From 5b78400e212825f4c1f4c9ce3f6fc68dea1c2496 Mon Sep 17 00:00:00 2001 From: Stefan Schweter Date: Tue, 16 Jul 2019 23:41:57 +0200 Subject: [PATCH] docs: fix link to modeling example source (bert) --- docs/source/model_doc/overview.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/model_doc/overview.rst b/docs/source/model_doc/overview.rst index 7e24115ae3..8c77efd3f9 100644 --- a/docs/source/model_doc/overview.rst +++ b/docs/source/model_doc/overview.rst @@ -90,7 +90,7 @@ where * ``bert_config.json`` or ``openai_gpt_config.json`` a configuration file for the model, and * ``pytorch_model.bin`` a PyTorch dump of a pre-trained instance of ``BertForPreTraining``\ , ``OpenAIGPTModel``\ , ``TransfoXLModel``\ , ``GPT2LMHeadModel`` (saved with the usual ``torch.save()``\ ) - If ``PRE_TRAINED_MODEL_NAME_OR_PATH`` is a shortcut name, the pre-trained weights will be downloaded from AWS S3 (see the links `here `__\ ) and stored in a cache folder to avoid future download (the cache folder can be found at ``~/.pytorch_pretrained_bert/``\ ). + If ``PRE_TRAINED_MODEL_NAME_OR_PATH`` is a shortcut name, the pre-trained weights will be downloaded from AWS S3 (see the links `here `__\ ) and stored in a cache folder to avoid future download (the cache folder can be found at ``~/.pytorch_pretrained_bert/``\ ). * ``cache_dir`` can be an optional path to a specific directory to download and cache the pre-trained model weights. This option is useful in particular when you are using distributed training: to avoid concurrent access to the same weights you can set for example ``cache_dir='./pretrained_model_{}'.format(args.local_rank)`` (see the section on distributed training for more information).