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README.md
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@@ -61,7 +61,7 @@ For this part, we use 2 datasets, the first one: [allocine](https://huggingface.
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| [BaptisteDoyen/camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli) | 378.39 | **86.37** | **73.74** |
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| [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) | 520.58 | 84.97 | 70.05 |
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The second one: [mlsum](https://huggingface.co/datasets/mlsum) used to train the summarization models. We use the articles summary part to predict their topics. In this aim, we aggregate sub-topics and select a few of them. In this case, the hypothesis template used is "C'est un article traitant de {}." and the candidate labels are: "économie", "politique", "sport"
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| **model** | **time (ms)** | **accuracy (%)** | **MCC (x100)** |
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| :--------------: | :-----------: | :--------------: | :------------: |
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| [BaptisteDoyen/camembert-base-xnli](https://huggingface.co/BaptisteDoyen/camembert-base-xnli) | 378.39 | **86.37** | **73.74** |
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| [MoritzLaurer/mDeBERTa-v3-base-mnli-xnli](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-mnli-xnli) | 520.58 | 84.97 | 70.05 |
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The second one: [mlsum](https://huggingface.co/datasets/mlsum) used to train the summarization models. We use the articles summary part to predict their topics. In this aim, we aggregate sub-topics and select a few of them. In this case, the hypothesis template used is "C'est un article traitant de {}." and the candidate labels are: "économie", "politique", "sport" and "science".
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| **model** | **time (ms)** | **accuracy (%)** | **MCC (x100)** |
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| :--------------: | :-----------: | :--------------: | :------------: |
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