{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:DW6ZTTVGR2YERAHH7T3PXRTN3O","short_pith_number":"pith:DW6ZTTVG","canonical_record":{"source":{"id":"2106.02241","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-06-04T04:00:16Z","cross_cats_sorted":[],"title_canon_sha256":"e8b4f8ac50b8bf37362fee6bbb8980373a17398950a210c497533689673bc3b4","abstract_canon_sha256":"e3c2a6327bbcdfeb2b695ea1b2a8e897e91d4cf3e243d44217e6969d8fbab5b7"},"schema_version":"1.0"},"canonical_sha256":"1dbd99cea68eb04880e7fcf6fbc66ddb8f3b5def89e6b05db4e98dfbaf5bf60b","source":{"kind":"arxiv","id":"2106.02241","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.02241","created_at":"2026-07-05T02:46:17Z"},{"alias_kind":"arxiv_version","alias_value":"2106.02241v1","created_at":"2026-07-05T02:46:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.02241","created_at":"2026-07-05T02:46:17Z"},{"alias_kind":"pith_short_12","alias_value":"DW6ZTTVGR2YE","created_at":"2026-07-05T02:46:17Z"},{"alias_kind":"pith_short_16","alias_value":"DW6ZTTVGR2YERAHH","created_at":"2026-07-05T02:46:17Z"},{"alias_kind":"pith_short_8","alias_value":"DW6ZTTVG","created_at":"2026-07-05T02:46:17Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:DW6ZTTVGR2YERAHH7T3PXRTN3O","target":"record","payload":{"canonical_record":{"source":{"id":"2106.02241","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-06-04T04:00:16Z","cross_cats_sorted":[],"title_canon_sha256":"e8b4f8ac50b8bf37362fee6bbb8980373a17398950a210c497533689673bc3b4","abstract_canon_sha256":"e3c2a6327bbcdfeb2b695ea1b2a8e897e91d4cf3e243d44217e6969d8fbab5b7"},"schema_version":"1.0"},"canonical_sha256":"1dbd99cea68eb04880e7fcf6fbc66ddb8f3b5def89e6b05db4e98dfbaf5bf60b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:46:17.231102Z","signature_b64":"gh1gGUZXFzFDoQlmazjSQteO6Yd3gQXl7pkWSMsTIJ3Sosf+7BGlQt/vUxSJ6bysRmjRDUnO8N1sNUNQTEbFCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1dbd99cea68eb04880e7fcf6fbc66ddb8f3b5def89e6b05db4e98dfbaf5bf60b","last_reissued_at":"2026-07-05T02:46:17.230688Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:46:17.230688Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2106.02241","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":"2026-07-05T02:46:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qMbB8ghDE7ZmF49+Oe6DwrURAcO5WFdaZt6HVoI4bgnTgw1amcuvD6karrxfn2A3WdDd73o+eaUg+8MLvTKqAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T08:07:00.438325Z"},"content_sha256":"20df33a9642a4c9ec7209f921027645833a6bbbc811471fe22dfe6fb686a5795","schema_version":"1.0","event_id":"sha256:20df33a9642a4c9ec7209f921027645833a6bbbc811471fe22dfe6fb686a5795"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:DW6ZTTVGR2YERAHH7T3PXRTN3O","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ERNIE-Tiny : A Progressive Distillation Framework for Pretrained Transformer Compression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"HaiFeng Wang, Hao Tian, Hua Wu, Jiaxiang Liu, Shikun Feng, Weixin Liu, Weiyue Su, Xuyi Chen, Yu Sun","submitted_at":"2021-06-04T04:00:16Z","abstract_excerpt":"Pretrained language models (PLMs) such as BERT adopt a training paradigm which first pretrain the model in general data and then finetune the model on task-specific data, and have recently achieved great success. However, PLMs are notorious for their enormous parameters and hard to be deployed on real-life applications. Knowledge distillation has been prevailing to address this problem by transferring knowledge from a large teacher to a much smaller student over a set of data. We argue that the selection of thee three key components, namely teacher, training data, and learning objective, is cr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.02241","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/2106.02241/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":"2026-07-05T02:46:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TyOdqWrSxtjpx/pa5JBGvsifKwgWJ+iTPGYwUQKtDoDS+tFbxNuXDZAg5XAG+SmwI138UDcjK3adW5h92KdTDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T08:07:00.438689Z"},"content_sha256":"a6f188476fd6f17bba1205f493b80fe64c67a62c821298e49baa7805e15fa3e7","schema_version":"1.0","event_id":"sha256:a6f188476fd6f17bba1205f493b80fe64c67a62c821298e49baa7805e15fa3e7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DW6ZTTVGR2YERAHH7T3PXRTN3O/bundle.json","state_url":"https://pith.science