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Confidence scores correlate with answer correctness and can be used for filtering low-confidence predictions or ranking multiple candidate answers.","intents":["I need to filter out low-confidence QA predictions to maintain answer quality","I want to rank multiple candidate answers by their confidence scores","I need to detect when a question cannot be answered from the given passage","I want to measure model uncertainty for downstream decision-making"],"best_for":["QA system builders implementing confidence-based filtering","teams building multi-stage retrieval pipelines with confidence thresholds","researchers studying model calibration and uncertainty quantification","production systems requiring answer quality guarantees"],"limitations":["Confidence scores are not perfectly calibrated; may overestimate accuracy on out-of-domain text","SQuAD 2.0 training includes synthetic unanswerable questions; real-world unanswerable patterns may differ","No explicit 'no answer' token; confidence thresholding is required to detect unanswerable questions","Confidence scores reflect training data distribution; may not generalize to different question types or domains","Combining start and end logits into single confidence is heuristic; no principled Bayesian uncertainty quantification"],"requires":["Python 3.7+","transformers library 4.0+","PyTorch 1.9+ or TensorFlow 2.4+","Understanding of softmax probability interpretation","Empirical calibration on target domain to determine optimal confidence threshold"],"input_types":["question-passage pairs (text)","raw logits from model output"],"output_types":["confidence scores (float, 0.0-1.0 range)","boolean (answerable/unanswerable) based on threshold","ranked list of candidate answers with scores"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-google-bert--bert-large-cased-whole-word-masking-finetuned-squad__cap_4","uri":"capability://automation.workflow.batch.inference.with.attention.masking","name":"batch inference with attention masking","description":"Processes multiple question-passage pairs simultaneously through vectorized transformer operations, with automatic padding and attention masking to handle variable-length sequences. 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