|
--- |
|
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. --> |
|
|
|
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. |
|
Our aim was to have a model which can simplify German-language text. |
|
|
|
## Model Details |
|
|
|
### Model Description |
|
|
|
<!-- Provide a longer summary of what this model is. --> |
|
|
|
|
|
|
|
- **Developed by:** Members of the [Public Interest AI research group](https://publicinterest.ai/), [HIIG Berlin](https://www.hiig.de/) |
|
- **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:** https://github.com/fhewett/simba |
|
<!-- - **Paper [optional]:** [More Information Needed] --> |
|
- **Project website:** https://publicinterest.ai/tool/simba |
|
|
|
## 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. Contact us if you have a dataset which you think could work (parallel texts, German standard & German simplified). |
|
|
|
<!-- ### 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 |
|
|
|
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). |
|
|
|
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 |
|
|
|
<!-- 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 |
|
|
|
<!-- This section describes the evaluation protocols and provides the results. --> |
|
|
|
#### Summary |
|
|
|
|
|
<!-- ## Citation [optional] |
|
|
|
**BibTeX:** |
|
|
|
[More Information Needed] |
|
|
|
**APA:** |
|
|
|
[More Information Needed]--> |
|
|
|
## Model Card Contact |
|
|
|
simba -at- hiig.de |
