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Hey Chris,
I tried to run the evaluation.py script with the following model facebook/mbart-large-cc25 (fine-tuned on a german dataset for the summarization task). However, during evaluation, I ran into GPU memory issues (on an NVIDIA GeForce RTX 2070 SUPER with 8 GB) with a beam size of 3 and a maximum of 3 articles per cluster. Do you have some suggestions on reducing the GPU memory footprint? Could you also share the resources you used to evaluate the DynE decoder?
Best,
Ribin
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