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Autocomplete Python Code with Transformers

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Autocomplete Python Code with Transformers

This is a small project we created to train a character level autoregressive transformer (or LSTM) model to predict Python source code. We trained it on GitHub repositories found on awesome pytorch list. Github repo: https://github.com/lab-ml/python_autocomplete You can try training on Google Colab: https://colab.research.google.com/github/lab-ml/python_autocomplete/blob/master/notebooks/train.ipynb Here are some sample evaluations/visualizations of the trained model: https://colab.research.google.com/github/lab-ml/python_autocomplete/blob/master/notebooks/evaluate.ipynb Working on a simple VSCode extension to test this out. Will open source it soon on the same repository.

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Actual performance

3points
2comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, google, visual · Missing: mac, agents, macos
64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: lua, open source, io · Missing: https docs, excited, just released
57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: google · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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