FT-B1 | Basic | B1 | 42.4 | 60.0 | .75 | .55 | .6 | .75 | .66 | |
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#### Summary |
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## Citation |
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**BibTeX:** |
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@inproceedings{hewett-etal-2024-elaborative, |
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title = "Elaborative Simplification for {G}erman-Language Texts", |
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author = "Hewett, Freya and |
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Asghari, Hadi and |
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Stede, Manfred", |
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editor = "Kawahara, Tatsuya and |
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Demberg, Vera and |
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Ultes, Stefan and |
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Inoue, Koji and |
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Mehri, Shikib and |
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Howcroft, David and |
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Komatani, Kazunori", |
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booktitle = "Proceedings of the 25th Annual Meeting of the Special Interest Group on Discourse and Dialogue", |
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month = sep, |
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year = "2024", |
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address = "Kyoto, Japan", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2024.sigdial-1.3", |
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doi = "10.18653/v1/2024.sigdial-1.3", |
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pages = "29--39"} |
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**APA:** |
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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. |
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## Model Card Contact |
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frhew |
