Seed-mediated AuNR Synthesis Extraction with GPT-3

The files were used as follows: Empty synthesis template: clean_template_formatted.json Training sets for fine-tuning GPT-3 to extract structured seed-mediated AuNR growth procedures from unstructured text: aunr_synth_v6_ucfx_static_0_240_0_corrected_publishable.json Predictions and corrected predictions for evaluating the performance of GPT-3 recipe extraction completions: aunr_synth_v6_ucfx_static_0_40_6_predict_publishable.json aunr_synth_v6_ucfx_static_0_40_6_corrected_publishable.json GPT-3 recipe extraction completion prediction over the complete dataset: aunr_synth_v6_ucfx_static_0_1137_0_predict_publishable.json GPT-3 recipe extraction completion prediction over the complete dataset merged by paper with empty fields removed: aunr_synth_v6_ucfx_static_dynamic_0_1137_0_predict_paperwise_merged_publishable.json GPT-3 recipe extraction completion prediction over the complete dataset merged by paper with empty fields removed and post-processing applied: aunr_synth_v6_ucfx_static_dynamic_0_1137_0_predict_paperwise_merged_postprocessed_n_recipes_publishable.json Performance evaluation scripts: prediction_statistics_complete.py paperwise_prediction_statistics_complete.py paragraphwise_prediction_statistics_complete.py