/pith/DW6ZTTVGR2YERAHH7T3PXRTN3O/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DW6ZTTVGR2YERAHH7T3PXRTN3O/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":"2026-07-05T08:07:00Z","links":{"resolver":"https://pith.science/pith/DW6ZTTVGR2YERAHH7T3PXRTN3O","bundle":"https://pith.science/pith/DW6ZTTVGR2YERAHH7T3PXRTN3O/bundle.json","state":"https://pith.science/pith/DW6ZTTVGR2YERAHH7T3PXRTN3O/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DW6ZTTVGR2YERAHH7T3PXRTN3O/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:DW6ZTTVGR2YERAHH7T3PXRTN3O","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":"e3c2a6327bbcdfeb2b695ea1b2a8e897e91d4cf3e243d44217e6969d8fbab5b7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-06-04T04:00:16Z","title_canon_sha256":"e8b4f8ac50b8bf37362fee6bbb8980373a17398950a210c497533689673bc3b4"},"schema_version":"1.0","source":{"id":"2106.02241","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.02241","created_at":"2026-07-05T02:46:17Z"},{"alias_kind":"arxiv_version","alias_value":"2106.02241v1","created_at":"2026-07-05T02:46:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.02241","created_at":"2026-07-05T02:46:17Z"},{"alias_kind":"pith_short_12","alias_value":"DW6ZTTVGR2YE","created_at":"2026-07-05T02:46:17Z"},{"alias_kind":"pith_short_16","alias_value":"DW6ZTTVGR2YERAHH","created_at":"2026-07-05T02:46:17Z"},{"alias_kind":"pith_short_8","alias_value":"DW6ZTTVG","created_at":"2026-07-05T02:46:17Z"}],"graph_snapshots":[{"event_id":"sha256:a6f188476fd6f17bba1205f493b80fe64c67a62c821298e49baa7805e15fa3e7","target":"graph","created_at":"2026-07-05T02:46:17Z","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/2106.02241/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Pretrained language models (PLMs) such as BERT adopt a training paradigm which first pretrain the model in general data and then finetune the model on task-specific data, and have recently achieved great success. However, PLMs are notorious for their enormous parameters and hard to be deployed on real-life applications. Knowledge distillation has been prevailing to address this problem by transferring knowledge from a large teacher to a much smaller student over a set of data. We argue that the selection of thee three key components, namely teacher, training data, and learning objective, is cr","authors_text":"HaiFeng Wang, Hao Tian, Hua Wu, Jiaxiang Liu, Shikun Feng, Weixin Liu, Weiyue Su, Xuyi Chen, Yu Sun","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-06-04T04:00:16Z","title":"ERNIE-Tiny : A Progressive Distillation Framework for Pretrained Transformer Compression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.02241","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":"sha256:20df33a9642a4c9ec7209f921027645833a6bbbc811471fe22dfe6fb686a5795","target":"record","created_at":"2026-07-05T02:46:17Z","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":"e3c2a6327bbcdfeb2b695ea1b2a8e897e91d4cf3e243d44217e6969d8fbab5b7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-06-04T04:00:16Z","title_canon_sha256":"e8b4f8ac50b8bf37362fee6bbb8980373a17398950a210c497533689673bc3b4"},"schema_version":"1.0","source":{"id":"2106.02241","kind":"arxiv","version":1}},"canonical_sha256":"1dbd99cea68eb04880e7fcf6fbc66ddb8f3b5def89e6b05db4e98dfbaf5bf60b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1dbd99cea68eb04880e7fcf6fbc66ddb8f3b5def89e6b05db4e98dfbaf5bf60b","first_computed_at":"2026-07-05T02:46:17.230688Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:46:17.230688Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gh1gGUZXFzFDoQlmazjSQteO6Yd3gQXl7pkWSMsTIJ3Sosf+7BGlQt/vUxSJ6bysRmjRDUnO8N1sNUNQTEbFCA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:46:17.231102Z","signed_message":"canonical_sha256_bytes"},"source_id":"2106.02241","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:20df33a9642a4c9ec7209f921027645833a6bbbc811471fe22dfe6fb686a5795","sha256:a6f188476fd6f17bba1205f493b80fe64c67a62c821298e49baa7805e15fa3e7"],"state_sha256":"c937271351fdc3857b5644c6d48145d0a80536d9fa6b0a2ed7402628dba2db32"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XicSdr6nOZ2f85YD96PCtvQ6R4SvvpGYFg+xC4kRhihNFEC1Ld3la4+nL40kmQDcnyxnDXY2hPILoYJKipeEBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-05T08:07:00.440655Z","bundle_sha256":"e3817054b908bdb13bf16dc5d13e05a7a55849e2f080a63893bba529a2d8258c"}}