{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:NYYYQRJ74FOFGVG63ZPRZUAQD3","short_pith_number":"pith:NYYYQRJ7","schema_version":"1.0","canonical_sha256":"6e3188453fe15c5354dede5f1cd0101ed8f4fb1d92d93774a7a4d39ded6e433a","source":{"kind":"arxiv","id":"2112.02448","version":2},"attestation_state":"computed","paper":{"title":"Emojich -- zero-shot emoji generation using Russian language: a technical report","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Aleksandr Nikolich, Alex Shonenkov, Daria Bakshandaeva, Denis Dimitrov","submitted_at":"2021-12-04T23:37:32Z","abstract_excerpt":"This technical report presents a text-to-image neural network \"Emojich\" that generates emojis using captions in Russian language as a condition. We aim to keep the generalization ability of a pretrained big model ruDALL-E Malevich (XL) 1.3B parameters at the fine-tuning stage, while giving special style to the images generated. Here are presented some engineering methods, code realization, all hyper-parameters for reproducing results and a Telegram bot where everyone can create their own customized sets of stickers. Also, some newly generated emojis obtained by \"Emojich\" model are demonstrated"},"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":"2112.02448","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-12-04T23:37:32Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"8bbd0e57112d6feb73af33e580ddae2f2992693babf9ee413217dee122ab7447","abstract_canon_sha256":"2bff7503d0e282d73275c24135ee15a5c10a4a1c1e7c612dd2ee2b3ad497d6ef"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:48:09.816176Z","signature_b64":"8jKLOb5JxpMzMpxNY15aSFKGBhCHMOxNULSwCt3wjocKpXXfsyXXIDmv/4/30vr8vzGlm1A61qQgvFXEssiIAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6e3188453fe15c5354dede5f1cd0101ed8f4fb1d92d93774a7a4d39ded6e433a","last_reissued_at":"2026-07-05T03:48:09.815794Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:48:09.815794Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Emojich -- zero-shot emoji generation using Russian language: a technical report","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Aleksandr Nikolich, Alex Shonenkov, Daria Bakshandaeva, Denis Dimitrov","submitted_at":"2021-12-04T23:37:32Z","abstract_excerpt":"This technical report presents a text-to-image neural network \"Emojich\" that generates emojis using captions in Russian language as a condition. We aim to keep the generalization ability of a pretrained big model ruDALL-E Malevich (XL) 1.3B parameters at the fine-tuning stage, while giving special style to the images generated. Here are presented some engineering methods, code realization, all hyper-parameters for reproducing results and a Telegram bot where everyone can create their own customized sets of stickers. Also, some newly generated emojis obtained by \"Emojich\" model are demonstrated"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.02448","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/2112.02448/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":"2112.02448","created_at":"2026-07-05T03:48:09.815855+00:00"},{"alias_kind":"arxiv_version","alias_value":"2112.02448v2","created_at":"2026-07-05T03:48:09.815855+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.02448","created_at":"2026-07-05T03:48:09.815855+00:00"},{"alias_kind":"pith_short_12","alias_value":"NYYYQRJ74FOF","created_at":"2026-07-05T03:48:09.815855+00:00"},{"alias_kind":"pith_short_16","alias_value":"NYYYQRJ74FOFGVG6","created_at":"2026-07-05T03:48:09.815855+00:00"},{"alias_kind":"pith_short_8","alias_value":"NYYYQRJ7","created_at":"2026-07-05T03:48:09.815855+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/NYYYQRJ74FOFGVG63ZPRZUAQD3","json":"https://pith.science/pith/NYYYQRJ74FOFGVG63ZPRZUAQD3.json","graph_json":"https://pith.science/api/pith-number/NYYYQRJ74FOFGVG63ZPRZUAQD3/graph.json","events_json":"https://pith.science/api/pith-number/NYYYQRJ74FOFGVG63ZPRZUAQD3/events.json","paper":"https://pith.science/paper/NYYYQRJ7"},"agent_actions":{"view_html":"https://pith.science/pith/NYYYQRJ74FOFGVG63ZPRZUAQD3","download_json":"https://pith.science/pith/NYYYQRJ74FOFGVG63ZPRZUAQD3.json","view_paper":"https://pith.science/paper/NYYYQRJ7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2112.02448&json=true","fetch_graph":"https://pith.science/api/pith-number/NYYYQRJ74FOFGVG63ZPRZUAQD3/graph.json","fetch_events":"https://pith.science/api/pith-number/NYYYQRJ74FOFGVG63ZPRZUAQD3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NYYYQRJ74FOFGVG63ZPRZUAQD3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NYYYQRJ74FOFGVG63ZPRZUAQD3/action/storage_attestation","attest_author":"https://pith.science/pith/NYYYQRJ74FOFGVG63ZPRZUAQD3/action/author_attestation","sign_citation":"https://pith.science/pith/NYYYQRJ74FOFGVG63ZPRZUAQD3/action/citation_signature","submit_replication":"https://pith.science/pith/NYYYQRJ74FOFGVG63ZPRZUAQD3/action/replication_record"}},"created_at":"2026-07-05T03:48:09.815855+00:00","updated_at":"2026-07-05T03:48:09.815855+00:00"}