{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:7PRKJB6LVC63JFC5D53D2NUVSL","short_pith_number":"pith:7PRKJB6L","canonical_record":{"source":{"id":"2105.09052","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-05-19T10:37:44Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0119824935f43dfb98c177dde411e223405e1b7cbb9826854d79226f353a031b","abstract_canon_sha256":"4c3d8cf22328c031bebd816b7f0e363d98e787106938d25f1edc38ee23cb56e7"},"schema_version":"1.0"},"canonical_sha256":"fbe2a487cba8bdb4945d1f763d369592f1241240b56043b3b9ac70aaa11d3b01","source":{"kind":"arxiv","id":"2105.09052","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2105.09052","created_at":"2026-07-05T02:41:36Z"},{"alias_kind":"arxiv_version","alias_value":"2105.09052v1","created_at":"2026-07-05T02:41:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.09052","created_at":"2026-07-05T02:41:36Z"},{"alias_kind":"pith_short_12","alias_value":"7PRKJB6LVC63","created_at":"2026-07-05T02:41:36Z"},{"alias_kind":"pith_short_16","alias_value":"7PRKJB6LVC63JFC5","created_at":"2026-07-05T02:41:36Z"},{"alias_kind":"pith_short_8","alias_value":"7PRKJB6L","created_at":"2026-07-05T02:41:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:7PRKJB6LVC63JFC5D53D2NUVSL","target":"record","payload":{"canonical_record":{"source":{"id":"2105.09052","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-05-19T10:37:44Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0119824935f43dfb98c177dde411e223405e1b7cbb9826854d79226f353a031b","abstract_canon_sha256":"4c3d8cf22328c031bebd816b7f0e363d98e787106938d25f1edc38ee23cb56e7"},"schema_version":"1.0"},"canonical_sha256":"fbe2a487cba8bdb4945d1f763d369592f1241240b56043b3b9ac70aaa11d3b01","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:41:36.688228Z","signature_b64":"pLv+7ZubOk2+XNv14uSDgaD1QAZK3F/VmHp+8nBuP/EnQaQ3mQuvG77u92tCDPlfwqlA6tj6jez3o3nXePerBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fbe2a487cba8bdb4945d1f763d369592f1241240b56043b3b9ac70aaa11d3b01","last_reissued_at":"2026-07-05T02:41:36.687828Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:41:36.687828Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2105.09052","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":"2026-07-05T02:41:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2dRokyf4RPoQNXSwPLLDjMDHkZtD1UHa0IbUSwAiIU7aMmEPVxmun0uzfZKnYVHywdeFkIv3N/OlMscRHJh3Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T12:44:05.059886Z"},"content_sha256":"39bbf95aaa4f72a7f7b7b737471a35d10984f9a0cc9985130118832de22661c9","schema_version":"1.0","event_id":"sha256:39bbf95aaa4f72a7f7b7b737471a35d10984f9a0cc9985130118832de22661c9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:7PRKJB6LVC63JFC5D53D2NUVSL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Methods for Detoxification of Texts for the Russian Language","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Alexander Panchenko, Daniil Moskovskiy, Daryna Dementieva, David Dale, Nikita Semenov, Olga Kozlova, Varvara Logacheva","submitted_at":"2021-05-19T10:37:44Z","abstract_excerpt":"We introduce the first study of automatic detoxification of Russian texts to combat offensive language. Such a kind of textual style transfer can be used, for instance, for processing toxic content in social media. While much work has been done for the English language in this field, it has never been solved for the Russian language yet. We test two types of models - unsupervised approach based on BERT architecture that performs local corrections and supervised approach based on pretrained language GPT-2 model - and compare them with several baselines. In addition, we describe evaluation setup"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.09052","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/2105.09052/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":"2026-07-05T02:41:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EXF2LSWx1Z+/We+U7H9dNPGREKs4NAgdIndfZF7tvP88l8p3FvF/evokirQ1NTvhlY9LxGjThxk6hyN3JZyMCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T12:44:05.060425Z"},"content_sha256":"8179d618ba048c7ee21d4a493c3071c0520bcd5078dde826ec517ef2d9db609b","schema_version":"1.0","event_id":"sha256:8179d618ba048c7ee21d4a493c3071c0520bcd5078dde826ec517ef2d9db609b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7PRKJB6LVC63JFC5D53D2NUVSL/bundle.json","state_url":"https://pith.science