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First successful fine-tuning run, resulting model is no good but it does train successfully
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Initial attempt at fine-tuning using LoRA with basic cross-entropy loss
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ran full testing with updated prompt, improves team and other_stats performance, slight drop on game performance
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trying to prompt engineer fixes for other_stats table
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Adding completed pre-training testing runs to python notebook
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Created evaluation loop for running on full dataframes
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Merge branch 'main' of https://huggingface.co/USC-Applied-NLP-Group/SQL-Generation into main
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Updated evaluation function to give tolerance for slight floating point differences
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train data eda notebook
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Classify training data
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Test
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fixed issue with inconsistent formatting in training data
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updating prompt to include team name and abbreviations
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Small tweaks to evaluation function
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updating test pretrained script to evaluate model outputs
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Renamed demo script and added initial pre-trained test python notebook
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Adding SQL-NLP training data
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added better prompt engineering
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tweaking prompt a little further so it only outputs SQL more consistently
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Updated to use real nba database for test query
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added nba kaggle sqlite database and testing python notebook
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Update README.md
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Create README.md
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Delete README.md
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adding test code for deepseek coder model
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initial commit
