Victor Sanh


2022

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PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts
Stephen Bach | Victor Sanh | Zheng Xin Yong | Albert Webson | Colin Raffel | Nihal V. Nayak | Abheesht Sharma | Taewoon Kim | M Saiful Bari | Thibault Fevry | Zaid Alyafeai | Manan Dey | Andrea Santilli | Zhiqing Sun | Srulik Ben-david | Canwen Xu | Gunjan Chhablani | Han Wang | Jason Fries | Maged Al-shaibani | Shanya Sharma | Urmish Thakker | Khalid Almubarak | Xiangru Tang | Dragomir Radev | Mike Tian-jian Jiang | Alexander Rush
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

PromptSource is a system for creating, sharing, and using natural language prompts. Prompts are functions that map an example from a dataset to a natural language input and target output. Using prompts to train and query language models is an emerging area in NLP that requires new tools that let users develop and refine these prompts collaboratively. PromptSource addresses the emergent challenges in this new setting with (1) a templating language for defining data-linked prompts, (2) an interface that lets users quickly iterate on prompt development by observing outputs of their prompts on many examples, and (3) a community-driven set of guidelines for contributing new prompts to a common pool. Over 2,000 prompts for roughly 170 datasets are already available in PromptSource. PromptSource is available at https://github.com/bigscience-workshop/promptsource.

2021

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Low-Complexity Probing via Finding Subnetworks
Steven Cao | Victor Sanh | Alexander Rush
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

The dominant approach in probing neural networks for linguistic properties is to train a new shallow multi-layer perceptron (MLP) on top of the model’s internal representations. This approach can detect properties encoded in the model, but at the cost of adding new parameters that may learn the task directly. We instead propose a subtractive pruning-based probe, where we find an existing subnetwork that performs the linguistic task of interest. Compared to an MLP, the subnetwork probe achieves both higher accuracy on pre-trained models and lower accuracy on random models, so it is both better at finding properties of interest and worse at learning on its own. Next, by varying the complexity of each probe, we show that subnetwork probing Pareto-dominates MLP probing in that it achieves higher accuracy given any budget of probe complexity. Finally, we analyze the resulting subnetworks across various tasks to locate where each task is encoded, and we find that lower-level tasks are captured in lower layers, reproducing similar findings in past work.

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Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning
Prasetya Utama | Nafise Sadat Moosavi | Victor Sanh | Iryna Gurevych
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Recent prompt-based approaches allow pretrained language models to achieve strong performances on few-shot finetuning by reformulating downstream tasks as a language modeling problem. In this work, we demonstrate that, despite its advantages on low data regimes, finetuned prompt-based models for sentence pair classification tasks still suffer from a common pitfall of adopting inference heuristics based on lexical overlap, e.g., models incorrectly assuming a sentence pair is of the same meaning because they consist of the same set of words. Interestingly, we find that this particular inference heuristic is significantly less present in the zero-shot evaluation of the prompt-based model, indicating how finetuning can be destructive to useful knowledge learned during the pretraining. We then show that adding a regularization that preserves pretraining weights is effective in mitigating this destructive tendency of few-shot finetuning. Our evaluation on three datasets demonstrates promising improvements on the three corresponding challenge datasets used to diagnose the inference heuristics.

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Block Pruning For Faster Transformers
François Lagunas | Ella Charlaix | Victor Sanh | Alexander Rush
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Pre-training has improved model accuracy for both classification and generation tasks at the cost of introducing much larger and slower models. Pruning methods have proven to be an effective way of reducing model size, whereas distillation methods are proven for speeding up inference. We introduce a block pruning approach targeting both small and fast models. Our approach extends structured methods by considering blocks of any size and integrates this structure into the movement pruning paradigm for fine-tuning. We find that this approach learns to prune out full components of the underlying model, such as attention heads. Experiments consider classification and generation tasks, yielding among other results a pruned model that is a 2.4x faster, 74% smaller BERT on SQuAD v1, with a 1% drop on F1, competitive both with distilled models in speed and pruned models in size.

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Datasets: A Community Library for Natural Language Processing
Quentin Lhoest | Albert Villanova del Moral | Yacine Jernite | Abhishek Thakur | Patrick von Platen | Suraj Patil | Julien Chaumond | Mariama Drame | Julien Plu | Lewis Tunstall | Joe Davison | Mario Šaško | Gunjan Chhablani | Bhavitvya Malik | Simon Brandeis | Teven Le Scao | Victor Sanh | Canwen Xu | Nicolas Patry | Angelina McMillan-Major | Philipp Schmid | Sylvain Gugger | Clément Delangue | Théo Matussière | Lysandre Debut | Stas Bekman | Pierric Cistac | Thibault Goehringer | Victor Mustar | François Lagunas | Alexander Rush | Thomas Wolf
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

The scale, variety, and quantity of publicly-available NLP datasets has grown rapidly as researchers propose new tasks, larger models, and novel benchmarks. Datasets is a community library for contemporary NLP designed to support this ecosystem. Datasets aims to standardize end-user interfaces, versioning, and documentation, while providing a lightweight front-end that behaves similarly for small datasets as for internet-scale corpora. The design of the library incorporates a distributed, community-driven approach to adding datasets and documenting usage. After a year of development, the library now includes more than 650 unique datasets, has more than 250 contributors, and has helped support a variety of novel cross-dataset research projects and shared tasks. The library is available at https://github.com/huggingface/datasets.

2020

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Transformers: State-of-the-Art Natural Language Processing
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

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.