Cost-effective Selection of Pretraining Data: A Case Study of Pretraining BERT on Social Media

• URL: http://arxiv.org/abs/2010.01150v1
• Date: Fri, 2 Oct 2020 18:06:31 GMT
• Title: Cost-effective Selection of Pretraining Data: A Case Study of Pretraining BERT on Social Media
• Authors: Xiang Dai and Sarvnaz Karimi and Ben Hachey and Cecile Paris
• Abstract summary: Recent studies on domain-specific BERT models show that effectiveness on downstream tasks can be improved when models are pretrained on in-domain data. We pretrain two models on tweets and forum text respectively, and empirically demonstrate the effectiveness of these two resources. In addition, we investigate how similarity measures can be used to nominate in-domain pretraining data.
• Score: 18.21146856681127
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent studies on domain-specific BERT models show that effectiveness on downstream tasks can be improved when models are pretrained on in-domain data. Often, the pretraining data used in these models are selected based on their subject matter, e.g., biology or computer science. Given the range of applications using social media text, and its unique language variety, we pretrain two models on tweets and forum text respectively, and empirically demonstrate the effectiveness of these two resources. In addition, we investigate how similarity measures can be used to nominate in-domain pretraining data. We publicly release our pretrained models at https://bit.ly/35RpTf0.

Related papers

• Data Shapley in One Training Run [88.59484417202454]
  Data Shapley provides a principled framework for attributing data's contribution within machine learning contexts. Existing approaches require re-training models on different data subsets, which is computationally intensive. This paper introduces In-Run Data Shapley, which addresses these limitations by offering scalable data attribution for a target model of interest.
  arXiv  Detail & Related papers  (2024-06-16T17:09:24Z)
• On the Connection between Pre-training Data Diversity and Fine-tuning Robustness [66.30369048726145]
  We find that the primary factor influencing downstream effective robustness is data quantity. We demonstrate our findings on pre-training distributions drawn from various natural and synthetic data sources.
  arXiv  Detail & Related papers  (2023-07-24T05:36:19Z)
• Masked Autoencoders As The Unified Learners For Pre-Trained Sentence Representation [77.47617360812023]
  We extend the recently proposed MAE style pre-training strategy, RetroMAE, to support a wide variety of sentence representation tasks. The first stage performs RetroMAE over generic corpora, like Wikipedia, BookCorpus, etc., from which the base model is learned. The second stage takes place on domain-specific data, e.g., MS MARCO and NLI, where the base model is continuingly trained based on RetroMAE and contrastive learning.
  arXiv  Detail & Related papers  (2022-07-30T14:34:55Z)
• bert2BERT: Towards Reusable Pretrained Language Models [51.078081486422896]
  We propose bert2BERT, which can effectively transfer the knowledge of an existing smaller pre-trained model to a large model. bert2BERT saves about 45% and 47% computational cost of pre-training BERT_BASE and GPT_BASE by reusing the models of almost their half sizes.
  arXiv  Detail & Related papers  (2021-10-14T04:05:25Z)
• Adapt-and-Distill: Developing Small, Fast and Effective Pretrained Language Models for Domains [45.07506437436464]
  We present a general approach to developing small, fast and effective pre-trained models for specific domains. This is achieved by adapting the off-the-shelf general pre-trained models and performing task-agnostic knowledge distillation in target domains.
  arXiv  Detail & Related papers  (2021-06-25T07:37:05Z)
• Few-Shot Named Entity Recognition: A Comprehensive Study [92.40991050806544]
  We investigate three schemes to improve the model generalization ability for few-shot settings. We perform empirical comparisons on 10 public NER datasets with various proportions of labeled data. We create new state-of-the-art results on both few-shot and training-free settings.
  arXiv  Detail & Related papers  (2020-12-29T23:43:16Z)
• Don't Stop Pretraining: Adapt Language Models to Domains and Tasks [81.99843216550306]
  We present a study across four domains (biomedical and computer science publications, news, and reviews) and eight classification tasks. A second phase of pretraining in-domain (domain-adaptive pretraining) leads to performance gains. Adapting to the task's unlabeled data (task-adaptive pretraining) improves performance even after domain-adaptive pretraining.
  arXiv  Detail & Related papers  (2020-04-23T04:21:19Z)
• Data Augmentation using Pre-trained Transformer Models [2.105564340986074]
  We study different types of transformer based pre-trained models such as auto-regressive models (GPT-2), auto-encoder models (BERT), and seq2seq models (BART) for conditional data augmentation. We show that prepending the class labels to text sequences provides a simple yet effective way to condition the pre-trained models for data augmentation.
  arXiv  Detail & Related papers  (2020-03-04T18:35:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.