{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:M4QFFR7QJTHVHDFIZSMI2AB3W7","short_pith_number":"pith:M4QFFR7Q","canonical_record":{"source":{"id":"2501.13758","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-01-23T15:36:35Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"5e92402e15dc4f597e9a64b2e7910d58dc2193da9fe25fb07579dd716d906c12","abstract_canon_sha256":"55afdb940f83928ca5f6402fc4074f10d67b3422eda7c803ad2ecf0d88178467"},"schema_version":"1.0"},"canonical_sha256":"672052c7f04ccf538ca8cc988d003bb7e4bfde9e1cc064b56b7f4e1fbb35e6cc","source":{"kind":"arxiv","id":"2501.13758","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.13758","created_at":"2026-07-05T10:04:33Z"},{"alias_kind":"arxiv_version","alias_value":"2501.13758v1","created_at":"2026-07-05T10:04:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.13758","created_at":"2026-07-05T10:04:33Z"},{"alias_kind":"pith_short_12","alias_value":"M4QFFR7QJTHV","created_at":"2026-07-05T10:04:33Z"},{"alias_kind":"pith_short_16","alias_value":"M4QFFR7QJTHVHDFI","created_at":"2026-07-05T10:04:33Z"},{"alias_kind":"pith_short_8","alias_value":"M4QFFR7Q","created_at":"2026-07-05T10:04:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:M4QFFR7QJTHVHDFIZSMI2AB3W7","target":"record","payload":{"canonical_record":{"source":{"id":"2501.13758","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-01-23T15:36:35Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"5e92402e15dc4f597e9a64b2e7910d58dc2193da9fe25fb07579dd716d906c12","abstract_canon_sha256":"55afdb940f83928ca5f6402fc4074f10d67b3422eda7c803ad2ecf0d88178467"},"schema_version":"1.0"},"canonical_sha256":"672052c7f04ccf538ca8cc988d003bb7e4bfde9e1cc064b56b7f4e1fbb35e6cc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:04:33.641737Z","signature_b64":"THidzCWFu2XAEJgETlTKxAURHWjRUiaZu0g2g+6qCHU4R2mHyhXVA/fnFn4ZGJn9f55IiRf39nitFfxx8WNRDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"672052c7f04ccf538ca8cc988d003bb7e4bfde9e1cc064b56b7f4e1fbb35e6cc","last_reissued_at":"2026-07-05T10:04:33.641255Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:04:33.641255Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.13758","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":"2026-07-05T10:04:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9DgtiXzprumycZaE3rynYSxFDFICqWozv8igDJ3Tpj+wJ3dVfm6Z2MVma4ts39WG2L3bCV1oUWHolH5OA7mdDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T17:50:00.065285Z"},"content_sha256":"65a0b0fc32367044cc8c3cda1021a2c231597cb6aab172b71cce87719bba8c44","schema_version":"1.0","event_id":"sha256:65a0b0fc32367044cc8c3cda1021a2c231597cb6aab172b71cce87719bba8c44"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:M4QFFR7QJTHVHDFIZSMI2AB3W7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"2-Tier SimCSE: Elevating BERT for Robust Sentence Embeddings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Junjin Wang, Yumeng Wang, Ziran Zhou","submitted_at":"2025-01-23T15:36:35Z","abstract_excerpt":"Effective sentence embeddings that capture semantic nuances and generalize well across diverse contexts are crucial for natural language processing tasks. We address this challenge by applying SimCSE (Simple Contrastive Learning of Sentence Embeddings) using contrastive learning to fine-tune the minBERT model for sentiment analysis, semantic textual similarity (STS), and paraphrase detection. Our contributions include experimenting with three different dropout techniques, namely standard dropout, curriculum dropout, and adaptive dropout, to tackle overfitting, proposing a novel 2-Tier SimCSE F"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.13758","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/2501.13758/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":"2026-07-05T10:04:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QFIcrkjN/Kmw1W3VLGb0mBnaTDJjmHQyJ8mYKf3d/PQ/fk+vv/oPJmSI50qLnq7PPA+jUNs3CxSfBtIMuf9ABQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T17:50:00.065871Z"},"content_sha256":"ebbeb7750eefd814d621b457ec3fda0078ffcaa9b9108096c557cc708cb0bc8c","schema_version":"1.0","event_id":"sha256:ebbeb7750eefd814d621b457ec3fda0078ffcaa9b9108096c557cc708cb0bc8c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/M4QFFR7QJTHVHDFIZSMI2AB3W7/bundle.json","state_url":"https://pith