bert-base-turkish-cased-ner

Performs sequence labeling on Turkish text using a fine-tuned BERT-base model that classifies individual tokens into entity categories (person, location, organization, etc.). The model uses a transformer encoder architecture with a token-level classification head trained on Turkish NER datasets, enabling character-level and subword-level entity boundary detection through WordPiece tokenization. Outputs per-token probability distributions across entity classes, allowing downstream systems to extract structured entity spans with confidence scores.

Supports export to multiple inference-optimized formats (ONNX, SafeTensors, PyTorch) enabling deployment across heterogeneous hardware and runtime environments. The model can be loaded via HuggingFace transformers library in native PyTorch format, converted to ONNX for CPU-optimized inference via ONNX Runtime, or serialized as SafeTensors for faster deserialization and reduced memory overhead. Endpoints-compatible flag indicates support for HuggingFace Inference Endpoints and Azure ML deployment pipelines.

Implements token classification at the subword level using BERT's WordPiece tokenizer, which splits Turkish words into morphologically-aware subword units (e.g., 'İstanbul' → ['İ', 'st', 'anbul']). The model classifies each subword token independently, then aggregates predictions to entity-level spans through post-processing logic (e.g., taking the first subword's label or majority voting). This approach handles Turkish morphological complexity and out-of-vocabulary words by decomposing them into learned subword units.

Supports efficient batch processing of multiple Turkish text sequences with automatic padding to the longest sequence in the batch, minimizing wasted computation on shorter sequences. The model uses attention masks to ignore padding tokens during transformer computation, enabling variable-length batch processing without padding all sequences to the fixed 512-token maximum. Batch inference is optimized for GPU throughput, processing multiple documents in parallel while maintaining per-sequence output alignment.

Distributed under MIT license via HuggingFace Model Hub with 340k+ downloads, enabling unrestricted commercial and research use, modification, and redistribution. The model is versioned and tracked on HuggingFace with full reproducibility metadata (training data, hyperparameters, evaluation metrics), allowing downstream users to audit, fine-tune, or integrate into proprietary systems without licensing friction. Open-source distribution includes model cards documenting intended use, limitations, and evaluation results.