{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:IOCCICNYPHWUS2D72P3APN4D6K","short_pith_number":"pith:IOCCICNY","schema_version":"1.0","canonical_sha256":"43842409b879ed49687fd3f607b783f2b86a316f7f2fd19cddd86dec4c4cfc68","source":{"kind":"arxiv","id":"1909.05658","version":1},"attestation_state":"computed","paper":{"title":"UER: An Open-Source Toolkit for Pre-training Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Haotang Deng, Hui Chen, Jinbin Zhang, Qi Ju, Tao Liu, Wei Lu, Xiaoyong Du, Xi Chen, Xin Zhao, Zhe Zhao","submitted_at":"2019-09-12T13:46:58Z","abstract_excerpt":"Existing works, including ELMO and BERT, have revealed the importance of pre-training for NLP tasks. While there does not exist a single pre-training model that works best in all cases, it is of necessity to develop a framework that is able to deploy various pre-training models efficiently. For this purpose, we propose an assemble-on-demand pre-training toolkit, namely Universal Encoder Representations (UER). UER is loosely coupled, and encapsulated with rich modules. By assembling modules on demand, users can either reproduce a state-of-the-art pre-training model or develop a pre-training mod"},"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":"1909.05658","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-09-12T13:46:58Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"3d27eee090c51a6760b3daa0f31b531460e2affa2441605ca0df1ff7bdbcc298","abstract_canon_sha256":"e9b8e4e2d88f699cb9c40bf7bf0be1de21d4bffb6f9510bda31b2ed3bf32419f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:04:17.256408Z","signature_b64":"V9ZDnCjvkmMs2TZSg1YSbwF6ND0VNhG7DjfH0UrD9vfACkyszDUi4XpHsPbwLATE9ZSifHsd2M1otJ27ewjqBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"43842409b879ed49687fd3f607b783f2b86a316f7f2fd19cddd86dec4c4cfc68","last_reissued_at":"2026-07-05T00:04:17.255999Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:04:17.255999Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"UER: An Open-Source Toolkit for Pre-training Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Haotang Deng, Hui Chen, Jinbin Zhang, Qi Ju, Tao Liu, Wei Lu, Xiaoyong Du, Xi Chen, Xin Zhao, Zhe Zhao","submitted_at":"2019-09-12T13:46:58Z","abstract_excerpt":"Existing works, including ELMO and BERT, have revealed the importance of pre-training for NLP tasks. While there does not exist a single pre-training model that works best in all cases, it is of necessity to develop a framework that is able to deploy various pre-training models efficiently. For this purpose, we propose an assemble-on-demand pre-training toolkit, namely Universal Encoder Representations (UER). UER is loosely coupled, and encapsulated with rich modules. By assembling modules on demand, users can either reproduce a state-of-the-art pre-training model or develop a pre-training mod"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.05658","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/1909.05658/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":"1909.05658","created_at":"2026-07-05T00:04:17.256056+00:00"},{"alias_kind":"arxiv_version","alias_value":"1909.05658v1","created_at":"2026-07-05T00:04:17.256056+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.05658","created_at":"2026-07-05T00:04:17.256056+00:00"},{"alias_kind":"pith_short_12","alias_value":"IOCCICNYPHWU","created_at":"2026-07-05T00:04:17.256056+00:00"},{"alias_kind":"pith_short_16","alias_value":"IOCCICNYPHWUS2D7","created_at":"2026-07-05T00:04:17.256056+00:00"},{"alias_kind":"pith_short_8","alias_value":"IOCCICNY","created_at":"2026-07-05T00:04:17.256056+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/IOCCICNYPHWUS2D72P3APN4D6K","json":"https://pith.science/pith/IOCCICNYPHWUS2D72P3APN4D6K.json","graph_json":"https://pith.science/api/pith-number/IOCCICNYPHWUS2D72P3APN4D6K/graph.json","events_json":"https://pith.science/api/pith-number/IOCCICNYPHWUS2D72P3APN4D6K/events.json","paper":"https://pith.science/paper/IOCCICNY"},"agent_actions":{"view_html":"https://pith.science/pith/IOCCICNYPHWUS2D72P3APN4D6K","download_json":"https://pith.science/pith/IOCCICNYPHWUS2D72P3APN4D6K.json","view_paper":"https://pith.science/paper/IOCCICNY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1909.05658&json=true","fetch_graph":"https://pith.science/api/pith-number/IOCCICNYPHWUS2D72P3APN4D6K/graph.json","fetch_events":"https://pith.science/api/pith-number/IOCCICNYPHWUS2D72P3APN4D6K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IOCCICNYPHWUS2D72P3APN4D6K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IOCCICNYPHWUS2D72P3APN4D6K/action/storage_attestation","attest_author":"https://pith.science/pith/IOCCICNYPHWUS2D72P3APN4D6K/action/author_attestation","sign_citation":"https://pith.science/pith/IOCCICNYPHWUS2D72P3APN4D6K/action/citation_signature","submit_replication":"https://pith.science/pith/IOCCICNYPHWUS2D72P3APN4D6K/action/replication_record"}},"created_at":"2026-07-05T00:04:17.256056+00:00","updated_at":"2026-07-05T00:04:17.256056+00:00"}