From 150f3cd9fa9a360eaf1bbc9178a5b894d899e74b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Gr=C3=A9gory=20Ch=C3=A2tel?= Date: Thu, 6 Dec 2018 19:22:07 +0100 Subject: [PATCH] Few typos in README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index b9d57bcb3f..d443ba7a07 100644 --- a/README.md +++ b/README.md @@ -69,7 +69,7 @@ This package comprises the following classes that can be imported in Python and The repository further comprises: -- Three examples on how to use Bert (in the [`examples` folder](./examples)): +- Four examples on how to use Bert (in the [`examples` folder](./examples)): - [`extract_features.py`](./examples/extract_features.py) - Show how to extract hidden states from an instance of `BertModel`, - [`run_classifier.py`](./examples/run_classifier.py) - Show how to fine-tune an instance of `BertForSequenceClassification` on GLUE's MRPC task, - [`run_squad.py`](./examples/run_squad.py) - Show how to fine-tune an instance of `BertForQuestionAnswering` on SQuAD v1.0 task. @@ -284,7 +284,7 @@ An example on how to use this class is given in the [`run_classifier.py`](./exam `BertForMultipleChoice` is a fine-tuning model that includes `BertModel` and a linear layer on top of the `BertModel`. -The linear layer outputs a single value for each choice of a multiple choice problem, then all the output corresponding to an instance are passed through a softmax to get the model choice. +The linear layer outputs a single value for each choice of a multiple choice problem, then all the outputs corresponding to an instance are passed through a softmax to get the model choice. This implementation is largely inspired by the work of OpenAI in [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) and the answer of Jacob Devlin in the following [issue](https://github.com/google-research/bert/issues/38).