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---
license: mit
datasets:
- LennardZuendorf/legalis
language:
- de
metrics:
- accuracy
library_name: transformers
tags:
- legal
---
<h1> Legalis BERT Model </h1>
<h2> Model Details </h2>
<h3> Model Description </h3>
This is a court case prediction model build for a university course, this particular one utalises text classification with
- **Developed by:** Lennard Zündorf
- **Model type:** transformer-based
- **Language(s) (NLP):** German
- **Finetuned from model :** [German BERT/ gbert-base](https://huggingface.co/deepset/gbert-base)
<h3> Model Sources </h3>
- **Repository:** [GitHub](https://github.com/LennardZuendorf/legalis)
- **Demo:** on [Huggingface](https://huggingface.co/spaces/LennardZuendorf/legalis)
<h2> Uses </h2>
You can use this model to try and predict the outcome of a court case based on the legal facts.
<h2> Training Details </h2>
<h3>Training Data
This model uses the similarly named [dataset](https://huggingface.co/models?dataset=dataset:LennardZuendorf/legalis)
<h3> Testing Data & Metrics
<h4> Metrics </h4>
There has not been any testing yet.
<h3> Results </h3>
The accuracy score against the testing split is as high as 0.60 |