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The model processes question-passage pairs through BERT-style token embeddings and attention layers, then predicts start and end token positions marking the answer span. Unlike generative QA models, it operates via span selection from existing text, enabling high precision on factoid questions where answers appear verbatim in the source material.","intents":["extract factual answers from documents without generating new text","build reading comprehension systems that cite exact source locations","implement low-latency QA pipelines that don't require decoding time","create fact-checking tools that ground answers in provided passages"],"best_for":["teams building document-based QA systems (legal, medical, technical documentation)","developers needing deterministic, citable answers from fixed corpora","resource-constrained environments where generation latency is prohibitive"],"limitations":["cannot answer questions when the answer doesn't appear verbatim in the passage","struggles with multi-hop reasoning requiring synthesis across distant text segments","performance degrades on paraphrased or implicit answers not directly stated in source","limited to English language tasks; no multilingual variant documented"],"requires":["PyTorch 1.9+","transformers library 4.0+","input passages must be pre-tokenized and formatted as question-passage pairs","GPU recommended for inference speed (CPU inference ~500ms per sample)"],"input_types":["text (question string)","text (passage/context string)","structured JSON with 'question' and 'context' fields"],"output_types":["structured JSON with 'answer' (extracted span), 'start_logit', 'end_logit', 'start_index', 'end_index'","confidence scores via softmax over token positions"],"categories":["search-retrieval","question-answering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-tau--splinter-base__cap_1","uri":"capability://memory.knowledge.passage.aware.contextual.encoding.with.attention.masking","name":"passage-aware contextual encoding with attention masking","description":"The model encodes question-passage pairs through stacked transformer layers with bidirectional self-attention, using segment embeddings to distinguish question tokens from passage tokens. Attention masking prevents the model from attending across question-passage boundaries inappropriately, and positional embeddings track token positions within the concatenated sequence. This architecture enables the model to build rich contextual representations where question semantics inform passage understanding.","intents":["encode question-passage pairs into aligned contextual representations","leverage bidirectional context to improve answer span prediction accuracy","implement semantic matching between questions and relevant passage regions","build dense retrieval systems that score passage relevance to queries"],"best_for":["developers building dense passage retrieval systems for QA pipelines","teams implementing semantic search over document collections","researchers fine-tuning extractive QA models on domain-specific corpora"],"limitations":["maximum sequence length typically 512 tokens; longer passages require truncation or sliding-window approaches","attention computation is O(n²) in sequence length, causing quadratic slowdown on very long passages","segment embeddings assume binary question/passage split; doesn't natively handle multi-document scenarios","no built-in mechanism for handling passages longer than model's training context window"],"requires":["transformers library with SplinterForQuestionAnswering class","input sequences must be tokenized with special tokens [CLS], [SEP], [PAD]","token_type_ids (segment IDs) must be provided to distinguish question from passage","attention_mask tensor to handle variable-length inputs in batches"],"input_types":["tokenized input_ids (integer tensor, shape [batch_size, seq_length])","token_type_ids (integer tensor marking question vs passage segments)","attention_mask (binary tensor masking padding tokens)"],"output_types":["contextual embeddings (hidden states from final transformer layer)","start/end logits (unnormalized scores for each token position)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-tau--splinter-base__cap_2","uri":"capability://code.generation.editing.fine.tuning.on.extractive.qa.datasets.with.span.based.loss","name":"fine-tuning on extractive qa datasets with span-based loss","description":"Splinter can be fine-tuned on extractive QA datasets (SQuAD, Natural Questions, etc.) using a span-based loss function that independently predicts start and end token positions. The training objective minimizes cross-entropy loss for both start and end position predictions, allowing the model to learn task-specific answer span patterns. The model supports standard PyTorch training loops with HuggingFace Trainer API, enabling domain adaptation without architectural changes.","intents":["adapt Splinter to domain-specific QA tasks (medical, legal, financial documents)","improve answer extraction accuracy on proprietary datasets","transfer knowledge from public QA benchmarks to private corpora","implement active learning pipelines that iteratively improve on hard examples"],"best_for":["teams with labeled QA datasets (100+ examples minimum for meaningful fine-tuning)","organizations building vertical-specific QA systems (healthcare, legal tech)","researchers experimenting with domain adaptation and transfer learning"],"limitations":["requires manually annotated span labels (start/end token indices) in training data","span-based loss assumes single contiguous answer spans; doesn't handle multiple disjoint answers","fine-tuning on small datasets (<500 examples) risks overfitting without careful regularization","no built-in support for multi-answer scenarios where multiple valid spans exist"],"requires":["PyTorch 1.9+","transformers library with Trainer class","training data in SQuAD-format JSON or HuggingFace Dataset format","GPU with 8GB+ VRAM for batch training (batch_size=16-32)","learning rate typically 2e-5 to 5e-5 for fine-tuning"],"input_types":["JSON dataset with 'question', 'context', 'answers' (list of {'text': str, 'answer_start': int})","HuggingFace Dataset object with 'input_ids', 'token_type_ids', 'start_positions', 'end_positions'"],"output_types":["fine-tuned model checkpoint (PyTorch state_dict)","training metrics (loss, F1, exact match scores on validation set)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-tau--splinter-base__cap_3","uri":"capability://automation.workflow.batch.inference.with.dynamic.padding.and.variable.length.handling","name":"batch