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typos

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thomwolf 2019-07-27 12:08:21 +02:00
parent ac27548b25
commit 4cc1bf81ee
3 changed files with 6 additions and 6 deletions
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4
pytorch_transformers/modeling_auto.py
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@ -157,7 +157,7 @@ class AutoModel(object):
- contains `xlnet`: XLNetConfig (XLNet model)
- contains `xlm`: XLMConfig (XLM model)
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are desactivated)
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
To train the model, you should first set it back in training mode with `model.train()`
Params:
@ -179,7 +179,7 @@ class AutoModel(object):
- the model was saved using the `save_pretrained(save_directory)` (loaded by suppling the save directory).
**state_dict**: an optional state dictionnary for the model to use instead of a state dictionary loaded
from saved weights file.
This option can be used if you want to create a model from a pretrained configuraton but load your own weights.
This option can be used if you want to create a model from a pretrained configuration but load your own weights.
In this case though, you should check if using `save_pretrained(dir)` and `from_pretrained(save_directory)` is not
a simpler option.
**cache_dir**: (`optional`) string:

4
pytorch_transformers/modeling_utils.py
View File

@ -324,7 +324,7 @@ class PreTrainedModel(nn.Module):
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
r"""Instantiate a pretrained pytorch model from a pre-trained model configuration.
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are desactivated)
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
To train the model, you should first set it back in training mode with `model.train()`
Params:
@ -346,7 +346,7 @@ class PreTrainedModel(nn.Module):
- the model was saved using the `save_pretrained(save_directory)` (loaded by suppling the save directory).
**state_dict**: an optional state dictionnary for the model to use instead of a state dictionary loaded
from saved weights file.
This option can be used if you want to create a model from a pretrained configuraton but load your own weights.
This option can be used if you want to create a model from a pretrained configuration but load your own weights.
In this case though, you should check if using `save_pretrained(dir)` and `from_pretrained(save_directory)` is not
a simpler option.
**cache_dir**: (`optional`) string:

4
pytorch_transformers/tokenization_bert.py
View File

@ -119,7 +119,7 @@ class BertTokenizer(PreTrainedTokenizer):
Only has an effect when do_basic_tokenize=True
**tokenize_chinese_chars**: (`optional`) boolean (default True)
Whether to tokenize Chinese characters.
This should likely be desactivated for Japanese:
This should likely be deactivated for Japanese:
see: https://github.com/huggingface/pytorch-pretrained-BERT/issues/328
"""
super(BertTokenizer, self).__init__(unk_token=unk_token, sep_token=sep_token,
@ -214,7 +214,7 @@ class BasicTokenizer(object):
List of token not to split.
**tokenize_chinese_chars**: (`optional`) boolean (default True)
Whether to tokenize Chinese characters.
This should likely be desactivated for Japanese:
This should likely be deactivated for Japanese:
see: https://github.com/huggingface/pytorch-pretrained-BERT/issues/328
"""
if never_split is None: