-
followed by
supervised fine-tuning on a
small task-specific dataset. The
pretrain dataset is
typically an
unlabeled large corpus, such as The Pile. Tasks...
-
Initialize with a
previously pretrained DeepS****-Coder Base v1.5 7B.
Further pretrain with 500B
tokens (6% DeepS****Math Corpus, 4% AlgebraicStack, 10% arXiv...
- x} are
sampled from the
original pretraining dataset D
pretrain {\displaystyle D_{\text{
pretrain}}} , and the
model is
trained to
maximize the log-likelihood...
-
trillions of tokens"" (PDF). Wang, Boxin; Ping, Wei (2023). ""Shall We
Pretrain Autoregressive Language Models with Retrieval? A
Comprehensive Study""...
-
asked Hinton to
explain in
simpler terms how the
Boltzmann machine could "
pretrain"
backpropagation networks,
Hinton quipped that
Richard Feynman reportedly...
-
learning of a
certain data type (e.g. text, image, audio, video) is to
pretrain the
model using large datasets of
general context,
unlabeled data. Depending...
- LSTM to a
Transformer encoder,
giving rise to BERT. BERT has the same
pretrain-fine-tune workflow, but uses a
Transformer for
parallelizable training...
- com****tional
resources and time
required for
model training. With the "
pretrain, then finetune"
method used for most
large language models,
there are two...
- 20. Hinton, Geoffrey; Salakhutdinov,
Ruslan (2012). "A
better way to
pretrain deep
Boltzmann machines" (PDF).
Advances in Neural. 3: 1–9.
Archived from...
- task-specific data. For example, to make a
chatbot in this method, one
first pretrains a
large transformer model over a few
trillion words of text s****ed from...
