{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:TWL6NWHKS6UQCHGCH53PPHVE65","short_pith_number":"pith:TWL6NWHK","schema_version":"1.0","canonical_sha256":"9d97e6d8ea97a9011cc23f76f79ea4f7798fc93b076f53ad3853616caed72741","source":{"kind":"arxiv","id":"2109.04380","version":2},"attestation_state":"computed","paper":{"title":"ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Chaochen Gao, Jizhong Han, Liangjun Zang, Songlin Hu, Xing Wu, Zhongyuan Wang","submitted_at":"2021-09-09T16:07:31Z","abstract_excerpt":"Contrastive learning has been attracting much attention for learning unsupervised sentence embeddings. The current state-of-the-art unsupervised method is the unsupervised SimCSE (unsup-SimCSE). Unsup-SimCSE takes dropout as a minimal data augmentation method, and passes the same input sentence to a pre-trained Transformer encoder (with dropout turned on) twice to obtain the two corresponding embeddings to build a positive pair. As the length information of a sentence will generally be encoded into the sentence embeddings due to the usage of position embedding in Transformer, each positive pai"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2109.04380","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-09-09T16:07:31Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c6cd68765bd985d36c651a2c1c51946dc0c5b5fb54b1806294aa5c60d4eb24c1","abstract_canon_sha256":"7a7294235dee401219cfca2d7f8e880d42f32b5810eea4afa61fe8bd2a2c49c4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:56:11.899088Z","signature_b64":"lXen7J4NpTPftTv0QY9vlpmUS81eeXe1qcpzDU5QhreAlt9EwCwvgxx98E7HuPaJbdDKV6cz4KJSA/LOnhAADw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9d97e6d8ea97a9011cc23f76f79ea4f7798fc93b076f53ad3853616caed72741","last_reissued_at":"2026-07-05T04:56:11.898730Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:56:11.898730Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Chaochen Gao, Jizhong Han, Liangjun Zang, Songlin Hu, Xing Wu, Zhongyuan Wang","submitted_at":"2021-09-09T16:07:31Z","abstract_excerpt":"Contrastive learning has been attracting much attention for learning unsupervised sentence embeddings. The current state-of-the-art unsupervised method is the unsupervised SimCSE (unsup-SimCSE). Unsup-SimCSE takes dropout as a minimal data augmentation method, and passes the same input sentence to a pre-trained Transformer encoder (with dropout turned on) twice to obtain the two corresponding embeddings to build a positive pair. As the length information of a sentence will generally be encoded into the sentence embeddings due to the usage of position embedding in Transformer, each positive pai"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.04380","kind":"arxiv","version":2},"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/2109.04380/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2109.04380","created_at":"2026-07-05T04:56:11.898787+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.04380v2","created_at":"2026-07-05T04:56:11.898787+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.04380","created_at":"2026-07-05T04:56:11.898787+00:00"},{"alias_kind":"pith_short_12","alias_value":"TWL6NWHKS6UQ","created_at":"2026-07-05T04:56:11.898787+00:00"},{"alias_kind":"pith_short_16","alias_value":"TWL6NWHKS6UQCHGC","created_at":"2026-07-05T04:56:11.898787+00:00"},{"alias_kind":"pith_short_8","alias_value":"TWL6NWHK","created_at":"2026-07-05T04:56:11.898787+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TWL6NWHKS6UQCHGCH53PPHVE65","json":"https://pith.science/pith/TWL6NWHKS6UQCHGCH53PPHVE65.json","graph_json":"https://pith.science/api/pith-number/TWL6NWHKS6UQCHGCH53PPHVE65/graph.json","events_json":"https://pith.science/api/pith-number/TWL6NWHKS6UQCHGCH53PPHVE65/events.json","paper":"https://pith.science/paper/TWL6NWHK"},"agent_actions":{"view_html":"https://pith.science/pith/TWL6NWHKS6UQCHGCH53PPHVE65","download_json":"https://pith.science/pith/TWL6NWHKS6UQCHGCH53PPHVE65.json","view_paper":"https://pith.science/paper/TWL6NWHK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.04380&json=true","fetch_graph":"https://pith.science/api/pith-number/TWL6NWHKS6UQCHGCH53PPHVE65/graph.json","fetch_events":"https://pith.science/api/pith-number/TWL6NWHKS6UQCHGCH53PPHVE65/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TWL6NWHKS6UQCHGCH53PPHVE65/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TWL6NWHKS6UQCHGCH53PPHVE65/action/storage_attestation","attest_author":"https://pith.science/pith/TWL6NWHKS6UQCHGCH53PPHVE65/action/author_attestation","sign_citation":"https://pith.science/pith/TWL6NWHKS6UQCHGCH53PPHVE65/action/citation_signature","submit_replication":"https://pith.science/pith/TWL6NWHKS6UQCHGCH53PPHVE65/action/replication_record"}},"created_at":"2026-07-05T04:56:11.898787+00:00","updated_at":"2026-07-05T04:56:11.898787+00:00"}