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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.
In the citing paper
“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
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · cited 2×
“The Transformer (Vaswani et al., 2017) has rapidly become the dominant architecture for natural language processing, surpassing alternative neural models such as convolutional and recurrent neural networks in performance for tasks in both natural language understanding and natural language…”
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173 · cited 4×
“, 2018) and the original source code for BERT (Devlin et al., 2018), both from Google Research.”
InfluentialRoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515 · cited 3×
“…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),…”
InfluentialDeep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983 · cited 1×
“Model pretraining (McCann et al., 2017; Howard and Ruder, 2018; Peters et al., 2018; Devlin et al., 2018) allows models to be trained on generic corpora and subsequently be easily adapted to specific tasks with strong performance.”
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929 · cited 2×
“, 2019) T5 (Raffel et al., 2019) MarianMT (J.”
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.703 · Cited by 8,821
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×
“Two core libraries are NLTK (Loper and Bird, 2002) and Stanford CoreNLP (Manning et al., 2014), which collect a variety of different approaches to NLP in a single package.”
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×
“The Transformer architecture is particularly conducive to pretraining on large text corpora, leading to major gains in accuracy on downstream tasks including text classification (Yang et al., 2019), language understanding (Liu et al.”
InfluentialDistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019) · arXiv · doi:10.48550/arxiv.1910.01108 · Cited by 4,653 · cited 2×
“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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Self-citationPolitical Ideology and Polarization: A Multi-dimensional Approach
Sinno, B., Oviedo, B., Atwell, K., Alikhani, M., & Li, J. J. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.17 · Cited by 12
Adekkanattu, P., Furmanchuk, A., Wu, Y., Pathak, A., Patra, B. G., Bost, S., Morrow, D., Wang, G. H.-M., Yang, Y., Forrest, N. J., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01266-7 · Cited by 12
TÜLU 3: Pushing Frontiers in Open Language Model Post-Training
Lambert, N., Morrison, J., Pyatkin, V., Huang, S., Ivison, H., Brahman, F., Miranda, L. J. V., Liu, A., Dziri, N., Lyu, S., et al. (2024) · arXiv · doi:10.48550/arxiv.2411.15124 · Cited by 12
Validating Large Language Models with ReLM
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MOPRD: A multidisciplinary open peer review dataset
Lin, J., Song, J., Zhou, Z., Chen, Y., & Shi, X. (2022) · arXiv · doi:10.48550/arxiv.2212.04972 · Cited by 11
LEGO-GraphRAG: Modularizing Graph-Based Retrieval-Augmented Generation for Design Space Exploration
Cao, Y., Gao, Z., Li, Z., Xie, X., Zhou, S. K., & Xu, J. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3748191.3748194 · Cited by 11
Al-Hussaini, I., Nakajima An, D., Lee, A. J., Bi, S., & Mitchell, C. S. (2022) · 2022 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata55660.2022.10020807 · Cited by 10
Universality of representation in biological and artificial neural networks
Hosseini, E., Casto, C., Zaslavsky, N., Conwell, C., Richardson, M., & Fedorenko, E. (2024) · doi:10.1101/2024.12.26.629294 · Cited by 10
Gekhman, Z., Oved, N., Keller, O., Szpektor, I., & Reichart, R. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00549 · Cited by 9
Large Language Models can Implement Policy Iteration
Brooks, E., Walls, L., Lewis, R. L., & Singh, S. (2022) · arXiv · doi:10.48550/arxiv.2210.03821 · Cited by 9
