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Paper Citation Record · LEDGER

How to Fine-Tune BERT for Text Classification?

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:1905.05583.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1905.05583 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T10:46:03.507296Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

95
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 5041efed-ce41-4506-8160-4923527fe180 · inbound

To Tune or Not To Tune? How About the Best of Both Worlds? cites this paper.

To Tune or Not To Tune? How About the Best of Both Worlds? How to Fine-Tune BERT for Text Classification?

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-05-25T00:46:30.640759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-25T00:45:10.865484Z digest=sha256:88dd18757f28ad2746517bd8577798e07d1c01e2f4c2a246935a5579fa46f749

Observation 891a41cc-66a5-4ea0-bff3-4a3073492f16 · inbound

FinBERT: Financial Sentiment Analysis with Pre-trained Language Models cites this paper.

FinBERT: Financial Sentiment Analysis with Pre-trained Language Models How to Fine-Tune BERT for Text Classification?

Reference 29

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verified exact
arxiv_id, observed 2026-05-15T20:26:05.125770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-15T20:26:04.766701Z digest=sha256:b61c826d7881838300af67d31c7f13a8ae0835afa588f7396ff9547fc56123eb

Observation e4e5b810-f296-40b7-be6f-a77e1dd6a4c6 · inbound

OPT: Open Pre-trained Transformer Language Models cites this paper.

OPT: Open Pre-trained Transformer Language Models How to Fine-Tune BERT for Text Classification?

Reference 78

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metadata mismatch
arxiv_id, observed 2026-05-10T20:53:17.383063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-10T20:53:16.720145Z digest=sha256:d87a41163bd5a7cf8dfe53cf946e3bad219b2716d092a3aa60897ec078785d1c

Observation 50a35f0b-5963-43f7-9d04-a9077cb23b78 · inbound

Towards the Anonymization of the Language Modeling cites this paper.

Towards the Anonymization of the Language Modeling How to Fine-Tune BERT for Text Classification?

Reference 50

Resolution
verified exact
arxiv_id, observed 2026-05-23T06:32:39.544581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-23T06:28:16.975305Z digest=sha256:f885e9ba82041c780db6c0aac4df93a6ed38ac4e802e5d7ee994dae29620352e

Observation 6fb85b13-196f-4e51-aaf3-a5a4ed227fc5 · inbound

Lowering the Barrier of Machine Learning: Achieving Zero Manual Labeling in Review Classification Using LLMs cites this paper.

Lowering the Barrier of Machine Learning: Achieving Zero Manual Labeling in Review Classification Using LLMs How to Fine-Tune BERT for Text Classification?

Reference 27

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unresolved
no resolver link, observed 2026-08-09T10:46:03.507296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T10:46:03.507296Z digest=sha256:bc220524353589bbe4f5f996e62f52ad6ef3b34385a45aa938b3af513752aadb

Observation 8945b60d-bc60-41cf-855e-d76e45f9745d · inbound

Stay Focused: Problem Drift in Multi-Agent Debate cites this paper.

Stay Focused: Problem Drift in Multi-Agent Debate How to Fine-Tune BERT for Text Classification?

Reference 8

Resolution
metadata mismatch
arxiv_id, observed 2026-05-23T01:52:22.981793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-23T01:48:50.818281Z digest=sha256:816d7108b7499af6fe39eafa516605e35b613ef7a0a65b540117c5ad44621709

Observation ec4d545e-f24f-47f3-9818-38e7563cb046 · inbound

Optimising Language Models for Downstream Tasks: A Post-Training Perspective cites this paper.

Optimising Language Models for Downstream Tasks: A Post-Training Perspective How to Fine-Tune BERT for Text Classification?

Reference 214

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unresolved
no resolver link, observed 2026-08-06T22:44:44.568882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:44:44.568882Z digest=sha256:429e4bf59021131cd26ad72fa77bca95da709f46fdf6ac8f406836bf153327ec

Observation 9bd6e89e-2d7d-44c6-9d8e-4c57d51c1a67 · inbound

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text cites this paper.

Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Text How to Fine-Tune BERT for Text Classification?

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-06T18:27:20.374992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:27:20.374992Z digest=sha256:36b816843fb04302501c0390720dd08c0f43ba338d8cc29bd2efb938af762341

Observation d63fe4c4-efae-4003-a284-2e58a6e4d6a0 · inbound

Tiny Reward Models cites this paper.