/pith/7PRKJB6LVC63JFC5D53D2NUVSL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7PRKJB6LVC63JFC5D53D2NUVSL/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":"2026-08-18T12:44:05Z","links":{"resolver":"https://pith.science/pith/7PRKJB6LVC63JFC5D53D2NUVSL","bundle":"https://pith.science/pith/7PRKJB6LVC63JFC5D53D2NUVSL/bundle.json","state":"https://pith.science/pith/7PRKJB6LVC63JFC5D53D2NUVSL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7PRKJB6LVC63JFC5D53D2NUVSL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:7PRKJB6LVC63JFC5D53D2NUVSL","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":"4c3d8cf22328c031bebd816b7f0e363d98e787106938d25f1edc38ee23cb56e7","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-05-19T10:37:44Z","title_canon_sha256":"0119824935f43dfb98c177dde411e223405e1b7cbb9826854d79226f353a031b"},"schema_version":"1.0","source":{"id":"2105.09052","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2105.09052","created_at":"2026-07-05T02:41:36Z"},{"alias_kind":"arxiv_version","alias_value":"2105.09052v1","created_at":"2026-07-05T02:41:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.09052","created_at":"2026-07-05T02:41:36Z"},{"alias_kind":"pith_short_12","alias_value":"7PRKJB6LVC63","created_at":"2026-07-05T02:41:36Z"},{"alias_kind":"pith_short_16","alias_value":"7PRKJB6LVC63JFC5","created_at":"2026-07-05T02:41:36Z"},{"alias_kind":"pith_short_8","alias_value":"7PRKJB6L","created_at":"2026-07-05T02:41:36Z"}],"graph_snapshots":[{"event_id":"sha256:8179d618ba048c7ee21d4a493c3071c0520bcd5078dde826ec517ef2d9db609b","target":"graph","created_at":"2026-07-05T02:41:36Z","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/2105.09052/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We introduce the first study of automatic detoxification of Russian texts to combat offensive language. Such a kind of textual style transfer can be used, for instance, for processing toxic content in social media. While much work has been done for the English language in this field, it has never been solved for the Russian language yet. We test two types of models - unsupervised approach based on BERT architecture that performs local corrections and supervised approach based on pretrained language GPT-2 model - and compare them with several baselines. In addition, we describe evaluation setup","authors_text":"Alexander Panchenko, Daniil Moskovskiy, Daryna Dementieva, David Dale, Nikita Semenov, Olga Kozlova, Varvara Logacheva","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-05-19T10:37:44Z","title":"Methods for Detoxification of Texts for the Russian Language"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.09052","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":"sha256:39bbf95aaa4f72a7f7b7b737471a35d10984f9a0cc9985130118832de22661c9","target":"record","created_at":"2026-07-05T02:41:36Z","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":"4c3d8cf22328c031bebd816b7f0e363d98e787106938d25f1edc38ee23cb56e7","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-05-19T10:37:44Z","title_canon_sha256":"0119824935f43dfb98c177dde411e223405e1b7cbb9826854d79226f353a031b"},"schema_version":"1.0","source":{"id":"2105.09052","kind":"arxiv","version":1}},"canonical_sha256":"fbe2a487cba8bdb4945d1f763d369592f1241240b56043b3b9ac70aaa11d3b01","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fbe2a487cba8bdb4945d1f763d369592f1241240b56043b3b9ac70aaa11d3b01","first_computed_at":"2026-07-05T02:41:36.687828Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:41:36.687828Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pLv+7ZubOk2+XNv14uSDgaD1QAZK3F/VmHp+8nBuP/EnQaQ3mQuvG77u92tCDPlfwqlA6tj6jez3o3nXePerBw==","signature_status":"signed_v1","signed_at":"2026-07-05T02:41:36.688228Z","signed_message":"canonical_sha256_bytes"},"source_id":"2105.09052","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:39bbf95aaa4f72a7f7b7b737471a35d10984f9a0cc9985130118832de22661c9","sha256:8179d618ba048c7ee21d4a493c3071c0520bcd5078dde826ec517ef2d9db609b"],"state_sha256":"ca3ea30dbdbe65520a5991c2b8f48451751819c887b354fff1439d4c65bf8fa9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"J102kUI25LpUW7JsRCZqqj/hAYXfnZGIg6Z2kx5HyzqLoTUJ/c0XauSSQBmHjQJifr02r6QknXOvjUDUBljnDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T12:44:05.064535Z","bundle_sha256":"4793be0bd14672efd2fc3eb216f89f1158717d7ccc511d38681452829c05e0ae"}}