Paper Citation Record · LEDGER

On the Compression of Language Models for Code: An Empirical Study on CodeBERT

As of 12 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 2 inbound Pith citation observations for arXiv:2412.13737.

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

pith.paper-citation-record.v1
2412.13737 v1

Coverage vector

measured 65 of 65 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:53:58.558058Z

measured 67 of 67 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-19T00:01:42.228190Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T00:01:55.825781Z

Reference resolution

65 of 65 outbound references displayed

verified exact4
verified fuzzy23
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c2455cf9-309e-406d-a516-80ca50386ce0 · outbound

This paper cites Vulnerability Detection with Code Language Models: How Far Are We?.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Vulnerability Detection with Code Language Models: How Far Are We?

Reference 1

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Observation c7ecfc24-c462-44d8-bb38-5dbb8a14a472 · outbound

This paper cites When neural code completion models size up the situation: Attaining cheaper and faster completion through dynamic model inference,.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT When neural code completion models size up the situation: Attaining cheaper and faster completion through dynamic model inference,

Reference 2

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Observation 8b3803c4-64bf-44af-a350-17be321e0458 · outbound

This paper cites Code search is all you need? improving code suggestions with code search,.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Code search is all you need? improving code suggestions with code search,

Reference 3

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Observation d46ee9ab-d462-46c6-808c-a5ab22df9f69 · outbound

This paper cites Large Language Models for Software Engineering: A Systematic Literature Review.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Large Language Models for Software Engineering: A Systematic Literature Review

Reference 4

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Observation 7f3a86ce-2b66-4a08-ba29-94541eaafcef · outbound

This paper cites Green AI.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Green AI

Reference 5

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Observation 8a14d1cd-7bd2-44e9-913c-7740ebb3381b · outbound

This paper cites Distilling the Knowledge in a Neural Network.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Distilling the Knowledge in a Neural Network

Reference 6

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Observation e09e6b3c-6b3a-4e47-aab4-fbd9074503ee · outbound

This paper cites Q8bert: Quantized 8bit bert,.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Q8bert: Quantized 8bit bert,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:54:00.576690Z

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Observation 753816b8-59ba-4f4d-b58e-8b94dd7d3a68 · outbound

This paper cites Movement pruning: adaptive sparsity by fine-tuning,.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Movement pruning: adaptive sparsity by fine-tuning,

Reference 8

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Observation cb7d36ad-a73f-4cb9-9e6e-f20a9d3a6b86 · outbound

This paper cites Compressing pre-trained models of code into 3 mb,.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Compressing pre-trained models of code into 3 mb,

Reference 9

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Observation a035d1c4-1ab7-4b73-9588-5f2f12fd79f4 · outbound

This paper cites Greening large language models of code,.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Greening large language models of code,

Reference 10

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Observation 038fc14a-f56c-4821-b7dd-6a689cbc23b8 · outbound

This paper cites Towards greener yet powerful code generation via quantization: An empirical study,.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Towards greener yet powerful code generation via quantization: An empirical study,

Reference 11

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Observation 341f6f43-286b-49ba-876d-5d8121b9fec9 · outbound

This paper cites CodeBERT: A pre-trained model for programming and natural languages,.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT CodeBERT: A pre-trained model for programming and natural languages,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:54:00.554585Z

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Observation abb3af8b-a26d-426e-911f-966e8e53d416 · outbound

This paper cites On the compression of language models for code: An empirical study on codebert,.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT On the compression of language models for code: An empirical study on codebert,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:54:00.541657Z

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Observation 593526d5-2e95-4dad-919c-99eb9c506b1d · outbound

This paper cites Attention is all you need,.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Attention is all you need,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:54:00.527251Z

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Observation 6f5138c0-cf67-4ce6-b285-5e869e114520 · outbound

This paper cites Language models are unsupervised multitask learners,.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Language models are unsupervised multitask learners,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:54:00.514436Z

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Observation 55696f82-e0db-43dd-b5d0-3e0af0cf9295 · outbound

This paper cites Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Reference 16

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Observation 70f4f395-95a0-41c0-87ff-dbdfd64a8a06 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T12:53:58.364796Z

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Observation 76762ff5-41fe-4384-8a60-0b65a35ef404 · outbound

This paper cites CodeT5+: Open code large language models for code understanding and generation,.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT CodeT5+: Open code large language models for code understanding and generation,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:54:00.501544Z

