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README.md ADDED
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+ ---
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+ language: en
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+ license: cc-by-4.0
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+ pipeline_tag: summarization
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+ widget:
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+ - text: "Example question body."
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+ ---
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+
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+ # Titlewave: t5-base
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+
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+ ## Model description
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+
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+ Titlewave is a Chrome extension that helps you choose better titles for your Stack Overflow questions. See https://github.com/tennessejoyce/TitleWave for more information.
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+ This is one of two NLP models used in the Titlewave project, and its purpose is to suggests a new title based on on the body of the question. The companion model (https://huggingface.co/tennessejoyce/titlewave-bert-base-uncased) classifies whether question will be answered or not just based on the title
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+
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+ ## Intended use
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+
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+ Try out different titles for your Stack Overflow post, and see which one gives you the best chance of recieving an answer.
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+ This model can be used in your browser as a Chrome extension by following the installation instructions at https://github.com/tennessejoyce/TitleWave.
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+ Or load it in Python like this (which will automatically download the model to your machine):
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+
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+ ```python
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+ >>> from transformers import pipeline
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+ >>> classifier = pipeline('summarization', model='tennessejoyce/titlewave-t5-base')
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+ >>> body = """"Example question body."""
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+ >>> classifier(body)
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+
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+ [{'summary_text': 'Example title suggestion?'}]
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+ ```
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+
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+
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+ ## Training data
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+
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+ The weights were initialized from the BERT base model (https://huggingface.co/bert-base-uncased), which was trained on BookCorpus and English Wikipedia.
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+ Then the model was fine-tuned on the dataset of previous Stack Overflow post titles (https://archive.org/details/stackexchange).
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+ Specifically I used three years of posts from 2017-2019, filtered out posts which were closed, and selected 25% of the remaining posts at random to use in
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+ the training set. In order to improve the quality of the titles generated, the model was trained only on questions with an accepted answer.
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+
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+ ## Evaluation
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+
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+ See https://github.com/tennessejoyce/TitleWave/blob/master/model_training/test_summarizer.ipynb for the performance of the title generation model on the test set.
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+
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