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Transformers: State-of-the-Art Natural Language Processing
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Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, Alexander Rush. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. 2020.
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“Almost all CLaMs have been made publicly accessible via online model repositories like HuggingFace57.”
From The shaky foundations of large language models and foundation models for electronic health records (2023) · cites this paper as [57]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Employing BERT, GPT-2, RoBERTa, and DistilBERT, we achieved robust results; BERT for deep contextual insights, GPT-2 for coherent text generation, RoBERTa for performance with large data, and DistilBERT for fast, cost-effective efficiency [39].”
From Generative AI-Powered Educational Alignment: A Framework for Matching Syllabus Course Topics with Web Description (2024) · cites this paper as [39]
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“Open-source ecosystems such as Hugging Face and BLOOM reduce technical barriers but can reproduce extractive defaults without Indigenous authority over datasets, objectives, and deployment pathways (BigScience Workshop, 2022;Wolf et al., 2020).”
From Towards a decolonial AI sovereignty model for indigenous educational settings (2026) · cites this paper as Wolf et al., 2020
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“Before the emergence of ChatGPT, BERT is a commonly used pre-trained machine learning model for NLP tasks (Wolf et al., 2020), and a ubiquitous baseline in text classification experiments of research in recent years (Rogers et al., 2021).”
From Towards reliable generative AI-driven scaffolding: Reducing hallucinations and enhancing quality in self-regulated learning support (2026) · cites this paper as Wolf et al., 2020
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“Tamarin is implemented in roughly 9K lines of Python on top of HuggingFace Transformers (Wolf et al., 2020); it uses no custom CUDA and does not modify model weights.”
From Tamarin (HSQ): Reversible Hierarchical KV-Cache Compression for LLM Inference (2026) · cites this paper as Wolf et al., 2020
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“3 speedup on GPT-2 (seq. length 1K)”
From FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness (2022) · cites this paper as [87]
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“Table 2 shows up to 3 endto-end speedup compared to Huggingface and 1.7 speedup compared to Megatron-LM.”
From FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness (2022) · cites this paper as [87]
Experimental — accuracy not yet measuredIn the citing paper
“While these early explorations are promising, they do not explore how much or what kind of interactivity is beneficial.”
From Interactive Model Cards: A Human-Centered Approach to Model Documentation (2022) · cites this paper as [73]
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“along with high-quality open-source libraries [113] have lowered the barrier to entry and make it possible to build conversational models that exhibit high generalization and coherence [90].”
From Future directions for chatbot research: An interdisciplinary research agenda (2021) · cites this paper as [113]
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Cites
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“, 2018) and the original source code for BERT (Devlin et al., 2018), both from Google Research.”
InfluentialRoBERTa: A Robustly Optimized BERT Pretraining Approach
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“…text corpora, leading to major gains in accuracy on downstream tasks including text classification (Yang et al., 2019), language understanding (Liu et al., 2019b; Wang et al., 2018, 2019), machine translation (Lample and Conneau, 2019a), coreference resolution (Joshi et al., 2019),…”
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The Stanford CoreNLP Natural Language Processing Toolkit
Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J. R., Bethard, S. J., & McClosky, D. (2014) · Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) · doi:10.3115/v1/p14-5010 · Cited by 7,252 · cited 1×
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XLNet: Generalized Autoregressive Pretraining for Language Understanding
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1906.08237 · Cited by 6,687 · cited 3×
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InfluentialDistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
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“While core models like BERT and GPT-2 continue to be popular, other specialized models including DistilBERT (Sanh et al., 2019), which was developed for the library, are now widely downloaded by the community.”
Self-citation
Research works printed but not linked: 2
- Alan Akbik, Tanja Bergmann, Duncan Blythe, Kashif Rasul, Stefan Schweter, and Roland Vollgraf. 2019. Flair: An easy-to-use framework for state-of-the-art nlp. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Com- putational Linguistics (Demonstrations), pages 54- 59. aclweb.org.
- Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. OpenAI Blog, 1(8):9.
