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Fix some typos in README

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Christopher Goh 2019-10-07 01:14:34 +08:00 committed by Lysandre Debut
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@ -67,7 +67,7 @@ This repo is tested on Python 2.7 and 3.5+ (examples are tested only on python 3
### With pip ### With pip
First you need to install one of, or both, TensorFlow 2.0 and PyTorch. First you need to install one of, or both, TensorFlow 2.0 and PyTorch.
Please refere to [TensorFlow installation page](https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available) and/or [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific install command for your platform. Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available) and/or [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific install command for your platform.
When TensorFlow 2.0 and/or PyTorch has been installed, 🤗 Transformers can be installed using pip as follows: When TensorFlow 2.0 and/or PyTorch has been installed, 🤗 Transformers can be installed using pip as follows:
@ -78,7 +78,7 @@ pip install transformers
### From source ### From source
Here also, you first need to install one of, or both, TensorFlow 2.0 and PyTorch. Here also, you first need to install one of, or both, TensorFlow 2.0 and PyTorch.
Please refere to [TensorFlow installation page](https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available) and/or [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific install command for your platform. Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/pip#tensorflow-2.0-rc-is-available) and/or [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) regarding the specific install command for your platform.
When TensorFlow 2.0 and/or PyTorch has been installed, you can install from source by cloning the repository and running: When TensorFlow 2.0 and/or PyTorch has been installed, you can install from source by cloning the repository and running:
@ -423,7 +423,7 @@ Here is a quick summary of what you should take care of when migrating from `pyt
### Models always output `tuples` ### Models always output `tuples`
The main breaking change when migrating from `pytorch-pretrained-bert` to `transformers` is that the models forward method always outputs a `tuple` with various elements depending on the model and the configuration parameters. The main breaking change when migrating from `pytorch-pretrained-bert` to `transformers` is that the model's forward method always outputs a `tuple` with various elements depending on the model and the configuration parameters.
The exact content of the tuples for each model is detailed in the models' docstrings and the [documentation](https://huggingface.co/transformers/). The exact content of the tuples for each model is detailed in the models' docstrings and the [documentation](https://huggingface.co/transformers/).
@ -459,9 +459,9 @@ By enabling the configuration option `output_hidden_states`, it was possible to
Breaking change in the `from_pretrained()` method: Breaking change in the `from_pretrained()` method:
1. Models are now set in evaluation mode by default when instantiated with the `from_pretrained()` method. To train them don't forget to set them back in training mode (`model.train()`) to activate the dropout modules. 1. Models are now set in evaluation mode by default when instantiated with the `from_pretrained()` method. To train them, don't forget to set them back in training mode (`model.train()`) to activate the dropout modules.
2. The additional `*input` and `**kwargs` arguments supplied to the `from_pretrained()` method used to be directly passed to the underlying model's class `__init__()` method. They are now used to update the model configuration attribute instead which can break derived model classes build based on the previous `BertForSequenceClassification` examples. We are working on a way to mitigate this breaking change in [#866](https://github.com/huggingface/transformers/pull/866) by forwarding the the model `__init__()` method (i) the provided positional arguments and (ii) the keyword arguments which do not match any configuration class attributes. 2. The additional `*input` and `**kwargs` arguments supplied to the `from_pretrained()` method used to be directly passed to the underlying model's class `__init__()` method. They are now used to update the model configuration attribute instead, which can break derived model classes built based on the previous `BertForSequenceClassification` examples. We are working on a way to mitigate this breaking change in [#866](https://github.com/huggingface/transformers/pull/866) by forwarding the the model's `__init__()` method (i) the provided positional arguments and (ii) the keyword arguments which do not match any configuration class attributes.
Also, while not a breaking change, the serialization methods have been standardized and you probably should switch to the new method `save_pretrained(save_directory)` if you were using any other serialization method before. Also, while not a breaking change, the serialization methods have been standardized and you probably should switch to the new method `save_pretrained(save_directory)` if you were using any other serialization method before.