{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:GXNLIWPTOLCZEIJ4YUYWNQGA7A","short_pith_number":"pith:GXNLIWPT","canonical_record":{"source":{"id":"2401.17396","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-01-30T19:27:04Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c9d37e581d87490f42b7e0ac7066c3fc0405737a649458ccdb54725b91f54718","abstract_canon_sha256":"439b26a051bf404bdaf297f9cbfe8dc21a3c0283dc97cfecc68e44bbed29a1a0"},"schema_version":"1.0"},"canonical_sha256":"35dab459f372c592213cc53166c0c0f814c04297ef1f861808c2a574eae695ab","source":{"kind":"arxiv","id":"2401.17396","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.17396","created_at":"2026-07-05T07:39:36Z"},{"alias_kind":"arxiv_version","alias_value":"2401.17396v1","created_at":"2026-07-05T07:39:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.17396","created_at":"2026-07-05T07:39:36Z"},{"alias_kind":"pith_short_12","alias_value":"GXNLIWPTOLCZ","created_at":"2026-07-05T07:39:36Z"},{"alias_kind":"pith_short_16","alias_value":"GXNLIWPTOLCZEIJ4","created_at":"2026-07-05T07:39:36Z"},{"alias_kind":"pith_short_8","alias_value":"GXNLIWPT","created_at":"2026-07-05T07:39:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:GXNLIWPTOLCZEIJ4YUYWNQGA7A","target":"record","payload":{"canonical_record":{"source":{"id":"2401.17396","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-01-30T19:27:04Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c9d37e581d87490f42b7e0ac7066c3fc0405737a649458ccdb54725b91f54718","abstract_canon_sha256":"439b26a051bf404bdaf297f9cbfe8dc21a3c0283dc97cfecc68e44bbed29a1a0"},"schema_version":"1.0"},"canonical_sha256":"35dab459f372c592213cc53166c0c0f814c04297ef1f861808c2a574eae695ab","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:39:36.165705Z","signature_b64":"6RBD0aZd66yZ9OzUKVfRQd5h1w/z8sZ+oK/Ot+lp32YiOXGVpZ0XAdExBwqqbwqR2VfzpTRl0MCCzbq00k8XDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"35dab459f372c592213cc53166c0c0f814c04297ef1f861808c2a574eae695ab","last_reissued_at":"2026-07-05T07:39:36.165284Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:39:36.165284Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2401.17396","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":"2026-07-05T07:39:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eZ5NWv7mcRQFG2swX2rVcVc2Tn6V5ykooaXVs9yurBvVCl1Y2XPQ6muOEeBciio0Yf1/TfkOUkV5rWpXvSKMCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T00:46:08.207954Z"},"content_sha256":"b872e917705e2fd763131a05886ce40e130c218f84620f7e532e787815275fd8","schema_version":"1.0","event_id":"sha256:b872e917705e2fd763131a05886ce40e130c218f84620f7e532e787815275fd8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:GXNLIWPTOLCZEIJ4YUYWNQGA7A","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fine-tuning Transformer-based Encoder for Turkish Language Understanding Tasks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Savas Yildirim","submitted_at":"2024-01-30T19:27:04Z","abstract_excerpt":"Deep learning-based and lately Transformer-based language models have been dominating the studies of natural language processing in the last years. Thanks to their accurate and fast fine-tuning characteristics, they have outperformed traditional machine learning-based approaches and achieved state-of-the-art results for many challenging natural language understanding (NLU) problems. Recent studies showed that the Transformer-based models such as BERT, which is Bidirectional Encoder Representations from Transformers, have reached impressive achievements on many tasks. Moreover, thanks to their "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.17396","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/2401.17396/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":"2026-07-05T07:39:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3Ku8ldn56/VNSdtEZF8MAhRyIn3pG6vTlcxcL4RqH1LAnxCBc5OR0TVt3Ah2cJxIuOvlWt8GtFDrsW9wIpB+CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T00:46:08.208739Z"},"content_sha256":"f8cb5257be6535202179645e0d8d3abf5e1f264a3dd3881b322fa112a3851e57","schema_version":"1.0","event_id":"sha256:f8cb5257be6535202179645e0d8d3abf5e1f264a3dd3881b322fa112a3851e57"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GXNLIWPTOLCZEIJ4YUYWNQGA7A/bundle.json","state_url":"https://pith