
# Model Card for Model ID

This model performs entity extraction and relation extraction in a combined manner, using __*entity markers*__ and __*task triggers*__.
It processes German tax laws as input and outputs the extracted key figures with their properties and relations, based on a developed semantic model.

## Model Details

### Model Description

This is a fine-tuned token classification model, based on XLM-RoBERTa-Large, for the extraction of key figures and their logical connected properties from german tax legal texts. The entity- and relation extraction tasks are trained in a combined model using initial trigger token to distinguish between the tasks. For relation extraction additional tokens are used to mark the extracted entities and predict the relations between them.


- **Model type:** fine-tuned token classification model, based on XLM-RoBERTa-Large
- **Language(s) (NLP):** German

### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** https://github.com/danielsteinigen/nlp-legal-texts
- **Paper:** https://ceur-ws.org/Vol-3441/paper7.pdf
- **Demo:** https://huggingface.co/spaces/danielsteinigen/NLP-Legal-Texts
- **Data:** https://huggingface.co/datasets/danielsteinigen/KeyFiTax

## Uses
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("danielsteinigen/KeyFiTax")
model = AutoModelForTokenClassification.from_pretrained("danielsteinigen/KeyFiTax")

nlp = pipeline("ner", model=model, tokenizer=tokenizer)
key_figures = "[GRP-01]EStG § 66 Höhe des Kindergeldes, Zahlungszeitraum (1) Das Kindergeld beträgt monatlich für das erste und zweite Kind jeweils 219 Euro, für das dritte Kind 225 Euro und für das vierte und jedes weitere Kind jeweils 250 Euro."
conditions = "[GRP-00]EStG § 66 Höhe des Kindergeldes, Zahlungszeitraum (1) Das Kindergeld beträgt monatlich für das erste und zweite Kind jeweils 219 Euro, für das dritte Kind 225 Euro und für das vierte und jedes weitere Kind jeweils 250 Euro."
relations_1 = "[REL]EStG § 66 Höhe des Kindergeldes, Zahlungszeitraum (1) Das [SUB][ENT-01]Kindergeld[/ENT-01][/SUB] beträgt [OBJ][ENT-07]monatlich[/ENT-07][/OBJ] [OBJ][ENT-02]für das erste und zweite Kind[/ENT-02][/OBJ] [OBJ][ENT-07]jeweils[/ENT-07][/OBJ] [OBJ][ENT-03]219[/ENT-03][/OBJ] [OBJ][ENT-04]Euro[/ENT-04][/OBJ], [OBJ][ENT-02]für das dritte Kind[/ENT-02][/OBJ] [OBJ][ENT-03]225[/ENT-03][/OBJ] [OBJ][ENT-04]Euro[/ENT-04][/OBJ] und [OBJ][ENT-02]für das vierte und jedes weitere Kind[/ENT-02][/OBJ] [OBJ][ENT-07]jeweils[/ENT-07][/OBJ] [OBJ][ENT-03]250[/ENT-03][/OBJ] [OBJ][ENT-04]Euro[/ENT-04][/OBJ]."
relations_2 = "[REL]EStG § 66 Höhe des Kindergeldes, Zahlungszeitraum (1) Das [OBJ][ENT-01]Kindergeld[/ENT-01][/OBJ] beträgt [OBJ][ENT-07]monatlich[/ENT-07][/OBJ] [OBJ][ENT-02]für das erste und zweite Kind[/ENT-02][/OBJ] [OBJ][ENT-07]jeweils[/ENT-07][/OBJ] [SUB][ENT-03]219[/ENT-03][/SUB] [OBJ][ENT-04]Euro[/ENT-04][/OBJ], [OBJ][ENT-02]für das dritte Kind[/ENT-02][/OBJ] [OBJ][ENT-03]225[/ENT-03][/OBJ] [OBJ][ENT-04]Euro[/ENT-04][/OBJ] und [OBJ][ENT-02]für das vierte und jedes weitere Kind[/ENT-02][/OBJ] [OBJ][ENT-07]jeweils[/ENT-07][/OBJ] [OBJ][ENT-03]250[/ENT-03][/OBJ] [OBJ][ENT-04]Euro[/ENT-04][/OBJ]."
relations_3 = "[REL]EStG § 66 Höhe des Kindergeldes, Zahlungszeitraum (1) Das [OBJ][ENT-01]Kindergeld[/ENT-01][/OBJ] beträgt [OBJ][ENT-07]monatlich[/ENT-07][/OBJ] [OBJ][ENT-02]für das erste und zweite Kind[/ENT-02][/OBJ] [OBJ][ENT-07]jeweils[/ENT-07][/OBJ] [OBJ][ENT-03]219[/ENT-03][/OBJ] [OBJ][ENT-04]Euro[/ENT-04][/OBJ], [OBJ][ENT-02]für das dritte Kind[/ENT-02][/OBJ] [SUB][ENT-03]225[/ENT-03][/SUB] [OBJ][ENT-04]Euro[/ENT-04][/OBJ] und [OBJ][ENT-02]für das vierte und jedes weitere Kind[/ENT-02][/OBJ] [OBJ][ENT-07]jeweils[/ENT-07][/OBJ] [OBJ][ENT-03]250[/ENT-03][/OBJ] [OBJ][ENT-04]Euro[/ENT-04][/OBJ]."
relations_4 = "[REL]EStG § 66 Höhe des Kindergeldes, Zahlungszeitraum (1) Das [OBJ][ENT-01]Kindergeld[/ENT-01][/OBJ] beträgt [OBJ][ENT-07]monatlich[/ENT-07][/OBJ] [OBJ][ENT-02]für das erste und zweite Kind[/ENT-02][/OBJ] [OBJ][ENT-07]jeweils[/ENT-07][/OBJ] [OBJ][ENT-03]219[/ENT-03][/OBJ] [OBJ][ENT-04]Euro[/ENT-04][/OBJ], [OBJ][ENT-02]für das dritte Kind[/ENT-02][/OBJ] [OBJ][ENT-03]225[/ENT-03][/OBJ] [OBJ][ENT-04]Euro[/ENT-04][/OBJ] und [OBJ][ENT-02]für das vierte und jedes weitere Kind[/ENT-02][/OBJ] [OBJ][ENT-07]jeweils[/ENT-07][/OBJ] [SUB][ENT-03]250[/ENT-03][/SUB] [OBJ][ENT-04]Euro[/ENT-04][/OBJ]."

