Added some simple evaluation results
#5
by
kapllan
- opened
README.md
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@@ -85,9 +85,27 @@ For further details see [Niklaus et al. 2023](https://arxiv.org/abs/2306.02069?u
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## Evaluation
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For
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### Model Architecture and Objective
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## Evaluation
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We compare joelito/legal-swiss-roberta-base with the other multilingual models.
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The results are based on the text classification tasks presented in [Niklaus et al. (2023)](https://arxiv.org/abs/2306.09237) which are part of [LEXTREME](https://huggingface.co/datasets/joelito/lextreme).
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We provide the arithmetic mean over three seeds (1, 2, 3) based on the macro-F1-score on the test set.
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The highest values are in bold.
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| \_name_or_path | SCP-BC | SCP-BF | SCP-CC | SCP-CF | SJPXL-C | SJPXL-F | SLAP-SC | SLAP-SF |
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| :------------------------------------------------------------------------------------------------------ | :-------- | :-------- | :-------- | :-------- | :-------- | :-------- | :------- | :-------- |
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| [ZurichNLP/swissbert-xlm-vocab](https://huggingface.co/ZurichNLP/swissbert-xlm-vocab) | 71.36 | 57.48 | 27.33 | 23.37 | 80.81 | 61.75 | 77.89 | 71.27 |
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| [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) | 66.56 | 56.58 | 22.67 | 21.31 | 77.26 | 60.79 | 73.54 | 72.24 |
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| [facebook/xmod-base](https://huggingface.co/facebook/xmod-base) | 70.35 | 58.16 | 23.87 | 19.57 | 80.55 | 60.84 | 73.16 | 69.03 |
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| [joelito/legal-swiss-longformer-base](https://huggingface.co/joelito/legal-swiss-longformer-base) | **73.25** | **60.06** | **28.68** | 24.39 | 87.46 | **65.23** | 83.84 | 77.96 |
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| [joelito/legal-swiss-roberta-base](https://huggingface.co/joelito/legal-swiss-roberta-base) | 72.41 | 59.31 | 25.99 | 23.27 | 87.48 | 64.16 | **86.8** | **81.56** |
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| [joelito/legal-swiss-roberta-large](https://huggingface.co/joelito/legal-swiss-roberta-large) | 70.95 | 57.59 | 27.86 | 23.48 | **88.33** | 62.92 | 82.1 | 78.62 |
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| [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) | 67.29 | 56.56 | 24.23 | 14.9 | 79.52 | 58.29 | 63.03 | 67.57 |
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| [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) | 72.01 | 57.59 | 22.93 | **25.18** | 79.41 | 60.89 | 67.64 | 74.13 |
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| [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) | 68.55 | 58.48 | 25.66 | 21.52 | 80.98 | 61.45 | 79.3 | 74.47 |
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| [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) | 69.5 | 58.15 | 27.9 | 22.05 | 82.19 | 61.24 | 81.09 | 71.82 |
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For more detailed insights into the performance on downstream tasks, such as [LEXTREME](https://huggingface.co/datasets/joelito/lextreme) ([Niklaus et al. 2023](https://arxiv.org/abs/2301.13126)) or [LEXGLUE](https://huggingface.co/datasets/lex_glue) ([Chalkidis et al. 2021](https://arxiv.org/abs/2110.00976)), we refer to the results presented in Niklaus et al. (2023) [1](https://arxiv.org/abs/2306.02069), [2](https://arxiv.org/abs/2306.09237).
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For further insights into the evaluation, we refer to the [trainer state](https://huggingface.co/joelito/legal-swiss-roberta-base/blob/main/last-checkpoint/trainer_state.json). Additional information is available in the [tensorboard](https://huggingface.co/joelito/legal-swiss-roberta-base/tensorboard).
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### Model Architecture and Objective
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