Reaction records satisfying these criteria were exported to JSON and deduplicated using OpenAI's data preparation tools (openai tools fine_tunes.prepare_data) to produce 1,339,260 unique records. The procedure text and structured JSON are combined using a prompt template (see Supporting Information) modified from Stanford Alpaca. A sequence length limit of 2048 tokens based on LLaMA tokenizer, is imposed due to memory considerations in fine-tuning the language models. This sequence limit reduces the number of records to 1,300,613 (97.11%) of 1,339,260. The cumulative distribution function of sequence lengths is shown in Figure . A subset of 100K records, hereinafter referred to as USPTO-ORD-100K, is randomly selected from the 1,300,613 records. Unless otherwise specified, a random 8:1:1 train:validation:test split is applied to USPTO-ORD-100K to train/evaluate models throughout this study. This data pipeline is schematically shown in Figure .