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Zhang, Y., Zhu, H., Wang, Y., Xu, N., Li, X., & Zhao, B. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.336 · Cited by 65
A Systematic Review and Replicability Study of BERT4Rec for Sequential Recommendation
Petrov, A. V., & Macdonald, C. (2022) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3523227.3548487 · Cited by 64
Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards
Delbrouck, J.-B., Chambon, P., Bluethgen, C., Tsai, E., Almusa, O., & Langlotz, C. P. (2022) · arXiv · doi:10.48550/arxiv.2210.12186 · Cited by 63
Unveiling the Dark Side of ChatGPT: Exploring Cyberattacks and Enhancing User Awareness
Alawida, M., Shawar, B. A., Abiodun, O. I., Mehmood, A., Omolara, A. E., & Hwaitat, A. K. A. (2024) · Information · doi:10.3390/info15010027 · Cited by 82
Understanding LLMs: A comprehensive overview from training to inference
Liu, Y., He, H., Han, T., Zhang, X., Liu, M., Tian, J., Zhang, Y., Wang, J., Gao, X., Zhong, T., et al. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.129190 · Cited by 81
ReMemNN: A novel memory neural network for powerful interaction in aspect-based sentiment analysis
Liu, N., & Shen, B. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.02.018 · Cited by 66
Logic-Guided Data Augmentation and Regularization for Consistent Question Answering
Asai, A., & Hajishirzi, H. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.499 · Cited by 81
Automatic classification of scanned electronic health record documents
Goodrum, H., Roberts, K. E., & Bernstam, E. V. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104302 · Cited by 71
Glazkova, A. V., Glazkov, M., & Trifonov, T. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-73696-5_12 · Cited by 66
Inductive Relation Prediction by BERT
Zha, H., Chen, Z., & Yan, X. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i5.20537 · Cited by 67
A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation
Liu, T., Zhang, Y., Brockett, C., Mao, Y., Sui, Z., Chen, W., & Dolan, B. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.464 · Cited by 63
Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learning
Sheng, Q., Zhang, X., Cao, J., & Zhong, L. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482440 · Cited by 65
A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction
Xu, R., Wang, P., Liu, T., Zeng, S., Chang, B., & Sui, Z. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.370 · Cited by 65
CIRCLE: continual repair across programming languages
Yuan, W., Zhang, Q., He, T., Fang, C., Nguyen, Q. V. H., Hao, X., & Yin, H. (2022) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3533767.3534219 · Cited by 82
FaithDial : A Faithful Benchmark for Information-Seeking Dialogue
Dziri, N., Kamalloo, E., Milton, S., Zai͏̈ane, O. R., Yu, M., Ponti, E. M., & Reddy, S. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00529 · Cited by 86
Natural Language Processing Applications for Computer-Aided Diagnosis in Oncology
Li, C., Zhang, Y., Weng, Y., Wang, B., & Li, Z. (2023) · Diagnostics · doi:10.3390/diagnostics13020286 · Cited by 64
Retrieval Augmented Visual Question Answering with Outside Knowledge
Lin, W., & Byrne, B. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.772 · Cited by 126
MRNDR: Multihead Attention-Based Recommendation Network for Drug Repurposing
Feng, X., Ma, Z., Yu, C., & Xin, R. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01726 · Cited by 67
Mansoor, M. A., & Ansari, K. (2024) · Journal of Personalized Medicine · doi:10.3390/jpm14090958 · Cited by 91
Text Analysis for Psychology: Methods, Principles, and PracticesPreprint
Kennedy, B., Ashokkumar, A., Boyd, R. L., & Dehghani, M. (2021) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/h2b8t · Cited by 62
Cell2Sentence: Teaching Large Language Models the Language of BiologyPreprint
Levine, D. M., Rizvi, S. A., Lévy, S., Pallikkavaliyaveetil, N., Zhang, D., Chen, X., Ghadermarzi, S., Wu, R., Zheng, Z., Vrkic, I., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.09.11.557287 · Cited by 68
Park, S. H., Suh, C. H., Lee, J. H., Kahn, C. E., & Moy, L. P. (2024) · Korean Journal of Radiology · doi:10.3348/kjr.2024.0843 · Cited by 63
Effects of Age on Cortical Tracking of Word-Level Features of Continuous Competing Speech
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Visual Semantic Role Labeling for Video Understanding
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Modeling Fine-Grained Entity Types with Box Embeddings
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Chandra, R., & Kulkarni, V. (2022) · IEEE Access · doi:10.1109/access.2022.3152266 · Cited by 71
Practical Binary Code Similarity Detection with BERT-based Transferable Similarity Learning
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Sparse Low-rank Adaptation of Pre-trained Language Models
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MedExpQA: Multilingual benchmarking of Large Language Models for Medical Question Answering
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A proposed method for landslide detection based on transfer learning and graph neural network
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Cloud-Based Deep Learning for Co-Estimation of Battery State of Charge and State of Health
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Legal Knowledge Extraction for Knowledge Graph Based Question-Answering
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Model Extraction and Adversarial Transferability, Your BERT is Vulnerable!
He, X., Lyu, L., Sun, L., & Xu, Q. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.161 · Cited by 88
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Metadata
- Authors
- Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., von Platen, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Le Scao, T., Gugger, S., Drame, M., Lhoest, Q., & Rush, A.
- Affiliations
- Hugging Face (US)
- ORCID
- Venue
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
- Published
- 2020
- Pages
- 38-45
- OpenAlex
- W2979826702
- Cited by
- 8,741
- References
- 39
- Citations by year
- 2026: 851 · 2025: 1481 · 2024: 1405 · 2023: 2347 · 2022: 1686 · 2021: 872 · 2020: 94
- FWCI
- 477.50
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- transformer models, natural language processing, Hugging Face
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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