results_key_figures = nlp(key_figures)
results_conditions  = nlp(conditions)
results_relations_1   = nlp(relations_1)
results_relations_2   = nlp(relations_2)
results_relations_3   = nlp(relations_3)
results_relations_4   = nlp(relations_4)
print(results_key_figures)
print(results_conditions)
print(results_relations_1)
print(results_relations_2)
print(results_relations_3)
print(results_relations_4)


```

## Training Details

Training details can be found in our paper: [https://ceur-ws.org/Vol-3441/paper7.pdf](https://ceur-ws.org/Vol-3441/paper7.pdf)

### Training Data

The model is trained on our dataset __*KeyFiTax*__, which is published here: [https://huggingface.co/datasets/danielsteinigen/KeyFiTax](https://huggingface.co/datasets/danielsteinigen/KeyFiTax)

## Evaluation

Evaluation details can be found in our paper: [https://ceur-ws.org/Vol-3441/paper7.pdf](https://ceur-ws.org/Vol-3441/paper7.pdf)


## Citation

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**
```
@inproceedings{steinigen2023semantic,
  title={Semantic Extraction of Key Figures and Their Properties From Tax Legal Texts Using Neural Models},
  author={Steinigen, Daniel and Namysl, Marcin and Hepperle, Markus and Krekeler, Jan and Landgraf, Susanne},
  url = {https://ceur-ws.org/Vol-3441/paper7.pdf},
  year={2023}
  journal={Sixth Workshop on Automated Semantic Analysis of Information in Legal Text (ASAIL 2023)}, 
  series = {CEUR Workshop Proceedings},
  venue = {Braga, Portugal},
  eventdate = {2023-06-23}
}

```

**APA:**

Steinigen, D., Namysl, M., Hepperle, M., Krekeler, J., & Landgraf, S. (2023). Semantic Extraction of Key Figures and Their Properties From Tax Legal Texts Using Neural Models. 
Proceedings of Sixth Workshop on Automated Semantic Analysis of Information in Legal Text, Braga, Portugal, June 23, 2023. 
CEUR-WS.org, online CEUR-WS.org/Vol-3441/paper7.pdf.

## Additional Information

### Licensing Information
```
MIT License

