hiig-ai-lab/simba_best_092024

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We fine-tuned meta-llama/Meta-Llama-3-8B-Instruct with a set of ca. 800 newspaper articles which have been simplified by the Austrian Press Agency. Our aim was to have a model which can simplify German-language text.

Model Details

Model Description

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• —Developed by: Members of the Public Interest AI research group, HIIG Berlin
• —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

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• —Repository: https://github.com/fhewett/simba <!-- - Paper [optional]: [More Information Needed] -->
• —Project website: https://publicinterest.ai/tool/simba

Uses

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Direct Use

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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.

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. Contact us if you have a dataset which you think could work (parallel texts, German standard & German simplified).

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Bias, Risks, and Limitations

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

Recommendations

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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

We offer two tools to interact with our model: an online app and a browser extension. They can be viewed and used here.

Alternatively, to load the model using transformers:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
device = "cuda"
tokenizer = AutoTokenizer.from_pretrained("hiig-piai/simba_best_092024")
model = AutoModelForCausalLM.from_pretrained("hiig-piai/simba_best_092024", 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 A2-Niveau in Deutsch vereinfachen? Schreibe maximal 5 Sätze.
{input_text}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Training Details

Training Data

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A sample of the data used to train our model can be found here.

Training Hyperparameters

• —Training regime: Our training script can be found here. <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

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Evaluation

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Summary

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Model Card Contact

simba -at- hiig.de