Elasticbert Base

🚀 ElasticBERT-BASE

ElasticBERT-BASE是ElasticBERT模型的基础版本实现，可用于自然语言处理任务，为高效NLP提供了标准评估和强大的基线。

🚀 快速开始

本部分将展示如何使用ElasticBERT-BASE进行序列分类任务。

基础用法

>>> from transformers import BertTokenizer as ElasticBertTokenizer
>>> from models.configuration_elasticbert import ElasticBertConfig
>>> from models.modeling_elasticbert import ElasticBertForSequenceClassification

>>> num_output_layers = 1
>>> config = ElasticBertConfig.from_pretrained('fnlp/elasticbert-base', num_output_layers=num_output_layers )
>>> tokenizer = ElasticBertTokenizer.from_pretrained('fnlp/elasticbert-base')
>>> model = ElasticBertForSequenceClassification.from_pretrained('fnlp/elasticbert-base', config=config)

>>> input_ids = tokenizer.encode('The actors are fantastic .', return_tensors='pt')
>>> outputs = model(input_ids)

📚 详细文档

模型描述

这是ElasticBERT的基础版本实现。相关论文为 Towards Efficient NLP: A Standard Evaluation and A Strong Baseline，作者包括Xiangyang Liu、Tianxiang Sun、Junliang He、Lingling Wu、Xinyu Zhang、Hao Jiang、Zhao Cao、Xuanjing Huang和Xipeng Qiu。

代码链接

可在 fastnlp/elasticbert 查看相关代码。

数据集

该模型使用了以下数据集进行训练：

引用信息

如果您使用了该模型，请按照以下格式引用：

@article{liu2021elasticbert,
  author    = {Xiangyang Liu and
               Tianxiang Sun and
               Junliang He and
               Lingling Wu and
               Xinyu Zhang and
               Hao Jiang and
               Zhao Cao and
               Xuanjing Huang and
               Xipeng Qiu},
  title     = {Towards Efficient {NLP:} {A} Standard Evaluation and {A} Strong Baseline},
  journal   = {CoRR},
  volume    = {abs/2110.07038},
  year      = {2021},
  url       = {https://arxiv.org/abs/2110.07038},
  eprinttype = {arXiv},
  eprint    = {2110.07038},
  timestamp = {Fri, 22 Oct 2021 13:33:09 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2110-07038.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}