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Observation f68a963d-da9f-4799-b257-ced534676849 · outbound

This paper cites Evaluating large language models trained on code,.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Evaluating large language models trained on code,

Reference 19

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Observation 9eefc90f-e0df-492e-b5fa-0c513438e02e · outbound

This paper cites Assemble foundation models for automatic code summarization,.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Assemble foundation models for automatic code summarization,

Reference 20

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Observation ab40e0e3-b260-4127-a4d2-7ea31f3ff8e6 · outbound

This paper cites Assessing generalizability of codebert,.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Assessing generalizability of codebert,

Reference 21

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Observation 1f6dbad3-5ba2-4e0f-8bff-31f2036694c7 · outbound

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On the Compression of Language Models for Code: An Empirical Study on CodeBERT Optimal brain damage,

Reference 23

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Observation 887a4111-d1d9-4ecd-a647-3d49c8ed700d · outbound

This paper cites Pruning filters for efficient convnets,.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Pruning filters for efficient convnets,

Reference 24

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Observation 320d121c-e9d7-4c0a-bfc3-4471265442f9 · outbound

This paper cites Dynamic Network Surgery for Efficient DNNs.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Dynamic Network Surgery for Efficient DNNs

Reference 25

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Observation 2872e01e-0f41-4754-87e3-d4b7ca29e739 · outbound

This paper cites Pruning Convolutional Neural Networks for Resource Efficient Inference.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Pruning Convolutional Neural Networks for Resource Efficient Inference

Reference 26

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This paper cites The State of Sparsity in Deep Neural Networks.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT The State of Sparsity in Deep Neural Networks

Reference 27

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Observation 6e2e1221-2449-4c0b-8795-adfa003f53df · outbound

This paper cites GraphCodeBERT: Pre-training Code Representations with Data Flow.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT GraphCodeBERT: Pre-training Code Representations with Data Flow

Reference 28

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Observation 2dbfc7eb-1916-46a7-ab02-f0e7a6f14f3d · outbound

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On the Compression of Language Models for Code: An Empirical Study on CodeBERT Software defect prediction via transformer,

Reference 29

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Observation 987572d4-b363-458f-b5f3-cd2f89db8999 · outbound

This paper cites TranS^3: A Transformer-based Framework for Unifying Code Summarization and Code Search.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT TranS^3: A Transformer-based Framework for Unifying Code Summarization and Code Search

Reference 30

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Observation 47819a14-d83a-45d2-9173-5d07eca2e617 · outbound

This paper cites A coefficient of agreement as a measure of thematic classification accuracy.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT A coefficient of agreement as a measure of thematic classification accuracy

Reference 31

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Observation f5b86270-3b75-4363-a5d2-b54c7dd4f8c0 · outbound

This paper cites Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool,.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool,

Reference 32

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This paper cites The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation,.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation,

Reference 33

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This paper cites Bleu: a Method for Automatic Evaluation of Machine Translation,.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Bleu: a Method for Automatic Evaluation of Machine Translation,

Reference 34

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Observation 460f0f8e-e056-4e7e-ad51-2edcc9c8ce92 · outbound

This paper cites BERTScore: Evaluating Text Generation with BERT.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT BERTScore: Evaluating Text Generation with BERT

Reference 35

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This paper cites Evaluating Code Summarization Techniques: A New Metric and an Empirical Characterization.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Evaluating Code Summarization Techniques: A New Metric and an Empirical Characterization

Reference 36

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Observation acf27b70-d1fb-4ddb-99d1-f6325e2b4bfc · outbound

This paper cites Expected reciprocal rank for graded relevance,.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Expected reciprocal rank for graded relevance,

Reference 37

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This paper cites Devign: Effective vulnerability identi- fication by learning comprehensive program semantics via graph neural networks,.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Devign: Effective vulnerability identi- fication by learning comprehensive program semantics via graph neural networks,

Reference 38

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Observation 840fb041-545e-426e-b093-2e6991ab0adb · outbound

This paper cites Available: https://doi.ieeecomputersociety.org/10.1109/ EMC2-NIPS53020.2019.00016.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Available: https://doi.ieeecomputersociety.org/10.1109/ EMC2-NIPS53020.2019.00016

Reference 39

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Observation 98e0e6cd-74d6-42e1-93eb-1d236db102a6 · outbound

This paper cites CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation

Reference 40

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This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 41

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This paper cites Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning

