130 lines
4.7 KiB
Markdown
130 lines
4.7 KiB
Markdown
---
|
|
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 |