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danielsteinigen
/
KeyFiTax

Token Classification
Transformers
PyTorch
Safetensors
German
xlm-roberta
legal
tax law
relation extraction
entity extraction
Model card Files Community
1
KeyFiTax
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---
license: mit
datasets:
- danielsteinigen/KeyFiTax
language:
- de
library_name: transformers
tags:
- legal
- tax law
- relation extraction
- entity extraction
---

# Model Card for Model ID

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## Model Details

### Model Description

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- **Developed by:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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- **Repository:** https://github.com/danielsteinigen/nlp-legal-texts
- **Paper:** [More Information Needed]
- **Demo:** https://huggingface.co/spaces/danielsteinigen/NLP-Legal-Texts

## Uses
```
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 = "[REL]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."

results_key_figures = nlp(key_figures)
results_conditions  = nlp(conditions)
results_relations   = nlp(relations)
print(results_key_figures)
print(results_conditions)
print(results_relations)


```
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### Direct Use

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### Out-of-Scope Use

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

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## Training Details

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#### Training Hyperparameters

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## Evaluation

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