{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-ahotrod--electra_large_discriminator_squad2_512","slug":"ahotrod--electra_large_discriminator_squad2_512","name":"electra_large_discriminator_squad2_512","type":"model","url":"https://huggingface.co/ahotrod/electra_large_discriminator_squad2_512","page_url":"https://unfragile.ai/ahotrod--electra_large_discriminator_squad2_512","categories":["model-training"],"tags":["transformers","pytorch","tf","electra","question-answering","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-ahotrod--electra_large_discriminator_squad2_512__cap_0","uri":"capability://search.retrieval.extractive.question.answering.on.squad.2.0.format","name":"extractive question-answering on squad 2.0 format","description":"Performs span-based extractive QA by identifying start and end token positions within a given passage using the ELECTRA discriminator architecture fine-tuned on SQuAD 2.0 dataset. The model uses bidirectional transformer attention to contextualize tokens and outputs logits for each token position, enabling extraction of answer spans directly from input text without generation. Handles unanswerable questions through a no-answer classification head trained on SQuAD 2.0's adversarial examples.","intents":["Extract direct answers from documents or passages when the answer text appears verbatim in the source","Build reading comprehension systems that cite exact text locations for answers","Deploy QA systems that must handle adversarial no-answer cases in production","Create fact-checking pipelines that validate claims against reference documents"],"best_for":["Teams building document-based QA systems with strict answer provenance requirements","Developers needing efficient inference for reading comprehension at scale","Organizations requiring models trained on adversarial QA datasets (SQuAD 2.0)"],"limitations":["Cannot generate answers not present in the input passage — only extracts existing spans","Requires passage length ≤512 tokens due to ELECTRA-large's context window, necessitating document chunking for longer texts","No built-in multi-hop reasoning — answers must be contained within a single passage","Performance degrades on out-of-domain text significantly different from SQuAD 2.0 distribution","Unanswerable question detection relies on SQuAD 2.0 adversarial patterns and may not generalize to other no-answer scenarios"],"requires":["PyTorch 1.9+ or TensorFlow 2.4+ runtime","Transformers library 4.0+","Minimum 2GB GPU VRAM for inference (batch_size=1)","Input text tokenized to ≤512 subword tokens using ELECTRA's WordPiece tokenizer"],"input_types":["text (question string)","text (passage/context string)","structured data (question-passage pairs in SQuAD format)"],"output_types":["structured data (start token index, end token index, answer span text)","structured data (no-answer probability score)","structured data (per-token logits for start/end positions)"],"categories":["search-retrieval","question-answering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-ahotrod--electra_large_discriminator_squad2_512__cap_1","uri":"capability://data.processing.analysis.token.level.span.prediction.with.logit.output","name":"token-level span prediction with logit output","description":"Outputs raw logits for start and end token positions across the entire input sequence, enabling downstream applications to implement custom decoding strategies. The model computes a dense vector of shape [sequence_length] for both start and end positions, allowing consumers to apply temperature scaling, beam search, or constrained decoding without retraining. This architectural choice exposes the model's confidence scores directly rather than post-processing them.","intents":["Implement custom answer span selection logic with domain-specific constraints or post-processing","Build ensemble systems that combine logits from multiple QA models","Perform uncertainty quantification by analyzing logit distributions across candidate spans","Create interactive QA systems where users can explore alternative answer spans ranked by model confidence"],"best_for":["ML engineers building production QA pipelines with custom inference logic","Researchers studying model confidence and calibration in reading comprehension","Teams requiring fine-grained control over answer selection beyond argmax"],"limitations":["Raw logits require post-processing (softmax, argmax) by the consumer — no built-in answer extraction","Logit values are not calibrated probabilities and may require temperature scaling for reliable confidence estimates","No beam search or constrained decoding built into the model — must be implemented downstream","Span selection logic (ensuring start_idx ≤ end_idx) is not enforced by the model and must be handled by the consumer"],"requires":["Transformers library 4.0+ with HuggingFace model loading","Post-processing code to convert logits to answer spans","Understanding of softmax and argmax operations for decoding"],"input_types":["text (tokenized question-passage pair)"],"output_types":["structured data (logits tensor of shape [batch_size, sequence_length] for start positions)","structured data (logits tensor of shape [batch_size, sequence_length] for end positions)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-ahotrod--electra_large_discriminator_squad2_512__cap_2","uri":"capability://safety.moderation.adversarial.no.answer.detection.via.binary.classification.head","name":"adversarial no-answer detection via binary classification