Copyright (c) 2023 Daniel Steinigen

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
```
{"language": ["de"], "license": "mit", "library_name": "transformers", "tags": ["legal", "tax law", "relation extraction", "entity extraction"], "datasets": ["danielsteinigen/KeyFiTax"], "widget": [{"text": "[GRP-01]EStG \u00a7 66 H\u00f6he des Kindergeldes, Zahlungszeitraum (1) Das Kindergeld betr\u00e4gt monatlich f\u00fcr das erste und zweite Kind jeweils 219 Euro, f\u00fcr das dritte Kind 225 Euro und f\u00fcr das vierte und jedes weitere Kind jeweils 250 Euro.", "example_title": "Key Figures"}, {"text": "[GRP-00]EStG \u00a7 66 H\u00f6he des Kindergeldes, Zahlungszeitraum (1) Das Kindergeld betr\u00e4gt monatlich f\u00fcr das erste und zweite Kind jeweils 219 Euro, f\u00fcr das dritte Kind 225 Euro und f\u00fcr das vierte und jedes weitere Kind jeweils 250 Euro.", "example_title": "Conditions"}, {"text": "[REL]EStG \u00a7 66 H\u00f6he des Kindergeldes, Zahlungszeitraum (1) Das [SUB][ENT-01]Kindergeld[/ENT-01][/SUB] betr\u00e4gt [OBJ][ENT-07]monatlich[/ENT-07][/OBJ] [OBJ][ENT-02]f\u00fcr das erste und zweite Kind[/ENT-02][/OBJ] [OBJ][ENT-07]jeweils[/ENT-07][/OBJ] [OBJ][ENT-03]219[/ENT-03][/OBJ] [OBJ][ENT-04]Euro[/ENT-04][/OBJ], [OBJ][ENT-02]f\u00fcr das dritte Kind[/ENT-02][/OBJ] [OBJ][ENT-03]225[/ENT-03][/OBJ] [OBJ][ENT-04]Euro[/ENT-04][/OBJ] und [OBJ][ENT-02]f\u00fcr das vierte und jedes weitere Kind[/ENT-02][/OBJ] [OBJ][ENT-07]jeweils[/ENT-07][/OBJ] [OBJ][ENT-03]250[/ENT-03][/OBJ] [OBJ][ENT-04]Euro[/ENT-04][/OBJ].", "example_title": "Relations 1"}, {"text": "[REL]EStG \u00a7 66 H\u00f6he des Kindergeldes, Zahlungszeitraum (1) Das [OBJ][ENT-01]Kindergeld[/ENT-01][/OBJ] betr\u00e4gt [OBJ][ENT-07]monatlich[/ENT-07][/OBJ] [OBJ][ENT-02]f\u00fcr das erste und zweite Kind[/ENT-02][/OBJ] [OBJ][ENT-07]jeweils[/ENT-07][/OBJ] [SUB][ENT-03]219[/ENT-03][/SUB] [OBJ][ENT-04]Euro[/ENT-04][/OBJ], [OBJ][ENT-02]f\u00fcr das dritte Kind[/ENT-02][/OBJ] [OBJ][ENT-03]225[/ENT-03][/OBJ] [OBJ][ENT-04]Euro[/ENT-04][/OBJ] und [OBJ][ENT-02]f\u00fcr das vierte und jedes weitere Kind[/ENT-02][/OBJ] [OBJ][ENT-07]jeweils[/ENT-07][/OBJ] [OBJ][ENT-03]250[/ENT-03][/OBJ] [OBJ][ENT-04]Euro[/ENT-04][/OBJ].", "example_title": "Relations 2"}, {"text": "[REL]EStG \u00a7 66 H\u00f6he des Kindergeldes, Zahlungszeitraum (1) Das [OBJ][ENT-01]Kindergeld[/ENT-01][/OBJ] betr\u00e4gt [OBJ][ENT-07]monatlich[/ENT-07][/OBJ] [OBJ][ENT-02]f\u00fcr das erste und zweite Kind[/ENT-02][/OBJ] [OBJ][ENT-07]jeweils[/ENT-07][/OBJ] [OBJ][ENT-03]219[/ENT-03][/OBJ] [OBJ][ENT-04]Euro[/ENT-04][/OBJ], [OBJ][ENT-02]f\u00fcr das dritte Kind[/ENT-02][/OBJ] [SUB][ENT-03]225[/ENT-03][/SUB] [OBJ][ENT-04]Euro[/ENT-04][/OBJ] und [OBJ][ENT-02]f\u00fcr das vierte und jedes weitere Kind[/ENT-02][/OBJ] [OBJ][ENT-07]jeweils[/ENT-07][/OBJ] [OBJ][ENT-03]250[/ENT-03][/OBJ] [OBJ][ENT-04]Euro[/ENT-04][/OBJ].", "example_title": "Relations 3"}, {"text": "[REL]EStG \u00a7 66 H\u00f6he des Kindergeldes, Zahlungszeitraum (1) Das [OBJ][ENT-01]Kindergeld[/ENT-01][/OBJ] betr\u00e4gt [OBJ][ENT-07]monatlich[/ENT-07][/OBJ] [OBJ][ENT-02]f\u00fcr das erste und zweite Kind[/ENT-02][/OBJ] [OBJ][ENT-07]jeweils[/ENT-07][/OBJ] [OBJ][ENT-03]219[/ENT-03][/OBJ] [OBJ][ENT-04]Euro[/ENT-04][/OBJ], [OBJ][ENT-02]f\u00fcr das dritte Kind[/ENT-02][/OBJ] [OBJ][ENT-03]225[/ENT-03][/OBJ] [OBJ][ENT-04]Euro[/ENT-04][/OBJ] und [OBJ][ENT-02]f\u00fcr das vierte und jedes weitere Kind[/ENT-02][/OBJ] [OBJ][ENT-07]jeweils[/ENT-07][/OBJ] [SUB][ENT-03]250[/ENT-03][/SUB] [OBJ][ENT-04]Euro[/ENT-04][/OBJ].", "example_title": "Relations 4"}]}
transformers pytorch safetensors xlm-roberta token-classification legal tax law relation extraction entity extraction de dataset:danielsteinigen/KeyFiTax license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #xlm-roberta #token-classification #legal #tax law #relation extraction #entity extraction #de #dataset-danielsteinigen/KeyFiTax #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 


# Model Card for Model ID

This model performs entity extraction and relation extraction in a combined manner, using __*entity markers*__ and __*task triggers*__.
It processes German tax laws as input and outputs the extracted key figures with their properties and relations, based on a developed semantic model.

## Model Details

### Model Description

This is a fine-tuned token classification model, based on XLM-RoBERTa-Large, for the extraction of key figures and their logical connected properties from german tax legal texts. The entity- and relation extraction tasks are trained in a combined model using initial trigger token to distinguish between the tasks. For relation extraction additional tokens are used to mark the extracted entities and predict the relations between them.


- Model type: fine-tuned token classification model, based on XLM-RoBERTa-Large
- Language(s) (NLP): German

### Model Sources



- Repository: URL
- Paper: URL
- Demo: URL
- Data: URL

## Uses


## Training Details

Training details can be found in our paper: URL

### Training Data

The model is trained on our dataset __*KeyFiTax*__, which is published here: URL

## Evaluation

Evaluation details can be found in our paper: URL


BibTeX:


APA:

Steinigen, D., Namysl, M., Hepperle, M., Krekeler, J., & Landgraf, S. (2023). Semantic Extraction of Key Figures and Their Properties From Tax Legal Texts Using Neural Models. 
Proceedings of Sixth Workshop on Automated Semantic Analysis of Information in Legal Text, Braga, Portugal, June 23, 2023. 
URL, online URL

## Additional Information

### Licensing Information

# Model Card for Model ID

This model performs entity extraction and relation extraction in a combined manner, using __*entity markers*__ and __*task triggers*__.
It processes German tax laws as input and outputs the extracted key figures with their properties and relations, based on a developed semantic model. ## Model Details ### Model Description