Reference 42

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This paper cites CodeSearchNet Challenge: Evaluating the State of Semantic Code Search.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT CodeSearchNet Challenge: Evaluating the State of Semantic Code Search

Reference 43

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This paper cites Ai-driven java performance testing: Balancing result quality with testing time,.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Ai-driven java performance testing: Balancing result quality with testing time,

Reference 44

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On the Compression of Language Models for Code: An Empirical Study on CodeBERT Automated generation and evaluation of jmh microbenchmark suites from unit tests,

Reference 45

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This paper cites Faster or slower? performance mystery of python idioms unveiled with empirical evidence,.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Faster or slower? performance mystery of python idioms unveiled with empirical evidence,

Reference 46

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On the Compression of Language Models for Code: An Empirical Study on CodeBERT Pruning filters with l1-norm and capped l1-norm for cnn compression,

Reference 47

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On the Compression of Language Models for Code: An Empirical Study on CodeBERT Rigorous benchmarking in reasonable time,

Reference 48

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On the Compression of Language Models for Code: An Empirical Study on CodeBERT Quantifying performance changes with effect size confidence intervals,

Reference 49

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On the Compression of Language Models for Code: An Empirical Study on CodeBERT Wilcoxon signed-rank test,

Reference 50

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Observation a5eb672f-52c6-420f-bbc8-791c4f11155d · outbound

This paper cites Dynamically reconfiguring software microbenchmarks: Reducing execution time without sacrificing result quality,.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Dynamically reconfiguring software microbenchmarks: Reducing execution time without sacrificing result quality,

Reference 51

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Observation 8e6d9bd0-4edb-4cee-a99d-c62ca8612635 · outbound

This paper cites Searching for a needle in a haystack: Predicting security vulnerabilities for windows vista,.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT Searching for a needle in a haystack: Predicting security vulnerabilities for windows vista,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:54:00.299941Z

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Observation 8f5a4ee9-ca34-4d42-8bff-8f93f6312bcb · outbound

This paper cites The importance of accounting for real-world labelling when predicting software vulnerabilities,.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT The importance of accounting for real-world labelling when predicting software vulnerabilities,

Reference 53

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Observation 7ddddd7e-f99d-4e84-a7f5-296e17982e38 · outbound

This paper cites On the use of evaluation measures for defect prediction studies,.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT On the use of evaluation measures for defect prediction studies,

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On the Compression of Language Models for Code: An Empirical Study on CodeBERT How software refactoring impacts execution time,

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On the Compression of Language Models for Code: An Empirical Study on CodeBERT Survey of code search based on deep learning,

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On the Compression of Language Models for Code: An Empirical Study on CodeBERT A survey of quantization methods for efficient neural network inference,

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On the Compression of Language Models for Code: An Empirical Study on CodeBERT Up or Down? Adaptive Rounding for Post-Training Quantization

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On the Compression of Language Models for Code: An Empirical Study on CodeBERT Source Code Summarization in the Era of Large Language Models

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On the Compression of Language Models for Code: An Empirical Study on CodeBERT Rethinking the Value of Network Pruning

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On the Compression of Language Models for Code: An Empirical Study on CodeBERT Taming performance variability,

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On the Compression of Language Models for Code: An Empirical Study on CodeBERT Compressing large-scale transformer- based models: A case study on bert,

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On the Compression of Language Models for Code: An Empirical Study on CodeBERT Available: https://doi.ieeecomputersociety.org/10.1109/ SANER53432.2022.00112

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On the Compression of Language Models for Code: An Empirical Study on CodeBERT Available: https://openreview.net/forum?id=rJqFGTslg

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On the Compression of Language Models for Code: An Empirical Study on CodeBERT Available: https://doi.org/10.1145/3628161

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On the Compression of Language Models for Code: An Empirical Study on CodeBERT Available: https://doi.org/10.5281/zenodo.14357478

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Model Compression vs. Adversarial Robustness: An Empirical Study on Language Models for Code cites this paper.

Model Compression vs. Adversarial Robustness: An Empirical Study on Language Models for Code On the Compression of Language Models for Code: An Empirical Study on CodeBERT

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A Metamorphic Testing Perspective on Knowledge Distillation for Language Models of Code: Does the Student Deeply Mimic the Teacher? cites this paper.

A Metamorphic Testing Perspective on Knowledge Distillation for Language Models of Code: Does the Student Deeply Mimic the Teacher? On the Compression of Language Models for Code: An Empirical Study on CodeBERT

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