• Corpus ID: 204509627

HuggingFace's Transformers: State-of-the-art Natural Language Processing

@article{Wolf2019HuggingFacesTS,
  title={HuggingFace's Transformers: State-of-the-art Natural Language Processing},
  author={Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and R{\'e}mi Louf and Morgan Funtowicz and Jamie Brew},
  journal={ArXiv},
  year={2019},
  volume={abs/1910.03771}
}
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. \textit{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… 

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References

SHOWING 1-10 OF 80 REFERENCES

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

TLDR
A new language representation model, BERT, designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks.

AllenNLP: A Deep Semantic Natural Language Processing Platform

TLDR
AllenNLP is described, a library for applying deep learning methods to NLP research that addresses issues with easy-to-use command-line tools, declarative configuration-driven experiments, and modular NLP abstractions.

Reformer: The Efficient Transformer

TLDR
This work replaces dot-product attention by one that uses locality-sensitive hashing and uses reversible residual layers instead of the standard residuals, which allows storing activations only once in the training process instead of several times, making the model much more memory-efficient and much faster on long sequences.

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

TLDR
This systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks and achieves state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more.

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

TLDR
This work proposes a method to pre-train a smaller general-purpose language representation model, called DistilBERT, which can be fine-tuned with good performances on a wide range of tasks like its larger counterparts, and introduces a triple loss combining language modeling, distillation and cosine-distance losses.

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

TLDR
This work presents two parameter-reduction techniques to lower memory consumption and increase the training speed of BERT, and uses a self-supervised loss that focuses on modeling inter-sentence coherence.

SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems

TLDR
A new benchmark styled after GLUE is presented, a new set of more difficult language understanding tasks, a software toolkit, and a public leaderboard are presented.

Transformer-XL: Attentive Language Models beyond a Fixed-Length Context

TLDR
This work proposes a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence, which consists of a segment-level recurrence mechanism and a novel positional encoding scheme.

exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformer Models

TLDR
ExBERT provides insights into the meaning of the contextual representations and attention by matching a human-specified input to similar contexts in large annotated datasets, and can quickly replicate findings from literature and extend them to previously not analyzed models.

Transfer Learning in Natural Language Processing

TLDR
An overview of modern transfer learning methods in NLP, how models are pre-trained, what information the representations they learn capture, and review examples and case studies on how these models can be integrated and adapted in downstream NLP tasks are presented.
...