14 нояб. 2021 г. · A highly cited paper on training tips for Transformers MT recommends getting the best results with 12k tokens per batch.

10 дек. 2019 г. · The BERT paper used 5e-5, 4e-5, 3e-5, and 2e-5 for fine-tuning. We use a batch size of 32 and fine-tune for 3 epochs over the data for all GLUE tasks.

26 нояб. 2020 г. · Small mini-batch size leads to a big variance in the gradients. In theory, with a sufficiently small learning rate, you can learn anything ...

17 авг. 2021 г. · The method they use is a simple sequence classification using BERT. They do it with batch size 48, learning rate 4e-5, optimization Adam, and ...

26 авг. 2020 г. · The fine-tuning examples which use BERT-Base should be able to run on a GPU that has at least 12GB of RAM using the hyperparameters given on this page.

11 мая 2022 г. · Only the current batch should be loaded in GPU RAM, so you should not need to reduce your training data size (assuming your data loading and training routines ...

5 июл. 2017 г. · Since the number of PP is often a power of 2, using a number of VP different from a power of 2 leads to poor performance.

3 мая 2021 г. · As I understand, the model accepts input in the shape of [Batch, Indices] where Batch is of arbitrary size (usually 32, 64 or whatever) and ...

22 нояб. 2023 г. · It depends on how you actually load your data on the GPU: if you load your whole dataset on the GPU, then increasing the dataset size will certainly increase ...

1 сент. 2022 г. · Standard BERT models take 768 (1024) dimensional vectors as their input. There is an encoding step that tokenizes and encodes a sentence ...