{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:GNRW3E4SF46FBYJLEKBSYASHC2","short_pith_number":"pith:GNRW3E4S","canonical_record":{"source":{"id":"2011.04446","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-11-09T14:12:07Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"5489bea83d2636ac5c3a8c48c977d992f25865e8c4692644b8b3957427bc7201","abstract_canon_sha256":"226ea86e67f9a612a82f25105a20d58951771dc2014704f22eb7f9b8b99f0206"},"schema_version":"1.0"},"canonical_sha256":"33636d93922f3c50e12b22832c0247168b3f3fee16095b161b45f5f8a402d8fa","source":{"kind":"arxiv","id":"2011.04446","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2011.04446","created_at":"2026-07-05T01:50:05Z"},{"alias_kind":"arxiv_version","alias_value":"2011.04446v1","created_at":"2026-07-05T01:50:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.04446","created_at":"2026-07-05T01:50:05Z"},{"alias_kind":"pith_short_12","alias_value":"GNRW3E4SF46F","created_at":"2026-07-05T01:50:05Z"},{"alias_kind":"pith_short_16","alias_value":"GNRW3E4SF46FBYJL","created_at":"2026-07-05T01:50:05Z"},{"alias_kind":"pith_short_8","alias_value":"GNRW3E4S","created_at":"2026-07-05T01:50:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:GNRW3E4SF46FBYJLEKBSYASHC2","target":"record","payload":{"canonical_record":{"source":{"id":"2011.04446","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-11-09T14:12:07Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"5489bea83d2636ac5c3a8c48c977d992f25865e8c4692644b8b3957427bc7201","abstract_canon_sha256":"226ea86e67f9a612a82f25105a20d58951771dc2014704f22eb7f9b8b99f0206"},"schema_version":"1.0"},"canonical_sha256":"33636d93922f3c50e12b22832c0247168b3f3fee16095b161b45f5f8a402d8fa","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:50:05.470409Z","signature_b64":"zmETEu0hsxtKaWuI2kZSpAvY+QggGkIMsodgEnKI2M8ZVZO3629FIUWwTMX0doToJdLs4iVO9tOVmdbiYfORBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"33636d93922f3c50e12b22832c0247168b3f3fee16095b161b45f5f8a402d8fa","last_reissued_at":"2026-07-05T01:50:05.469965Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:50:05.469965Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2011.04446","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":"2026-07-05T01:50:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eKyJ3Z51cT/XAyKQhgMjQyiNJDGGFBSnguwfoGpb9tL3D1LcvwY75sWsvRKcj9HjYzE7Wd+Z4X3WT4jxE3ovBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T04:05:31.603348Z"},"content_sha256":"be661be20d6312d1a4a4211c78551b9ac4f607af974c5d2d4d41e65bc1962e7f","schema_version":"1.0","event_id":"sha256:be661be20d6312d1a4a4211c78551b9ac4f607af974c5d2d4d41e65bc1962e7f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:GNRW3E4SF46FBYJLEKBSYASHC2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bangla Text Classification using Transformers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Akib Khan, Firoj Alam, Tanvirul Alam","submitted_at":"2020-11-09T14:12:07Z","abstract_excerpt":"Text classification has been one of the earliest problems in NLP. Over time the scope of application areas has broadened and the difficulty of dealing with new areas (e.g., noisy social media content) has increased. The problem-solving strategy switched from classical machine learning to deep learning algorithms. One of the recent deep neural network architecture is the Transformer. Models designed with this type of network and its variants recently showed their success in many downstream natural language processing tasks, especially for resource-rich languages, e.g., English. However, these m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.04446","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/2011.04446/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":"2026-07-05T01:50:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uUcxglW08m8N63jpvmVAL6zx92LvbwcX8bayRCDb979KwRkHkpt/Z2KT4/80EFUi4Govcmubo9w0sNGfJV41AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T04:05:31.603871Z"},"content_sha256":"6ab8895dad7e97c5869ec619d781e1a646d004c06e8303003eb5140eeeddb063","schema_version":"1.0","event_id":"sha256:6ab8895dad7e97c5869ec619d781e1a646d004c06e8303003eb5140eeeddb063"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GNRW3E4SF46FBYJLEKBSYASHC2/bundle.json","state_url":"https://pith.