head","description":"Includes a specialized classification head trained on SQuAD 2.0's adversarial no-answer examples to predict whether a given question-passage pair has an answerable question or not. This head operates on the [CLS] token representation and outputs a binary classification score, enabling the model to reject unanswerable questions rather than extracting spurious spans. The training process explicitly balances answerable vs. unanswerable examples from SQuAD 2.0.","intents":["Detect when a question cannot be answered from the provided passage and return null/no-answer","Build QA systems that avoid hallucinating answers when information is absent","Implement confidence thresholds that reject low-confidence predictions as unanswerable","Create fact-checking pipelines that distinguish between 'answer not found' and 'answer found but low confidence'"],"best_for":["Production QA systems where false positives (extracting wrong answers) are costly","Teams building customer-facing search or documentation systems requiring high precision","Applications in legal, medical, or financial domains where answer accuracy is critical"],"limitations":["No-answer detection is trained only on SQuAD 2.0 adversarial patterns and may not generalize to other types of unanswerable questions","Binary classification head does not provide granular confidence scores — only a single no-answer probability","Cannot distinguish between 'answer not in passage' vs. 'question is malformed' — treats both as unanswerable","Requires careful threshold tuning to balance precision vs. recall for no-answer detection in production"],"requires":["SQuAD 2.0 format input (question-passage pairs)","Post-processing logic to apply no-answer threshold (typically 0.5 or tuned per domain)"],"input_types":["text (question string)","text (passage string)"],"output_types":["structured data (no-answer probability score between 0 and 1)","structured data (boolean flag indicating answerability)"],"categories":["safety-moderation","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-ahotrod--electra_large_discriminator_squad2_512__cap_3","uri":"capability://data.processing.analysis.electra.discriminator.based.contextual.encoding","name":"electra discriminator-based contextual encoding","description":"Uses ELECTRA's discriminator architecture (trained via replaced token detection rather than masked language modeling) to encode question-passage pairs into contextualized token representations. The discriminator learns to detect tokens that have been replaced by a generator, resulting in more efficient pretraining and better fine-tuning performance on downstream tasks. This encoding is applied to the full input sequence, enabling the model to capture long-range dependencies within the 512-token context window.","intents":["Leverage more parameter-efficient pretraining approach for faster fine-tuning on custom QA datasets","Benefit from improved contextual representations compared to standard masked language modeling","Deploy QA models with better performance-to-parameter ratio for resource-constrained environments"],"best_for":["Teams fine-tuning QA models on domain-specific datasets with limited compute budgets","Researchers studying alternative pretraining objectives beyond masked language modeling","Organizations deploying QA models on edge devices or cost-sensitive cloud infrastructure"],"limitations":["ELECTRA pretraining is less widely adopted than BERT, resulting in fewer community resources and fewer downstream task benchmarks","The discriminator architecture is optimized for token-level classification and may not transfer as well to generation tasks","No official multilingual ELECTRA models — this model is English-only","Requires understanding of ELECTRA's pretraining approach to effectively fine-tune on custom data"],"requires":["Transformers library 4.0+ with ELECTRA model support","Understanding of ELECTRA architecture and pretraining objectives for effective fine-tuning"],"input_types":["text (question-passage pair tokenized with ELECTRA's WordPiece tokenizer)"],"output_types":["structured data (contextualized token embeddings of shape [batch_size, sequence_length, 1024])"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-ahotrod--electra_large_discriminator_squad2_512__cap_4","uri":"capability://automation.workflow.batch.inference.with.configurable.sequence.length","name":"batch inference with configurable sequence length","description":"Supports batched inference on multiple question-passage pairs simultaneously, with fixed input length of 512 tokens enforced at the tokenization stage. The model processes batches through the transformer encoder in parallel, enabling efficient GPU utilization. Input sequences longer than 512 tokens are truncated, and shorter sequences are padded with [PAD] tokens, with attention masks applied to ignore padding during computation.","intents":["Process multiple QA queries in parallel for improved throughput in production systems","Implement efficient document chunking pipelines that split long documents into 512-token passages","Build batch inference servers that maximize GPU utilization across multiple concurrent requests"],"best_for":["Production QA systems handling high query volumes with batch processing","Teams building document processing pipelines that require passage-level QA","Organizations optimizing inference cost by batching requests on GPU infrastructure"],"limitations":["Fixed 512-token context window requires explicit document