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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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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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Alam, F., Shaar, S., Dalvi, F., Sajjad, H., Nikolov, A., Mubarak, H., Martino, G. D. S., Abdelalí, A., Durrani, N., Darwish, K. M., et al. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.56 · Cited by 144
OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning
Ye, R., Wang, W., Chai, J., Li, D., Li, Z., Xu, Y., Du, Y., Wang, Y., & Chen, S. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671582 · Cited by 195
Noisy Channel Language Model Prompting for Few-Shot Text Classification
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CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization
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Chest X-ray pneumothorax segmentation using U-Net with EfficientNet and ResNet architectures
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Mulla, N., & Gharpure, P. (2023) · Progress in Artificial Intelligence · doi:10.1007/s13748-023-00295-9 · Cited by 132
COVID-19 sentiment analysis via deep learning during the rise of novel cases
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Unified Deep Learning Model for Multitask Reaction Predictions with Explanation
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Contrast and Generation Make BART a Good Dialogue Emotion Recognizer
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BERT based clinical knowledge extraction for biomedical knowledge graph construction and analysis
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Unifying Vision, Text, and Layout for Universal Document Processing
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Ogueji, K., Zhu, Y., & Lin, J. (2021) · Workshop on Multilingual Representation Learning (MRL) · doi:10.18653/v1/2021.mrl-1.11 · Cited by 141
Li, S., & Hoefler, T. (2021) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1145/3458817.3476145 · Cited by 148
Cosler, M., Hahn, C., Mendoza, D., Schmitt, F., & Trippel, C. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-37703-7_18 · Cited by 169
Generating Datasets with Pretrained Language Models
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Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization
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Neuro-Symbolic Speech Understanding in Aircraft Maintenance Metaverse
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Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering
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WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation
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Deep learning for accelerated and robust MRI reconstruction
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BioELECTRA:Pretrained Biomedical text Encoder using Discriminators
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Large-scale evidence for logarithmic effects of word predictability on reading time
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End-to-end generation of multiple-choice questions using text-to-text transfer transformer models
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FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks
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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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