science/pith/GNRW3E4SF46FBYJLEKBSYASHC2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GNRW3E4SF46FBYJLEKBSYASHC2/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":"2026-08-08T04:05:31Z","links":{"resolver":"https://pith.science/pith/GNRW3E4SF46FBYJLEKBSYASHC2","bundle":"https://pith.science/pith/GNRW3E4SF46FBYJLEKBSYASHC2/bundle.json","state":"https://pith.science/pith/GNRW3E4SF46FBYJLEKBSYASHC2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GNRW3E4SF46FBYJLEKBSYASHC2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:GNRW3E4SF46FBYJLEKBSYASHC2","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":"226ea86e67f9a612a82f25105a20d58951771dc2014704f22eb7f9b8b99f0206","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-11-09T14:12:07Z","title_canon_sha256":"5489bea83d2636ac5c3a8c48c977d992f25865e8c4692644b8b3957427bc7201"},"schema_version":"1.0","source":{"id":"2011.04446","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2011.04446","created_at":"2026-07-05T01:50:05Z"},{"alias_kind":"arxiv_version","alias_value":"2011.04446v1","created_at":"2026-07-05T01:50:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.04446","created_at":"2026-07-05T01:50:05Z"},{"alias_kind":"pith_short_12","alias_value":"GNRW3E4SF46F","created_at":"2026-07-05T01:50:05Z"},{"alias_kind":"pith_short_16","alias_value":"GNRW3E4SF46FBYJL","created_at":"2026-07-05T01:50:05Z"},{"alias_kind":"pith_short_8","alias_value":"GNRW3E4S","created_at":"2026-07-05T01:50:05Z"}],"graph_snapshots":[{"event_id":"sha256:6ab8895dad7e97c5869ec619d781e1a646d004c06e8303003eb5140eeeddb063","target":"graph","created_at":"2026-07-05T01:50:05Z","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/2011.04446/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Text classification has been one of the earliest problems in NLP. Over time the scope of application areas has broadened and the difficulty of dealing with new areas (e.g., noisy social media content) has increased. The problem-solving strategy switched from classical machine learning to deep learning algorithms. One of the recent deep neural network architecture is the Transformer. Models designed with this type of network and its variants recently showed their success in many downstream natural language processing tasks, especially for resource-rich languages, e.g., English. However, these m","authors_text":"Akib Khan, Firoj Alam, Tanvirul Alam","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-11-09T14:12:07Z","title":"Bangla Text Classification using Transformers"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.04446","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:be661be20d6312d1a4a4211c78551b9ac4f607af974c5d2d4d41e65bc1962e7f","target":"record","created_at":"2026-07-05T01:50:05Z","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":"226ea86e67f9a612a82f25105a20d58951771dc2014704f22eb7f9b8b99f0206","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-11-09T14:12:07Z","title_canon_sha256":"5489bea83d2636ac5c3a8c48c977d992f25865e8c4692644b8b3957427bc7201"},"schema_version":"1.0","source":{"id":"2011.04446","kind":"arxiv","version":1}},"canonical_sha256":"33636d93922f3c50e12b22832c0247168b3f3fee16095b161b45f5f8a402d8fa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"33636d93922f3c50e12b22832c0247168b3f3fee16095b161b45f5f8a402d8fa","first_computed_at":"2026-07-05T01:50:05.469965Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:50:05.469965Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zmETEu0hsxtKaWuI2kZSpAvY+QggGkIMsodgEnKI2M8ZVZO3629FIUWwTMX0doToJdLs4iVO9tOVmdbiYfORBA==","signature_status":"signed_v1","signed_at":"2026-07-05T01:50:05.470409Z","signed_message":"canonical_sha256_bytes"},"source_id":"2011.04446","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:be661be20d6312d1a4a4211c78551b9ac4f607af974c5d2d4d41e65bc1962e7f","sha256:6ab8895dad7e97c5869ec619d781e1a646d004c06e8303003eb5140eeeddb063"],"state_sha256":"1b20e51b242a9557c1499cce2adaf28d214be2b9caa024c9884e36d30fdb8ddf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WoAqU04lyzNuDdAqxrVWqGvGTlJk89Lj1wwUmMVNV2SXcMAmYkqePMlX4676Js1uwPTgbtIpHdGQjo2phRlFCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T04:05:31.607442Z","bundle_sha256":"a17aab27c6266c5dc1c231f817f463de88a99c1f9598318a29291d4466352b02"}}