The Ecological Fallacy in Annotation: Modeling Human Label Variation goes beyond Sociodemographics
Orlikowski, M., Röttger, P., Cimiano, P., & Hovy, D. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/2023.acl-short.88 · Cited by 9
Robust Deepfake Audio Detection via Bi-Level Optimization
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Named entity recognition of pharmacokinetic parameters in the scientific literature
Gonzalez Hernandez, F., Nguyen, Q., Smith, V. C., Cordero, J. A., Ballester, M. R., Duran, M., Solé, A., Chotsiri, P., Wattanakul, T., Mundin, G., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-73338-3 · Cited by 8
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Rhys Leahy, R. F. S., J. Restrepo, N., Lupu, Y., & F. Johnson, N. (2021) · Advances in Artificial Intelligence and Machine Learning · doi:10.54364/aaiml.2021.1112 · Cited by 5
Towards a decolonial AI sovereignty model for indigenous educational settings
Mbah, M. F., & Jarvis, L. (2026) · Cogent Education · doi:10.1080/2331186x.2026.2626631 · Cited by 7
Enabling Efficient Batch Serving for LMaaS via Generation Length Prediction
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A Logical Fallacy-Informed Framework for Argument Generation
Mouchel, L., Paul, D., Cui, S., West, R., Bosselut, A., & Faltings, B. (2025) · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · doi:10.18653/v1/2025.naacl-long.374 · Cited by 7
Petals: Collaborative Inference and Fine-tuning of Large Models
Borzunov, A., Baranchuk, D., Dettmers, T., Ryabinin, M., Belkada, Y., Chumachenko, A., Samygin, P., & Raffel, C. (2022) · arXiv · doi:10.48550/arxiv.2209.01188 · Cited by 6
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Chen, W., Huang, Z., Xie, L., Lin, B., Li, H., Lu, L., Tian, X., Cai, D., Zhang, Y., Wang, W., et al. (2024) · arXiv · doi:10.48550/arxiv.2409.01658 · Cited by 5
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Bobart, S. A., Hsu, E., Potter, T., Truong, L., Waterman, A., Jones, S., & Shafi, T. (2025) · Kidney Medicine · doi:10.1016/j.xkme.2025.101047 · Cited by 5
Gilani, K., Verket, M., Peters, C., Dumontier, M., Rocca, H.-P. B.-L., & Urovi, V. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110745 · Cited by 5
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Modarressi, A., Deilamsalehy, H., Dernoncourt, F., Bui, T., Rossi, R. A., Yoon, S., & Schütze, H. (2025) · arXiv · doi:10.48550/arxiv.2502.05167 · Cited by 3
Liu, L., Mendoza, R. A., Martin, T. R., & Miori, V. M. (2024) · Proceedings of the 2024 9th International Conference on Distance Education and Learning · doi:10.1145/3675812.3675874 · Cited by 4
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Park, G., Lee, S., & Park, Y. (2026) · IEEE Access · doi:10.1109/access.2026.3664226 · Cited by 4
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Maharana, A., Lee, D.-H., Tulyakov, S., Bansal, M., Barbieri, F., & Fang, Y. (2024) · arXiv · doi:10.48550/arxiv.2402.17753 · Cited by 3
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Li, H., Choi, J., & Ahn, J. H. (2022) · 2022 IEEE International Symposium on Workload Characterization (IISWC) · doi:10.1109/iiswc55918.2022.00019 · Cited by 3
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Karimi, P., Ballard, K., Vazirani, P., Jorigay, R. T. N., & Martin-Hammond, A. (2022) · Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-99194-4_17 · Cited by 3
CacheBlend: Fast Large Language Model Serving for RAG with Cached Knowledge Fusion
Yao, J., Li, H., Liu, Y., Ray, S., Cheng, Y., Zhang, Q., Du, K., Lu, S., & Jiang, J. (2026) · ACM Transactions on Computer Systems · doi:10.1145/3790254 · Cited by 3
Nano: Nested Human-in-the-Loop Reward Learning for Few-shot Language Model Control
Fan, X., Lyu, Y., Liang, P. P., Salakhutdinov, R., & Morency, L.-P. (2023) · Findings of the Association for Computational Linguistics: ACL 2023 · doi:10.18653/v1/2023.findings-acl.758 · Cited by 3
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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