dharshan3119/fio-narrative-distilgpt2

DistilGPT-2 fine-tuned on Boston Police FIO narratives

MAAI 902 project – Navi Dharshan B. Fine-tuned on 3683 train / 460 validation / 460 test redacted Boston FIO narratives (2024) (Analyze Boston, PDDL licence; names, officer names, addresses and neighbourhoods replaced with placeholders).

Training: learning rate 0.0001 with 10% warm-up and linear decay, batch 8, weight decay 0.01, block size 128; early stopping on validation loss (patience 3) stopped at epoch 4.74; best checkpoint at epoch 3.99 (validation loss 2.276).

Held-out test set

Metric	Base DistilGPT-2	Fine-tuned
Test perplexity (lower = better)	78.617	10.016
Next-token accuracy, top-1	0.309	0.529
Next-token accuracy, top-5	0.486	0.747
ROUGE-L F1	0.100	0.179
ROUGE-L precision	0.099	0.192
ROUGE-L recall	0.106	0.177
BLEU-4	0.004	0.038
Domain-style rate	0.027	0.987
Repetition rate (lower = better)	0.000	0.004
Distinct-2 (variety)	0.597	0.322

All generated text is synthetic, describes no real event and must not be presented as a police record.