{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:3XOSMORXOGHCVAGQD5E2QJGC4D","short_pith_number":"pith:3XOSMORX","schema_version":"1.0","canonical_sha256":"dddd263a37718e2a80d01f49a824c2e0d0e3fa607b5c79c3d194420936045814","source":{"kind":"arxiv","id":"2104.14289","version":2},"attestation_state":"computed","paper":{"title":"Multi-class Text Classification using BERT-based Active Learning","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.IR","authors_text":"Hemant Misra, Moosa Mohamed, Sumanth Prabhu","submitted_at":"2021-04-27T19:49:39Z","abstract_excerpt":"Text Classification finds interesting applications in the pickup and delivery services industry where customers require one or more items to be picked up from a location and delivered to a certain destination. Classifying these customer transactions into multiple categories helps understand the market needs for different customer segments. Each transaction is accompanied by a text description provided by the customer to describe the products being picked up and delivered which can be used to classify the transaction. BERT-based models have proven to perform well in Natural Language Understandi"},"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":"2104.14289","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.IR","submitted_at":"2021-04-27T19:49:39Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ead84e442d256990a32611f081f2b6b6fed2579f6b030bb8bf25beb8a55ef6ec","abstract_canon_sha256":"0c419aa8901f25dc31a40f3606daa0fe0213dfc621b9f8a3ce568f190077bf23"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:15:33.881111Z","signature_b64":"J3yC9czbgNA2BeejQcG9woWyXqTOUAbVPko2Lq0ei2JE6i8HWIa5lBuVidk2sOfAR0/TYrhbxy3zSOLpOWYODQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dddd263a37718e2a80d01f49a824c2e0d0e3fa607b5c79c3d194420936045814","last_reissued_at":"2026-07-05T03:15:33.880632Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:15:33.880632Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-class Text Classification using BERT-based Active Learning","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.IR","authors_text":"Hemant Misra, Moosa Mohamed, Sumanth Prabhu","submitted_at":"2021-04-27T19:49:39Z","abstract_excerpt":"Text Classification finds interesting applications in the pickup and delivery services industry where customers require one or more items to be picked up from a location and delivered to a certain destination. Classifying these customer transactions into multiple categories helps understand the market needs for different customer segments. Each transaction is accompanied by a text description provided by the customer to describe the products being picked up and delivered which can be used to classify the transaction. BERT-based models have proven to perform well in Natural Language Understandi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.14289","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/2104.14289/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":"2104.14289","created_at":"2026-07-05T03:15:33.880690+00:00"},{"alias_kind":"arxiv_version","alias_value":"2104.14289v2","created_at":"2026-07-05T03:15:33.880690+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.14289","created_at":"2026-07-05T03:15:33.880690+00:00"},{"alias_kind":"pith_short_12","alias_value":"3XOSMORXOGHC","created_at":"2026-07-05T03:15:33.880690+00:00"},{"alias_kind":"pith_short_16","alias_value":"3XOSMORXOGHCVAGQ","created_at":"2026-07-05T03:15:33.880690+00:00"},{"alias_kind":"pith_short_8","alias_value":"3XOSMORX","created_at":"2026-07-05T03:15:33.880690+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.01327","citing_title":"Reasoner for Real-World Event Detection: Scaling Reinforcement Learning via Adaptive Perplexity-Aware Sampling Strategy","ref_index":33,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3XOSMORXOGHCVAGQD5E2QJGC4D","json":"https://pith.science/pith/3XOSMORXOGHCVAGQD5E2QJGC4D.json","graph_json":"https://pith.science/api/pith-number/3XOSMORXOGHCVAGQD5E2QJGC4D/graph.json","events_json":"https://pith.science/api/pith-number/3XOSMORXOGHCVAGQD5E2QJGC4D/events.json","paper":"https://pith.science/paper/3XOSMORX"},"agent_actions":{"view_html":"https://pith.science/pith/3XOSMORXOGHCVAGQD5E2QJGC4D","download_json":"https://pith.science/pith/3XOSMORXOGHCVAGQD5E2QJGC4D.json","view_paper":"https://pith.science/paper/3XOSMORX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2104.14289&json=true","fetch_graph":"https://pith.science/api/pith-number/3XOSMORXOGHCVAGQD5E2QJGC4D/graph.json","fetch_events":"https://pith.science/api/pith-number/3XOSMORXOGHCVAGQD5E2QJGC4D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3XOSMORXOGHCVAGQD5E2QJGC4D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3XOSMORXOGHCVAGQD5E2QJGC4D/action/storage_attestation","attest_author":"https://pith.science/pith/3XOSMORXOGHCVAGQD5E2QJGC4D/action/author_attestation","sign_citation":"https://pith.science/pith/3XOSMORXOGHCVAGQD5E2QJGC4D/action/citation_signature","submit_replication":"https://pith.science/pith/3XOSMORXOGHCVAGQD5E2QJGC4D/action/replication_record"}},"created_at":"2026-07-05T03:15:33.880690+00:00","updated_at":"2026-07-05T03:15:33.880690+00:00"}