The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Transformers: State-of-the-Art Natural Language Processing
Open accessAbstract
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]
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
“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
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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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In the citing paper
“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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In the citing paper
“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]
In the citing paper
“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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Citations
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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Attribute or Abstain: Large Language Models as Long Document Assistants
Buchmann, J., Liu, X., & Gurevych, I. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2024.emnlp-main.463 · Cited by 3
Interpreting pretrained speech models for automatic speech assessment of voice disorders
Lau, H. S., Huntly, M., Morgan, N., Iyenoma, A., Zeng, B., & Bashford, T. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-67278-1_5 · Cited by 2
CLIMB – Curriculum Learning for Infant-inspired Model Building
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Elastic Weight Removal for Faithful and Abstractive Dialogue Generation
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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
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