Drones continuously generate flight log data containing valuable information about flight states, sensor readings, and system events. These logs are critical forensic artifacts for investigating incidents such as crashes or operational anomalies. However, previous forensic studies seldom explore the semantic context embedded within the human-readable flight log messages. This paper presents a Transformer-based named entity recognition (NER) framework to automatically extract meaningful entities such as events and issues from drone flight logs. To enhance generalization and simplify downstream analysis, the six original entity types defined in the DroNER dataset were merged into two higher-level classes Event and NonEvent to represent operational and anomalous contexts, respectively. We fine-tuned three pre-trained language models: BERT, DistilBERT, and SqueezeBERT, on this adapted dataset curated for drone forensic analysis. Experimental results show that SqueezeBERT, with only 51M parameters, achieves an F1 score of 96.79%, comparable to BERT’s 98.00%. This study is the first to benchmark. These findings suggest that lightweight Transformer architectures are highly promising for edge-level forensic NLP applications, enabling real-time log-based investigation automation on resource limited forensic platforms.