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merge #22

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Aug 1, 2019
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769bb64
Fixing a broken link.
rodgzilla Jul 31, 2019
97091ac
Small spelling fix
Pierrci Jul 31, 2019
f2a3eb9
Fix small typos
n1t0 Jul 31, 2019
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8 changes: 4 additions & 4 deletions README.md
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Expand Up @@ -18,7 +18,7 @@ These implementations have been tested on several datasets (see the example scri
| Section | Description |
|-|-|
| [Installation](#installation) | How to install the package |
| [Quick tour: Usage](#quick-tour-usage) | Tokenizers & models usage: Bert and GPT-2 |
| [Quick tour: Usage](#quick-tour) | Tokenizers & models usage: Bert and GPT-2 |
| [Quick tour: Fine-tuning/usage scripts](#quick-tour-of-the-fine-tuningusage-scripts) | Using provided scripts: GLUE, SQuAD and Text generation |
| [Migrating from pytorch-pretrained-bert to pytorch-transformers](#Migrating-from-pytorch-pretrained-bert-to-pytorch-transformers) | Migrating your code from pytorch-pretrained-bert to pytorch-transformers |
| [Documentation](https://huggingface.co/pytorch-transformers/) | Full API documentation and more |
Expand Down Expand Up @@ -194,7 +194,7 @@ python ./examples/run_glue.py \
--warmup_steps=120
```

On this machine we thus have a batch size of 32, please increase `gradient_accumulation_steps` to reach the same batch size if you have a smaller machine. These hyper-parameters should results in a Pearson correlation coefficient of `+0.917` on the development set.
On this machine we thus have a batch size of 32, please increase `gradient_accumulation_steps` to reach the same batch size if you have a smaller machine. These hyper-parameters should result in a Pearson correlation coefficient of `+0.917` on the development set.

#### Fine-tuning Bert model on the MRPC classification task

Expand Down Expand Up @@ -264,7 +264,7 @@ This is the model provided as `bert-large-uncased-whole-word-masking-finetuned-s
### `run_generation.py`: Text generation with GPT, GPT-2, Transformer-XL and XLNet

A conditional generation script is also included to generate text from a prompt.
The generation script include the [tricks](https://github.com/rusiaaman/XLNet-gen#methodology) proposed by by Aman Rusia to get high quality generation with memory models like Transformer-XL and XLNet (include a predefined text to make short inputs longer).
The generation script includes the [tricks](https://github.com/rusiaaman/XLNet-gen#methodology) proposed by by Aman Rusia to get high quality generation with memory models like Transformer-XL and XLNet (include a predefined text to make short inputs longer).

Here is how to run the script with the small version of OpenAI GPT-2 model:

Expand All @@ -283,7 +283,7 @@ Here is a quick summary of what you should take care of when migrating from `pyt

The main breaking change when migrating from `pytorch-pretrained-bert` to `pytorch-transformers` is that the models 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 are detailled in the models' docstrings and the [documentation](https://huggingface.co/pytorch-transformers/).
The exact content of the tuples for each model are detailed in the models' docstrings and the [documentation](https://huggingface.co/pytorch-transformers/).

In pretty much every case, you will be fine by taking the first element of the output as the output you previously used in `pytorch-pretrained-bert`.

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