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Fine-tune BERT for Extractive Summarization中文数据集LCSTS复现; Self-Supervised Learning for Contextualized Extractive Summarization; Heterogeneous Graph Neural Networks for Extractive Document Summarization; SummaRuNNer: A Recurrent Neural Network Based Sequence Model for Extractive Summarization of Documen You’ll notice that even this “slim” BERT has almost 110 million parameters. Indeed, your model is HUGE (that’s what she said). Fine-tuning models like BERT is both art and doing tons of failed experiments. Fortunately, the authors made some recommendations: Batch size: 16, 32 Learning rate (Adam): 5e-5, 3e-5, 2e-5 Number of epochs: 2, 3, 4

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