{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:7G2S4QVOLWZVOOOWWIYN7FE43R","short_pith_number":"pith:7G2S4QVO","canonical_record":{"source":{"id":"2110.09665","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-10-19T00:15:19Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b383e2bcbbdc911ef1dd34e3d91cdbacd1cd747af37c9bfa97111b0aad1421ff","abstract_canon_sha256":"ede2dbcaec6236d4ef6b8ce445befb81c203909cf2e5a395d02031f0e2c15d51"},"schema_version":"1.0"},"canonical_sha256":"f9b52e42ae5db35739d6b230df949cdc5ab816a20b4ccc41e8cd66867190cead","source":{"kind":"arxiv","id":"2110.09665","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.09665","created_at":"2026-07-05T03:23:57Z"},{"alias_kind":"arxiv_version","alias_value":"2110.09665v1","created_at":"2026-07-05T03:23:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.09665","created_at":"2026-07-05T03:23:57Z"},{"alias_kind":"pith_short_12","alias_value":"7G2S4QVOLWZV","created_at":"2026-07-05T03:23:57Z"},{"alias_kind":"pith_short_16","alias_value":"7G2S4QVOLWZVOOOW","created_at":"2026-07-05T03:23:57Z"},{"alias_kind":"pith_short_8","alias_value":"7G2S4QVO","created_at":"2026-07-05T03:23:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:7G2S4QVOLWZVOOOWWIYN7FE43R","target":"record","payload":{"canonical_record":{"source":{"id":"2110.09665","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-10-19T00:15:19Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b383e2bcbbdc911ef1dd34e3d91cdbacd1cd747af37c9bfa97111b0aad1421ff","abstract_canon_sha256":"ede2dbcaec6236d4ef6b8ce445befb81c203909cf2e5a395d02031f0e2c15d51"},"schema_version":"1.0"},"canonical_sha256":"f9b52e42ae5db35739d6b230df949cdc5ab816a20b4ccc41e8cd66867190cead","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:23:57.657518Z","signature_b64":"B+R01hZxuRbVRJd/HPLSFsyIYescBmQtKLs++UZOn7/z9lz354ZhulsCRh3IzIxQ9R0eSmQ8Hn7rAkO/FoL+Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f9b52e42ae5db35739d6b230df949cdc5ab816a20b4ccc41e8cd66867190cead","last_reissued_at":"2026-07-05T03:23:57.657039Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:23:57.657039Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2110.09665","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:23:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Y9Zxk1wFsfoAFJPdFZNgVbTSOzKR2sC4PEnzE861DDb67uJZbUmuhDgpClNLVT3QQs58FRwoFycyiy6gBmGHAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T08:46:36.385002Z"},"content_sha256":"a6adce3bce16167cb3b12b33eda2e14939c20cca8ca684272d5ad627d172bd00","schema_version":"1.0","event_id":"sha256:a6adce3bce16167cb3b12b33eda2e14939c20cca8ca684272d5ad627d172bd00"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:7G2S4QVOLWZVOOOWWIYN7FE43R","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Ensemble ALBERT on SQuAD 2.0","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Renee Li, Shilun Li, Veronica Peng","submitted_at":"2021-10-19T00:15:19Z","abstract_excerpt":"Machine question answering is an essential yet challenging task in natural language processing. Recently, Pre-trained Contextual Embeddings (PCE) models like Bidirectional Encoder Representations from Transformers (BERT) and A Lite BERT (ALBERT) have attracted lots of attention due to their great performance in a wide range of NLP tasks. In our Paper, we utilized the fine-tuned ALBERT models and implemented combinations of additional layers (e.g. attention layer, RNN layer) on top of them to improve model performance on Stanford Question Answering Dataset (SQuAD 2.0). We implemented four diffe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.09665","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2110.09665/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:23:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sb4SiytecXUQ1ZaYjwDcwXf1sqajSxMG9DhAM6cYU66NS6mzWcdFDrls95QctQKvLwV6V2MeZDWFJfDUjCFlDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T08:46:36.385686Z"},"content_sha256":"a7de9fd76da9bdf336e8c96b7d293d26212b7de8a9dee4db8bf6d8d6850c9e85","schema_version":"1.0","event_id":"sha256:a7de9fd76da9bdf336e8c96b7d293d26212b7de8a9dee4db8bf6d8d6850c9e85"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7G2S4QVOLWZVOOOWWIYN7FE43R/bundle.json","state_url":"https://pith.science