Bhavitvya Malik


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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

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Analyzing the Domain Robustness of Pretrained Language Models, Layer by Layer
Abhinav Ramesh Kashyap | Laiba Mehnaz | Bhavitvya Malik | Abdul Waheed | Devamanyu Hazarika | Min-Yen Kan | Rajiv Ratn Shah
Proceedings of the Second Workshop on Domain Adaptation for NLP

The robustness of pretrained language models(PLMs) is generally measured using performance drops on two or more domains. However, we do not yet understand the inherent robustness achieved by contributions from different layers of a PLM. We systematically analyze the robustness of these representations layer by layer from two perspectives. First, we measure the robustness of representations by using domain divergence between two domains. We find that i) Domain variance increases from the lower to the upper layers for vanilla PLMs; ii) Models continuously pretrained on domain-specific data (DAPT)(Gururangan et al., 2020) exhibit more variance than their pretrained PLM counterparts; and that iii) Distilled models (e.g., DistilBERT) also show greater domain variance. Second, we investigate the robustness of representations by analyzing the encoded syntactic and semantic information using diagnostic probes. We find that similar layers have similar amounts of linguistic information for data from an unseen domain.