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NghiemAbe
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Vi-Legal-PhoBert

Fill-Mask
Transformers
Safetensors
Vietnamese
roberta
legal
phobert
Inference Endpoints
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@@ -29,7 +29,7 @@ model = RobertaLongForMaskedLM.from_pretrained("NghiemAbe/Vi-Legal-PhoBert")
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  I evaluated my [Dev-Legal-Dataset](https://huggingface.co/datasets/NghiemAbe/dev_legal) and here are the results:
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  | Model | Paramaters | Language Type | Length | R@1 | R@5 | R@10 | R@20 | R@100 | MRR@5 | MRR@10 | MRR@20 | MRR@100 |
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  |-------------------------------|------------|---------------|--------|-------|-------|-------|-------|-------|-------|--------|--------|---------|
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- | vinai/phobert-base-v2 | 125M | vi | 256 | 0.188 | 0.378 | 0.480 | 0.542 | 0.704 | 0.272 | 0.285 | 0.290 | 0.294 |
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  | FacebookAI/xlm-roberta-base | 279M | mul | 512 | 0.012 | 0.042 | 0.064 | 0.091 | 0.207 | 0.025 | 0.028 | 0.030 | 0.033 |
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  | Geotrend/bert-base-vi-cased | 179M | vi | 512 | 0.098 | 0.175 | 0.202 | 0.241 | 0.356 | 0.131 | 0.136 | 0.139 | 0.142 |
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  | NlpHUST/roberta-base-vn | x | vi | 512 | 0.050 | 0.097 | 0.126 | 0.163 | 0.369 | 0.071 | 0.076 | 0.078 | 0.083 |
 
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  I evaluated my [Dev-Legal-Dataset](https://huggingface.co/datasets/NghiemAbe/dev_legal) and here are the results:
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  | Model | Paramaters | Language Type | Length | R@1 | R@5 | R@10 | R@20 | R@100 | MRR@5 | MRR@10 | MRR@20 | MRR@100 |
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  |-------------------------------|------------|---------------|--------|-------|-------|-------|-------|-------|-------|--------|--------|---------|
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+ | vinai/phobert-base-v2 | 125M | vi | 256 | 0.266 | 0.482 | 0.601 | 0.702 | 0.841 | 0.356 | 0.372 | 0.379 | 0.382 | 0.327| 0.345| 0.353| 0.357|
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  | FacebookAI/xlm-roberta-base | 279M | mul | 512 | 0.012 | 0.042 | 0.064 | 0.091 | 0.207 | 0.025 | 0.028 | 0.030 | 0.033 |
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  | Geotrend/bert-base-vi-cased | 179M | vi | 512 | 0.098 | 0.175 | 0.202 | 0.241 | 0.356 | 0.131 | 0.136 | 0.139 | 0.142 |
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  | NlpHUST/roberta-base-vn | x | vi | 512 | 0.050 | 0.097 | 0.126 | 0.163 | 0.369 | 0.071 | 0.076 | 0.078 | 0.083 |