chunking for longer texts, potentially splitting answer spans across chunks","Batch processing requires all sequences to be padded to the same length, wasting computation on shorter sequences","No dynamic batching — batch size must be fixed at inference time","Truncation of sequences longer than 512 tokens may lose relevant context needed to answer questions"],"requires":["PyTorch or TensorFlow runtime with batch processing support","Document chunking logic to handle passages longer than 512 tokens","Attention mask generation for variable-length sequences within a batch"],"input_types":["structured data (batch of question-passage pairs)"],"output_types":["structured data (batch of start/end logits and no-answer scores)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-ahotrod--electra_large_discriminator_squad2_512__cap_5","uri":"capability://tool.use.integration.huggingface.transformers.integration.with.model.hub.deployment","name":"huggingface transformers integration with model hub deployment","description":"Fully integrated with the HuggingFace Transformers library and model hub, enabling one-line model loading via `AutoModelForQuestionAnswering.from_pretrained()` and automatic tokenizer configuration. The model is deployed on HuggingFace's CDN with support for both PyTorch and TensorFlow backends, and includes inference API endpoints compatible with Azure and other cloud providers. Model weights are versioned and cached locally after first download.","intents":["Quickly prototype QA systems without downloading model weights manually","Deploy QA models to production using HuggingFace's managed inference endpoints","Switch between PyTorch and TensorFlow backends without code changes","Access model documentation, training details, and community discussions on the HuggingFace hub"],"best_for":["Developers building QA systems with HuggingFace Transformers ecosystem","Teams deploying models on Azure or other cloud providers with HuggingFace integration","Researchers and practitioners who want minimal setup overhead"],"limitations":["Requires internet connection for first-time model download (857MB+ for ELECTRA-large)","Model caching directory must have sufficient disk space (~2GB for model + tokenizer)","HuggingFace API rate limits may apply for high-volume inference requests","No built-in model versioning control — always downloads the latest version unless explicitly pinned"],"requires":["Python 3.6+","Transformers library 4.0+","PyTorch 1.9+ or TensorFlow 2.4+","Internet connection for model download","~2GB disk space for model weights and tokenizer"],"input_types":["text (question and passage strings)"],"output_types":["structured data (HuggingFace QuestionAnsweringModelOutput object with logits and no-answer scores)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":46,"verified":false,"data_access_risk":"high","permissions":["PyTorch 1.9+ or TensorFlow 2.4+ runtime","Transformers library 4.0+","Minimum 2GB GPU VRAM for inference (batch_size=1)","Input text tokenized to ≤512 subword tokens using ELECTRA's WordPiece tokenizer","Transformers library 4.0+ with HuggingFace model loading","Post-processing code to convert logits to answer spans","Understanding of softmax and argmax operations for decoding","SQuAD 2.0 format input (question-passage pairs)","Post-processing logic to apply no-answer threshold (typically 0.5 or tuned per domain)","Transformers library 4.0+ with ELECTRA model support"],"failure_modes":["Cannot generate answers not present in the input passage — only extracts existing spans","Requires passage length ≤512 tokens due to ELECTRA-large's context window, necessitating document chunking for longer texts","No built-in multi-hop reasoning — answers must be contained within a single passage","Performance degrades on out-of-domain text significantly different from SQuAD 2.0 distribution","Unanswerable question detection relies on SQuAD 2.0 adversarial patterns and may not generalize to other no-answer scenarios","Raw logits require post-processing (softmax, argmax) by the consumer — no built-in answer extraction","Logit values are not calibrated probabilities and may require temperature scaling for reliable confidence estimates","No beam search or constrained decoding built into the model — must be implemented downstream","Span selection logic (ensuring start_idx ≤ end_idx) is not enforced by the model and must be handled by the consumer","No-answer detection is trained only on SQuAD 2.0 adversarial patterns and may not generalize to other types of unanswerable questions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6399535408394161,"quality":0.37,"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":"2026-05-24T12:16:22.764Z","last_scraped_at":"2026-05-03T14:22:55.335Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":899590,"model_likes":7}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=ahotrod--electra_large_discriminator_squad2_512","compare_url":"https://unfragile.ai/compare?artifact=ahotrod--electra_large_discriminator_squad2_512"}},"signature":"ewNIyacvBOsXmkgq9xwSZ07hQ4sI6pjSHY0ulukQhvm5F/dqiSPs/HvTUTP3p/IUwjs063jAubXzS4h/spIzCA==","signedAt":"2026-06-20T02:43:32.852Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ahotrod--electra_large_discriminator_squad2_512","artifact":"https://unfragile.ai/ahotrod--electra_large_discriminator_squad2_512","verify":"https://unfragile.ai/api/v1/verify?slug=ahotrod--electra_large_discriminator_squad2_512","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"}}