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mbert-argmining-abstrct-en-es

Token Classification
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@@ -36,7 +36,7 @@ The results (F1 macro averaged at token level) achieved for each test set:
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  You can find more information:
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  - 📖 Paper: [Crosslingual Argument Mining in the Medical Domain](https://arxiv.org/abs/2301.10527)
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- - Code: [https://github.com/ragerri/abstrct-projections](https://github.com/ragerri/abstrct-projections)
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  You can load the model as follows:
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  You can find more information:
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  - 📖 Paper: [Crosslingual Argument Mining in the Medical Domain](https://arxiv.org/abs/2301.10527)
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+ - 💻Code: [https://github.com/ragerri/abstrct-projections](https://github.com/ragerri/abstrct-projections)
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  You can load the model as follows:
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