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license: apache-2.0 |
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# GPyT Project |
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GPyT is a GPT2 model trained from scratch (not fine tuned) on Python code from Github. Overall, it was ~200GB of pure |
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Python code, the current GPyT model is a mere 2 epochs through this data, so it may benefit greatly from continued training and/or fine-tuning. |
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Newlines are replaced by <newline> <N> |
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Input to the model is code, up to the context length of 1024, with newlines replaced by <newline> "</N>" |
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Here's a quick example of using this model: |
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```py |
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from transformers import AutoTokenizer, AutoModelWithLMHead |
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tokenizer = AutoTokenizer.from_pretrained("Reverb/GPyT") |
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model = AutoModelWithLMHead.from_pretrained("Reverb/GPyT") |
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# copy and paste some code in here |
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inp = """import""" |
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newlinechar = "<N>" |
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converted = inp.replace("\n", newlinechar) |
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tokenized = tokenizer.encode(converted, return_tensors='pt') |
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resp = model.generate(tokenized) |
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decoded = tokenizer.decode(resp[0]) |
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reformatted = decoded.replace("<N>","\n") |
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print(reformatted) |
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``` |
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Should produce: |
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```py |
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import numpy as np |
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import pytest |
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import pandas as pd<N |
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``` |
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## The Journey |
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The model took 6 major steps which are: |
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1. Data Collection |
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2. Raw Data Cleaning |
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3. Data Preprocessing |
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4. Building & Training the Tokenizer |
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5. Testing the Model on Large Dataset |
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6. Deploying the Final Model on HuggingFace |
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#### Data Collection |
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The data was collected from python github repositories using web scraping techniques, It took nearly a day to gather 200GB worth of data. |
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#### Raw Data Cleaning |
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200GB of python code?? sounds ridiculous! that's why we needed to clean the downloaded repositories from any non-python files such as PDF,idx..etc |
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#### Data Preprocessing |
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I tried splitting the lines of code for each repository then merged them all under one single text file named **python_text_data.txt** |
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#### Building & Training the Tokenizer |
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For this step I have used **ByteLevelBPETokenizer** and trained it then saved the model on the desktop |
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#### Testing the Model on Large Dataset |
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After training the tokenizer on a large dataset, It was time for some tests to see how good is the model before proceeding. |
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## Considerations: |
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> - This model is intended for educational and research use only. Do not trust model outputs. |
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> - Model is highly likely to regurgitate code almost exactly as it saw it. It's up to you to determine licensing if you intend to actually use the generated code. |
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> - All Python code was blindly pulled from github. This means included code is both Python 2 and 3, among other more subtle differences, such as tabs being 2 spaces in some cases and 4 in others...and more non-homologous things. |
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> - Along with the above, this means the code generated could wind up doing or suggesting just about anything. Run the generated code at own risk...it could be anything |
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