This is a fine-tuned token classification model, based on XLM-RoBERTa-Large, for the extraction of key figures and their logical connected properties from german tax legal texts. The entity- and relation extraction tasks are trained in a combined model using initial trigger token to distinguish between the tasks. For relation extraction additional tokens are used to mark the extracted entities and predict the relations between them.


- Model type: fine-tuned token classification model, based on XLM-RoBERTa-Large
- Language(s) (NLP): German ### Model Sources



- Repository: URL
- Paper: URL
- Demo: URL
- Data: URL ## Uses ## Training Details

Training details can be found in our paper: URL ### Training Data

The model is trained on our dataset __*KeyFiTax*__, which is published here: URL ## Evaluation

Evaluation details can be found in our paper: URL


BibTeX:


APA:

Steinigen, D., Namysl, M., Hepperle, M., Krekeler, J., & Landgraf, S. (2023). Semantic Extraction of Key Figures and Their Properties From Tax Legal Texts Using Neural Models. 
Proceedings of Sixth Workshop on Automated Semantic Analysis of Information in Legal Text, Braga, Portugal, June 23, 2023. 
URL, online URL ## Additional Information ### Licensing Information
TAGS
#transformers #pytorch #safetensors #xlm-roberta #token-classification #legal #tax law #relation extraction #entity extraction #de #dataset-danielsteinigen/KeyFiTax #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # Model Card for Model ID

This model performs entity extraction and relation extraction in a combined manner, using __*entity markers*__ and __*task triggers*__.
It processes German tax laws as input and outputs the extracted key figures with their properties and relations, based on a developed semantic model. ## Model Details ### Model Description

This is a fine-tuned token classification model, based on XLM-RoBERTa-Large, for the extraction of key figures and their logical connected properties from german tax legal texts. The entity- and relation extraction tasks are trained in a combined model using initial trigger token to distinguish between the tasks. For relation extraction additional tokens are used to mark the extracted entities and predict the relations between them.


- Model type: fine-tuned token classification model, based on XLM-RoBERTa-Large
- Language(s) (NLP): German ### Model Sources



- Repository: URL
- Paper: URL
- Demo: URL
- Data: URL ## Uses ## Training Details

Training details can be found in our paper: URL ### Training Data

The model is trained on our dataset __*KeyFiTax*__, which is published here: URL ## Evaluation

Evaluation details can be found in our paper: URL


BibTeX:


APA:

Steinigen, D., Namysl, M., Hepperle, M., Krekeler, J., & Landgraf, S. (2023). Semantic Extraction of Key Figures and Their Properties From Tax Legal Texts Using Neural Models. 
Proceedings of Sixth Workshop on Automated Semantic Analysis of Information in Legal Text, Braga, Portugal, June 23, 2023. 
URL, online URL ## Additional Information ### Licensing Information