/pith/7G2S4QVOLWZVOOOWWIYN7FE43R/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7G2S4QVOLWZVOOOWWIYN7FE43R/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-08T08:46:36Z","links":{"resolver":"https://pith.science/pith/7G2S4QVOLWZVOOOWWIYN7FE43R","bundle":"https://pith.science/pith/7G2S4QVOLWZVOOOWWIYN7FE43R/bundle.json","state":"https://pith.science/pith/7G2S4QVOLWZVOOOWWIYN7FE43R/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7G2S4QVOLWZVOOOWWIYN7FE43R/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:7G2S4QVOLWZVOOOWWIYN7FE43R","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"ede2dbcaec6236d4ef6b8ce445befb81c203909cf2e5a395d02031f0e2c15d51","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-10-19T00:15:19Z","title_canon_sha256":"b383e2bcbbdc911ef1dd34e3d91cdbacd1cd747af37c9bfa97111b0aad1421ff"},"schema_version":"1.0","source":{"id":"2110.09665","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.09665","created_at":"2026-07-05T03:23:57Z"},{"alias_kind":"arxiv_version","alias_value":"2110.09665v1","created_at":"2026-07-05T03:23:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.09665","created_at":"2026-07-05T03:23:57Z"},{"alias_kind":"pith_short_12","alias_value":"7G2S4QVOLWZV","created_at":"2026-07-05T03:23:57Z"},{"alias_kind":"pith_short_16","alias_value":"7G2S4QVOLWZVOOOW","created_at":"2026-07-05T03:23:57Z"},{"alias_kind":"pith_short_8","alias_value":"7G2S4QVO","created_at":"2026-07-05T03:23:57Z"}],"graph_snapshots":[{"event_id":"sha256:a7de9fd76da9bdf336e8c96b7d293d26212b7de8a9dee4db8bf6d8d6850c9e85","target":"graph","created_at":"2026-07-05T03:23:57Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2110.09665/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Machine question answering is an essential yet challenging task in natural language processing. Recently, Pre-trained Contextual Embeddings (PCE) models like Bidirectional Encoder Representations from Transformers (BERT) and A Lite BERT (ALBERT) have attracted lots of attention due to their great performance in a wide range of NLP tasks. In our Paper, we utilized the fine-tuned ALBERT models and implemented combinations of additional layers (e.g. attention layer, RNN layer) on top of them to improve model performance on Stanford Question Answering Dataset (SQuAD 2.0). We implemented four diffe","authors_text":"Renee Li, Shilun Li, Veronica Peng","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-10-19T00:15:19Z","title":"Ensemble ALBERT on SQuAD 2.0"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.09665","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a6adce3bce16167cb3b12b33eda2e14939c20cca8ca684272d5ad627d172bd00","target":"record","created_at":"2026-07-05T03:23:57Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"ede2dbcaec6236d4ef6b8ce445befb81c203909cf2e5a395d02031f0e2c15d51","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-10-19T00:15:19Z","title_canon_sha256":"b383e2bcbbdc911ef1dd34e3d91cdbacd1cd747af37c9bfa97111b0aad1421ff"},"schema_version":"1.0","source":{"id":"2110.09665","kind":"arxiv","version":1}},"canonical_sha256":"f9b52e42ae5db35739d6b230df949cdc5ab816a20b4ccc41e8cd66867190cead","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f9b52e42ae5db35739d6b230df949cdc5ab816a20b4ccc41e8cd66867190cead","first_computed_at":"2026-07-05T03:23:57.657039Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:23:57.657039Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"B+R01hZxuRbVRJd/HPLSFsyIYescBmQtKLs++UZOn7/z9lz354ZhulsCRh3IzIxQ9R0eSmQ8Hn7rAkO/FoL+Bg==","signature_status":"signed_v1","signed_at":"2026-07-05T03:23:57.657518Z","signed_message":"canonical_sha256_bytes"},"source_id":"2110.09665","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a6adce3bce16167cb3b12b33eda2e14939c20cca8ca684272d5ad627d172bd00","sha256:a7de9fd76da9bdf336e8c96b7d293d26212b7de8a9dee4db8bf6d8d6850c9e85"],"state_sha256":"0774fafcdeae2edc15659da59deb354d7e142f814ccdbb618839950f54ab8b0a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YGyzafGmLYxMCipjPgzcG+rLvpVFUeZlkI/9ZgLSEiArqlVLFRddyzvAUh8NpsXu8FwOLPjrFTb94GIGSQabAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-08T08:46:36.389601Z","bundle_sha256":"def082208b4e89813d29a2eaae4a3a79c0034137eff3b36067ba78f5d2f36b69"}}