.science/pith/M4QFFR7QJTHVHDFIZSMI2AB3W7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/M4QFFR7QJTHVHDFIZSMI2AB3W7/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":"2026-08-18T17:50:00Z","links":{"resolver":"https://pith.science/pith/M4QFFR7QJTHVHDFIZSMI2AB3W7","bundle":"https://pith.science/pith/M4QFFR7QJTHVHDFIZSMI2AB3W7/bundle.json","state":"https://pith.science/pith/M4QFFR7QJTHVHDFIZSMI2AB3W7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/M4QFFR7QJTHVHDFIZSMI2AB3W7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:M4QFFR7QJTHVHDFIZSMI2AB3W7","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":"55afdb940f83928ca5f6402fc4074f10d67b3422eda7c803ad2ecf0d88178467","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-01-23T15:36:35Z","title_canon_sha256":"5e92402e15dc4f597e9a64b2e7910d58dc2193da9fe25fb07579dd716d906c12"},"schema_version":"1.0","source":{"id":"2501.13758","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.13758","created_at":"2026-07-05T10:04:33Z"},{"alias_kind":"arxiv_version","alias_value":"2501.13758v1","created_at":"2026-07-05T10:04:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.13758","created_at":"2026-07-05T10:04:33Z"},{"alias_kind":"pith_short_12","alias_value":"M4QFFR7QJTHV","created_at":"2026-07-05T10:04:33Z"},{"alias_kind":"pith_short_16","alias_value":"M4QFFR7QJTHVHDFI","created_at":"2026-07-05T10:04:33Z"},{"alias_kind":"pith_short_8","alias_value":"M4QFFR7Q","created_at":"2026-07-05T10:04:33Z"}],"graph_snapshots":[{"event_id":"sha256:ebbeb7750eefd814d621b457ec3fda0078ffcaa9b9108096c557cc708cb0bc8c","target":"graph","created_at":"2026-07-05T10:04:33Z","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/2501.13758/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Effective sentence embeddings that capture semantic nuances and generalize well across diverse contexts are crucial for natural language processing tasks. We address this challenge by applying SimCSE (Simple Contrastive Learning of Sentence Embeddings) using contrastive learning to fine-tune the minBERT model for sentiment analysis, semantic textual similarity (STS), and paraphrase detection. Our contributions include experimenting with three different dropout techniques, namely standard dropout, curriculum dropout, and adaptive dropout, to tackle overfitting, proposing a novel 2-Tier SimCSE F","authors_text":"Junjin Wang, Yumeng Wang, Ziran Zhou","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-01-23T15:36:35Z","title":"2-Tier SimCSE: Elevating BERT for Robust Sentence Embeddings"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.13758","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":"sha256:65a0b0fc32367044cc8c3cda1021a2c231597cb6aab172b71cce87719bba8c44","target":"record","created_at":"2026-07-05T10:04:33Z","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":"55afdb940f83928ca5f6402fc4074f10d67b3422eda7c803ad2ecf0d88178467","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-01-23T15:36:35Z","title_canon_sha256":"5e92402e15dc4f597e9a64b2e7910d58dc2193da9fe25fb07579dd716d906c12"},"schema_version":"1.0","source":{"id":"2501.13758","kind":"arxiv","version":1}},"canonical_sha256":"672052c7f04ccf538ca8cc988d003bb7e4bfde9e1cc064b56b7f4e1fbb35e6cc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"672052c7f04ccf538ca8cc988d003bb7e4bfde9e1cc064b56b7f4e1fbb35e6cc","first_computed_at":"2026-07-05T10:04:33.641255Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:04:33.641255Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"THidzCWFu2XAEJgETlTKxAURHWjRUiaZu0g2g+6qCHU4R2mHyhXVA/fnFn4ZGJn9f55IiRf39nitFfxx8WNRDA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:04:33.641737Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.13758","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:65a0b0fc32367044cc8c3cda1021a2c231597cb6aab172b71cce87719bba8c44","sha256:ebbeb7750eefd814d621b457ec3fda0078ffcaa9b9108096c557cc708cb0bc8c"],"state_sha256":"3b045d5b1014b5107932dc029e95cb3cd6b9bd60233a39c4d2befe05e00b02cb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TCc3xt1bakmW8vhWy5Jlih335KtZe8xAxOUQnXHjDsTmXT7jxFUHyA7qLK9GzPhhZUey1bqsRPx70l82MIyoAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T17:50:00.069382Z","bundle_sha256":"d18c66d103a45c8e08a1c66be0087f9e010104ae28556b58019dc6e3f11b0683"}}