distilbert-base-uncased-distilled-squad

Identifies and extracts answer spans directly from input text by predicting start and end token positions using a fine-tuned DistilBERT encoder with two linear classification heads. The model processes tokenized text through 6 transformer layers (distilled from BERT-base's 12 layers) and outputs logits for each token position, enabling sub-second inference on CPU for passage-based QA tasks without requiring answer generation.

Provides pre-converted model weights across PyTorch, TensorFlow, TFLite, and CoreML formats stored in SafeTensors serialization, enabling deployment across diverse inference runtimes (cloud, mobile, edge) without requiring manual conversion pipelines. The model is registered with Hugging Face Hub's endpoints infrastructure, supporting direct API deployment to Azure, AWS, and other cloud providers via standardized model serving interfaces.

Fine-tuned specifically on the Stanford Question Answering Dataset (SQuAD v1.1) using supervised learning on 100K+ question-answer pairs, producing calibrated confidence scores (0-1) for each predicted span. The model learns to distinguish between answerable and unanswerable questions through contrastive training on negative examples, outputting both the extracted span and a confidence metric derived from softmax probabilities over token positions.

Supports efficient batch processing of multiple question-context pairs through Hugging Face Transformers' batching utilities, which handle variable-length inputs via dynamic padding (padding to max length in batch, not fixed 512), and return batched tensor outputs optimized for GPU/CPU parallelization. The pipeline automatically tokenizes questions and contexts, manages attention masks, and returns structured predictions for all samples in a single forward pass.

While trained on SQuAD (Wikipedia), the model can be applied to out-of-domain passages (medical, legal, technical) by reformulating questions or providing domain-specific context in the passage prefix, leveraging the learned span extraction capability without fine-tuning. This works because the underlying transformer learns general language understanding and token classification patterns that partially transfer to new domains, though with degraded accuracy.