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README.md
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* NumPy 1.22.2
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* SciPy 1.8.0
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* Regex 2022.1.18
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# To run the demo:
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1. Download and cd to project directory
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`git clone https://github.com/WHGTyen/ControllableComplexityChatbot`
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`cd ControllableComplexityChatbot`
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2. Install pip dependencies
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`pip install numpy scipy regex torch transformers`
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3. Clone ParlAI repository
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`git clone https://github.com/facebookresearch/ParlAI.git --branch 1.6.0`
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4. Setup ParlAI
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`cd ParlAI; python setup.py develop; cd ..`
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5. To run the demo:
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`python demo.py`
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6. To adjust generation parameters, edit values in `demo.py`
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# Data
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* `sample_wordlist.txt` is the 5000 most frequent words from [this list](https://github.com/first20hours/google-10000-english/blob/d0736d492489198e4f9d650c7ab4143bc14c1e9e/google-10000-english-no-swears.txt)
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* `filter.txt` was taken from [this list](https://github.com/dwyl/english-words/blob/22d7c41119076750a96fca2acd664ed994cc0a75/words_alpha.txt)
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* `complexity_model` was trained on data from the Cambridge Exams readability dataset, found [here](https://ilexir.co.uk/datasets/index.html)
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# Acknowledgements
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This paper reports on research supported by Cambridge University Press & Assessment. This work was performed using resources provided by the Cambridge Service for Data Driven Discovery (CSD3) operated by the [University of Cambridge Research Computing Service](www.csd3.cam.ac.uk), provided by Dell EMC and Intel using Tier-2 funding from the Engineering and Physical Sciences Research Council (capital grant EP/P020259/1), and [DiRAC funding from the Science and Technology Facilities Council](www.dirac.ac.uk).
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---
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title: ControllableBlenderCEFR
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sdk: gradio
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emoji: 🦀
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colorFrom: green
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colorTo: blue
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pinned: true
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short_description: A BlenderBot based Complexity adjuster
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---
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