Transformers: State-of-the-Art Natural Language Processing
By Thomas Wolf, Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf and 14 more. 2020
24 citations recorded by the source corpus.
Recent progress in natural language processing has been driven by advances in both model architecture and model pretraining. Transformer architectures have facilitated building higher-capacity models and pretraining has made it possible to effectively utilize this capacity for a wide variety of tasks. Transformers is an open-source library with the goal of opening up these advances to the wider machine learning community. The library consists of carefully engineered state-of-the art Transformer architectures under a unified API. Backing this library is a curated collection of pretrained models made by and available for the community. Transformers is designed to be extensible by researchers, simple for practitioners, and fast and robust in industrial deployments. The library is available at https://github.com/huggingface/transformers.
Subfields: Artificial Intelligence, Natural Language Processing, Model Architectures
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Citations in the corpus
- Language Models are Few-Shot Butlers (2021, positive): “Experiments were implemented with the Trans-formers (Wolf et al., 2020) and PyTorch (Paszke et al., 2019) libraries and were conducted on an NVidia RTX 3090.”
- MSˆ2: Multi-Document Summarization of Medical Studies (2021, unknown): “Using the EBM-NLP corpus (Nye et al., 2018), a crowd-sourced collection of PIO tags, 4 we train a token classification model (Wolf et al., 2020) to identify these spans in our study and review documents.”