.science/pith/GXNLIWPTOLCZEIJ4YUYWNQGA7A/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GXNLIWPTOLCZEIJ4YUYWNQGA7A/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":"2026-08-22T00:46:08Z","links":{"resolver":"https://pith.science/pith/GXNLIWPTOLCZEIJ4YUYWNQGA7A","bundle":"https://pith.science/pith/GXNLIWPTOLCZEIJ4YUYWNQGA7A/bundle.json","state":"https://pith.science/pith/GXNLIWPTOLCZEIJ4YUYWNQGA7A/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GXNLIWPTOLCZEIJ4YUYWNQGA7A/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:GXNLIWPTOLCZEIJ4YUYWNQGA7A","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":"439b26a051bf404bdaf297f9cbfe8dc21a3c0283dc97cfecc68e44bbed29a1a0","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-01-30T19:27:04Z","title_canon_sha256":"c9d37e581d87490f42b7e0ac7066c3fc0405737a649458ccdb54725b91f54718"},"schema_version":"1.0","source":{"id":"2401.17396","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.17396","created_at":"2026-07-05T07:39:36Z"},{"alias_kind":"arxiv_version","alias_value":"2401.17396v1","created_at":"2026-07-05T07:39:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.17396","created_at":"2026-07-05T07:39:36Z"},{"alias_kind":"pith_short_12","alias_value":"GXNLIWPTOLCZ","created_at":"2026-07-05T07:39:36Z"},{"alias_kind":"pith_short_16","alias_value":"GXNLIWPTOLCZEIJ4","created_at":"2026-07-05T07:39:36Z"},{"alias_kind":"pith_short_8","alias_value":"GXNLIWPT","created_at":"2026-07-05T07:39:36Z"}],"graph_snapshots":[{"event_id":"sha256:f8cb5257be6535202179645e0d8d3abf5e1f264a3dd3881b322fa112a3851e57","target":"graph","created_at":"2026-07-05T07:39:36Z","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/2401.17396/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep learning-based and lately Transformer-based language models have been dominating the studies of natural language processing in the last years. Thanks to their accurate and fast fine-tuning characteristics, they have outperformed traditional machine learning-based approaches and achieved state-of-the-art results for many challenging natural language understanding (NLU) problems. Recent studies showed that the Transformer-based models such as BERT, which is Bidirectional Encoder Representations from Transformers, have reached impressive achievements on many tasks. Moreover, thanks to their ","authors_text":"Savas Yildirim","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-01-30T19:27:04Z","title":"Fine-tuning Transformer-based Encoder for Turkish Language Understanding Tasks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.17396","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:b872e917705e2fd763131a05886ce40e130c218f84620f7e532e787815275fd8","target":"record","created_at":"2026-07-05T07:39:36Z","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":"439b26a051bf404bdaf297f9cbfe8dc21a3c0283dc97cfecc68e44bbed29a1a0","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-01-30T19:27:04Z","title_canon_sha256":"c9d37e581d87490f42b7e0ac7066c3fc0405737a649458ccdb54725b91f54718"},"schema_version":"1.0","source":{"id":"2401.17396","kind":"arxiv","version":1}},"canonical_sha256":"35dab459f372c592213cc53166c0c0f814c04297ef1f861808c2a574eae695ab","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"35dab459f372c592213cc53166c0c0f814c04297ef1f861808c2a574eae695ab","first_computed_at":"2026-07-05T07:39:36.165284Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:39:36.165284Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6RBD0aZd66yZ9OzUKVfRQd5h1w/z8sZ+oK/Ot+lp32YiOXGVpZ0XAdExBwqqbwqR2VfzpTRl0MCCzbq00k8XDg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:39:36.165705Z","signed_message":"canonical_sha256_bytes"},"source_id":"2401.17396","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b872e917705e2fd763131a05886ce40e130c218f84620f7e532e787815275fd8","sha256:f8cb5257be6535202179645e0d8d3abf5e1f264a3dd3881b322fa112a3851e57"],"state_sha256":"6732d7eca028bf24137fb63987c309df766f7a922ed8816acf2879f66aed1062"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mSl4vsr525dAoTyi6J2ed+2FFsLdlZ9fMUQDzTj6npHlvK4fSbnNi9AOPoJwT18sej7G4r5pYR4gRYBNcVP1Dw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-22T00:46:08.214807Z","bundle_sha256":"beb3c3c34494c0cb5ba64f8dc0dae9c1d0320c6debd47cb9ce8e4ef135f2499b"}}