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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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# Titlewave: t5-base |
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## Model description |
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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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## Intended use |
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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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```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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[{'summary_text': 'Example title suggestion?'}] |
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``` |
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## Training data |
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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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## Evaluation |
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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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