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