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Model: hiig-ai-lab/simba_best_092024 Source: Original Platform
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README.md
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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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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. 800 newspaper articles which have been simplified by the Austrian Press Agency.
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Our aim was to have a model which can simplify German-language text.
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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:** Members of the [Public Interest AI research group](https://publicinterest.ai/), [HIIG Berlin](https://www.hiig.de/)
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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:** https://github.com/fhewett/simba
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<!-- - **Paper [optional]:** [More Information Needed] -->
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- **Project website:** https://publicinterest.ai/tool/simba
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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. Contact us if you have a dataset which you think could work (parallel texts, German standard & German simplified).
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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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We offer two tools to interact with our model: an online app and a browser extension. They can be viewed and used [here](https://publicinterest.ai/tool/simba?lang=en).
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Alternatively, 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("hiig-piai/simba_best_092024")
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model = AutoModelForCausalLM.from_pretrained("hiig-piai/simba_best_092024", 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 A2-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). <!--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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<!-- This section describes the evaluation protocols and provides the results. -->
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#### Summary
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<!-- ## Citation [optional]
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]-->
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## Model Card Contact
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simba -at- hiig.de
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