SUMIN/HuggingFace_transformer
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Few typos in README.md

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Grégory Châtel
2018-12-06 19:22:07 +01:00
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@@ -69,7 +69,7 @@ This package comprises the following classes that can be imported in Python and
The repository further comprises: 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`, - [`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_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. - [`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`. `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). 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).