inference with dynamic padding and variable-length handling","description":"Splinter supports efficient batch inference through HuggingFace's tokenizer and model APIs, which automatically handle variable-length sequences via dynamic padding and attention masking. The model processes multiple question-passage pairs in parallel, padding shorter sequences to the longest in the batch and masking padding tokens to prevent attention computation on them. This design enables GPU utilization efficiency while maintaining correctness across variable-length inputs.","intents":["process multiple QA requests in parallel for throughput optimization","implement batch inference pipelines for document processing workflows","build API endpoints that handle concurrent QA requests efficiently","optimize GPU utilization when processing large document collections"],"best_for":["production QA systems handling high-throughput inference (100+ requests/sec)","batch document processing pipelines (indexing, knowledge extraction)","teams deploying Splinter on cloud infrastructure (AWS SageMaker, Azure ML, HuggingFace Inference API)"],"limitations":["batch size is constrained by GPU memory; typical max batch_size=32-64 on 8GB GPUs","dynamic padding adds overhead for highly variable-length sequences (e.g., 50-token vs 500-token passages)","no built-in support for streaming inference or online batching across requests","padding efficiency decreases when batch contains one very long sequence (forces all sequences to that length)"],"requires":["GPU with sufficient VRAM for batch_size × max_seq_length × hidden_dim computation","HuggingFace transformers AutoTokenizer for consistent tokenization","PyTorch DataLoader or equivalent for batching and shuffling","attention_mask tensor generation (handled automatically by tokenizer)"],"input_types":["list of question strings","list of passage strings (same length as questions)","optional: batch_size parameter (default 32)"],"output_types":["batched start_logits tensor (shape [batch_size, seq_length])","batched end_logits tensor (shape [batch_size, seq_length])","extracted answers with confidence scores for each sample in batch"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-tau--splinter-base__cap_4","uri":"capability://automation.workflow.model.deployment.to.cloud.inference.endpoints.with.standardized.apis","name":"model deployment to cloud inference endpoints with standardized apis","description":"Splinter is compatible with HuggingFace Inference API, Azure ML, and AWS SageMaker endpoints, enabling one-click deployment without custom containerization. The model follows the standard HuggingFace pipeline interface, allowing inference through REST APIs with automatic request/response serialization. Deployment handles model loading, batching, and GPU allocation transparently, abstracting infrastructure complexity from users.","intents":["deploy Splinter as a managed inference service without DevOps overhead","expose QA capabilities via REST API for web/mobile applications","scale inference horizontally across multiple GPU instances","integrate Splinter into existing cloud ML platforms (Azure, AWS, GCP)"],"best_for":["teams without ML infrastructure expertise seeking managed deployment","startups and small teams avoiding Kubernetes/Docker complexity","organizations requiring auto-scaling and high-availability QA services"],"limitations":["HuggingFace Inference API has rate limits (free tier: 30k requests/month)","cloud endpoint latency includes network round-trip time (~50-200ms) plus inference","custom preprocessing or postprocessing logic requires custom endpoint code","vendor lock-in: migrating between cloud providers requires re-deployment"],"requires":["HuggingFace account with API token (for HF Inference API)","Azure subscription and ML workspace (for Azure ML deployment)","AWS account with SageMaker permissions (for SageMaker endpoints)","model must be public on HuggingFace Hub or in private organization"],"input_types":["JSON payload with 'question' and 'context' fields","HTTP POST request to endpoint URL"],"output_types":["JSON response with 'answer', 'score', 'start', 'end' fields","HTTP 200 with inference results or HTTP 503 if service overloaded"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":37,"verified":false,"data_access_risk":"low","permissions":["PyTorch 1.9+","transformers library 4.0+","input passages must be pre-tokenized and formatted as question-passage pairs","GPU recommended for inference speed (CPU inference ~500ms per sample)","transformers library with SplinterForQuestionAnswering class","input sequences must be tokenized with special tokens [CLS], [SEP], [PAD]","token_type_ids (segment IDs) must be provided to distinguish question from passage","attention_mask tensor to handle variable-length inputs in batches","transformers library with Trainer class","training data in SQuAD-format JSON or HuggingFace Dataset format"],"failure_modes":["cannot answer questions when the answer doesn't appear verbatim in the passage","struggles with multi-hop reasoning requiring synthesis across distant text segments","performance degrades on paraphrased or implicit answers not directly stated in source","limited to English language tasks; no multilingual variant documented","maximum sequence length typically 512 tokens; longer passages require truncation or sliding-window approaches","attention computation is O(n²) in sequence length, causing quadratic slowdown on very long passages","segment embeddings assume binary question/passage split; doesn't natively handle multi-document scenarios","no built-in mechanism for handling passages longer than model's training context window","requires manually annotated span labels (start/end token indices) in training data","span-based loss assumes single contiguous answer spans; doesn't handle multiple disjoint answers","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.46706756264164206,"quality":0.2,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.766Z","last_scraped_at":"2026-05-03T14:22:55.335Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":83018,"model_likes":1}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=tau--splinter-base","compare_url":"https://unfragile.ai/compare?artifact=tau--splinter-base"}},"signature":"528YfOGn3HRqV7q/yzEVozLr3+L6vH816xz+T661jP58vvDy6zy5Gvw0PGJe+qNQQgXhm/P8lNxfKuLiz8GxBQ==","signedAt":"2026-06-21T00:53:02.724Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/tau--splinter-base","artifact":"https://unfragile.ai/tau--splinter-base","verify":"https://unfragile.ai/api/v1/verify?slug=tau--splinter-base","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}