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README.md

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+---
+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