Tiny Reward Models How to Fine-Tune BERT for Text Classification?

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-06T17:48:07.198467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:48:07.198467Z digest=sha256:541e04572faeb708f67a5d569507cb163cd1995232abe9b11512495e1747a610

Observation 599e3490-855f-4404-9a87-a9e8ea1f551f · inbound

Political Leaning and Politicalness Classification of Texts cites this paper.

Political Leaning and Politicalness Classification of Texts How to Fine-Tune BERT for Text Classification?

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-06T16:21:11.080273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T16:21:11.080273Z digest=sha256:3b6c97caaed51146f567abbf923be92a56af6ce1f2742ecc56b02a3417596f39

Observation 650c6b4b-4793-4eda-9bfa-6a10180ea15e · inbound

Optimizing Small Transformer-Based Language Models for Multi-Label Sentiment Analysis in Short Texts cites this paper.

Optimizing Small Transformer-Based Language Models for Multi-Label Sentiment Analysis in Short Texts How to Fine-Tune BERT for Text Classification?

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-05T05:46:09.690338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:46:09.690338Z digest=sha256:7699f9515ed98576e1c474c390a077ff8195632ba2ddf9f2e90c212b36ef5d3c

Observation c3165a80-ad5d-4682-9f1b-3a18f733598d · inbound

ADMEDTAGGER: an annotation framework for distillation of expert knowledge for the Polish medical language cites this paper.

ADMEDTAGGER: an annotation framework for distillation of expert knowledge for the Polish medical language How to Fine-Tune BERT for Text Classification?

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-21T15:54:14.530955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-21T15:53:11.348103Z digest=sha256:c79da3a73e36a894ad9dc959613ca434332311836a14622bd588d103866b57ab

Observation aede7dd5-0a44-48ae-9e09-71d11c5b2dac · inbound

Enhancing Construction Worker Safety in Extreme Heat: A Machine Learning Approach Utilizing Wearable Technology for Predictive Health Analytics cites this paper.

Enhancing Construction Worker Safety in Extreme Heat: A Machine Learning Approach Utilizing Wearable Technology for Predictive Health Analytics How to Fine-Tune BERT for Text Classification?

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-11T12:51:04.336365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T02:44:28.708504Z digest=sha256:e002f22eb42da339ffd85c2de9734bad9da05dc341ce33f789fad7d7b4e358e4

Observation 0b9ff41b-d278-418b-926a-365583541908 · inbound

Exploring Data Augmentation and Resampling Strategies for Transformer-Based Models to Address Class Imbalance in AI Scoring of Scientific Explanations in NGSS Classroom cites this paper.

Exploring Data Augmentation and Resampling Strategies for Transformer-Based Models to Address Class Imbalance in AI Scoring of Scientific Explanations in NGSS Classroom How to Fine-Tune BERT for Text Classification?

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-15T07:39:50.430827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-15T07:36:07.321806Z digest=sha256:298bf2d4debf5573175b17c8b0388f684d81f6b38a6e11a8a20da32e39c376d6

Observation cf110b5f-cb51-49f3-b8fe-c6051ee61527 · inbound

Response-free item difficulty modelling for multiple-choice items with fine-tuned transformers: Component-wise representation and multi-task learning cites this paper.

Response-free item difficulty modelling for multiple-choice items with fine-tuned transformers: Component-wise representation and multi-task learning How to Fine-Tune BERT for Text Classification?

Reference 170

Resolution
verified exact
arxiv_id, observed 2026-05-19T20:23:12.844150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-19T20:22:55.750693Z digest=sha256:71efe6c00bf4ea2f743f3c515b3f70034f8df8594c125195ec6640b6d7588397

Observation 242d29d0-cac7-45bb-a536-58d5d3555d9a · inbound

PortBERT: Navigating the Depths of Portuguese Language Models cites this paper.

PortBERT: Navigating the Depths of Portuguese Language Models How to Fine-Tune BERT for Text Classification?

Reference 9

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T23:06:20.124854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-06-28T14:43:28.401687Z digest=sha256:e9c7495b33e7d71e011605bd7aadc09bc91c24665101b729dcee50e4ce48e4d1