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README.md
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@@ -40,7 +40,7 @@ MLM procedure, the model also has to predict jump targets: whenever there is a j
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jump instruction, but it should also predict to which line of code it's supposed to jump. The idea is that this leads to better understanding of the long-term relations
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in the code, and as Wang et al yielded good results, we have adopted it.
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Finetuning happens based on triplet loss: given three functions, of which two are similar (see also [Data](#
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are similar to each other. We used [Batched Semi Hard Triplet Loss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchsemihardtripletloss) to
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ensure that the training task is not too easy.
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jump instruction, but it should also predict to which line of code it's supposed to jump. The idea is that this leads to better understanding of the long-term relations
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in the code, and as Wang et al yielded good results, we have adopted it.
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+
Finetuning happens based on triplet loss: given three functions, of which two are similar (see also [Data](https://huggingface.co/NetherlandsForensicInstitute/Multilingual-ASMBERT/blob/main/README.md#data)) and one is deviant, the model has to pick which two functions
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are similar to each other. We used [Batched Semi Hard Triplet Loss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#batchsemihardtripletloss) to
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ensure that the training task is not too easy.
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