TextPruner: A Model Pruning Toolkit for Pre-Trained Language Models

• URL: http://arxiv.org/abs/2203.15996v1
• Date: Wed, 30 Mar 2022 02:10:33 GMT
• Title: TextPruner: A Model Pruning Toolkit for Pre-Trained Language Models
• Authors: Ziqing Yang, Yiming Cui, Zhigang Chen
• Abstract summary: We introduce TextPruner, an open-source model pruning toolkit for pre-trained language models. TextPruner offers structured post-training pruning methods, including vocabulary pruning and transformer pruning. Our experiments with several NLP tasks demonstrate the ability of TextPruner to reduce the model size without re-training the model.
• Score: 18.49325959450621
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pre-trained language models have been prevailed in natural language processing and become the backbones of many NLP tasks, but the demands for computational resources have limited their applications. In this paper, we introduce TextPruner, an open-source model pruning toolkit designed for pre-trained language models, targeting fast and easy model compression. TextPruner offers structured post-training pruning methods, including vocabulary pruning and transformer pruning, and can be applied to various models and tasks. We also propose a self-supervised pruning method that can be applied without the labeled data. Our experiments with several NLP tasks demonstrate the ability of TextPruner to reduce the model size without re-training the model.

Related papers

• Pre-Training to Learn in Context [138.0745138788142]
  The ability of in-context learning is not fully exploited because language models are not explicitly trained to learn in context. We propose PICL (Pre-training for In-Context Learning), a framework to enhance the language models' in-context learning ability. Our experiments show that PICL is more effective and task-generalizable than a range of baselines, outperforming larger language models with nearly 4x parameters.
  arXiv  Detail & Related papers  (2023-05-16T03:38:06Z)
• Language Model Pre-Training with Sparse Latent Typing [66.75786739499604]
  We propose a new pre-training objective, Sparse Latent Typing, which enables the model to sparsely extract sentence-level keywords with diverse latent types. Experimental results show that our model is able to learn interpretable latent type categories in a self-supervised manner without using any external knowledge.
  arXiv  Detail & Related papers  (2022-10-23T00:37:08Z)
• Few-shot Prompting Towards Controllable Response Generation [49.479958672988566]
  We first explored the combination of prompting and reinforcement learning (RL) to steer models' generation without accessing any of the models' parameters. We apply multi-task learning to make the model learn to generalize to new tasks better. Experiment results show that our proposed method can successfully control several state-of-the-art (SOTA) dialogue models without accessing their parameters.
  arXiv  Detail & Related papers  (2022-06-08T14:48:06Z)
• Sparse*BERT: Sparse Models Generalize To New tasks and Domains [79.42527716035879]
  This paper studies how models pruned using Gradual Unstructured Magnitude Pruning can transfer between domains and tasks. We demonstrate that our general sparse model Sparse*BERT can become SparseBioBERT simply by pretraining the compressed architecture on unstructured biomedical text.
  arXiv  Detail & Related papers  (2022-05-25T02:51:12Z)
• Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing [78.8500633981247]
  This paper surveys and organizes research works in a new paradigm in natural language processing, which we dub "prompt-based learning" Unlike traditional supervised learning, which trains a model to take in an input x and predict an output y as P(y|x), prompt-based learning is based on language models that model the probability of text directly.
  arXiv  Detail & Related papers  (2021-07-28T18:09:46Z)
• Few-Shot Text Generation with Pattern-Exploiting Training [12.919486518128734]
  In this paper, we show that the underlying idea can also be applied to text generation tasks. We adapt Pattern-Exploiting Training (PET), a recently proposed few-shot approach, for finetuning generative language models on text generation tasks.
  arXiv  Detail & Related papers  (2020-12-22T10:53:07Z)
• Introducing various Semantic Models for Amharic: Experimentation and Evaluation with multiple Tasks and Datasets [19.855120632909124]
  We introduce different semantic models for Amharic. Models are build using word2Vec embeddings, distributional thesaurus (DT), contextual embeddings, and DT embeddings. We find that newly trained models perform better than pre-trained multilingual models.
  arXiv  Detail & Related papers  (2020-11-02T17:48:25Z)
• Unsupervised Paraphrasing with Pretrained Language Models [85.03373221588707]
  We propose a training pipeline that enables pre-trained language models to generate high-quality paraphrases in an unsupervised setting. Our recipe consists of task-adaptation, self-supervision, and a novel decoding algorithm named Dynamic Blocking. We show with automatic and human evaluations that our approach achieves state-of-the-art performance on both the Quora Question Pair and the ParaNMT datasets.
  arXiv  Detail & Related papers  (2020-10-24T11:55:28Z)

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.