first cut at adding data augmentation python notebook

added python notebook for testing finetuned model

First decent attempt, seems to generate valid queries after pre-training. Needs evaluation still

First successful fine-tuning run, resulting model is no good but it does train successfully

Initial attempt at fine-tuning using LoRA with basic cross-entropy loss

ran full testing with updated prompt, improves team and other_stats performance, slight drop on game performance

trying to prompt engineer fixes for other_stats table

Adding completed pre-training testing runs to python notebook

Created evaluation loop for running on full dataframes

Merge branch 'main' of https://huggingface.co/USC-Applied-NLP-Group/SQL-Generation into main

Updated evaluation function to give tolerance for slight floating point differences

train data eda notebook

Classify training data

Test

fixed issue with inconsistent formatting in training data

updating prompt to include team name and abbreviations

Small tweaks to evaluation function

updating test pretrained script to evaluate model outputs

Renamed demo script and added initial pre-trained test python notebook

Adding SQL-NLP training data

added better prompt engineering

tweaking prompt a little further so it only outputs SQL more consistently

Updated to use real nba database for test query

added nba kaggle sqlite database and testing python notebook

Update README.md

Create README.md

Delete README.md

adding test code for deepseek coder model

initial commit