根据参考文章1中使用bert fine-tuning MRPC任务的代码,我整理了一个适合colab平台tensorflow2.5环境的版本(原文中应该是tensorflow1.x),当作是对文章1的一个补充说明,对于文章1的内容就不赘述了。笔者水平有限,疏漏难免,如果发现有错误或是不规范的地方,欢迎与我讨论。 详细说明 下载GLUE数据 这里使用download_glue_d...
stride=0, truncation_strategy='longest_first', return_tensors=None, **kwargs): """ Returns a dictionary containing the encoded sequence or sequence pair and additional informations: the mask for sequence classification and the overflowing elements if a ``max_length`` is specified. Args...
BERT set new state-of-the-art performance on various sentence classification and sentence-pair regression tasks. BERT uses a cross-encoder: Two sentences are passed to the transformer network and the target value is predicted. However, this setup is unsuitable for various pair regression tasks due...
Objective function: 下游任务:8种, semantic relatedness, paraphrase detection, image-sentence ranking, question-type classification and 4 benchmark sentiment and subjectivity datasets。 InferSent Supervised Learning of Universal Sentence Representations from Natural Language Inference Data Code:https://github.co...
实验方案：事先制作好三元pair对，每个给出词a，词b，词y，用模型从候选词中找到x，其中x：y的关系...
fastText:Bag of Tricks for Efficient Text Classification Abhishek Thakur - Is That a Duplicate Quora Question? (Youtube speech) ESIM:Enhanced LSTM for Natural Language Inference SSE:Shortcut-Stacked Sentence Encoders for Multi-Domain Inference ...
MIL-based relation classification:这里使用MIL。因为在一篇document中,每一个entity会有多个mention,我们希望能够去聚合target entity所有的mention,并通过bi-affine pairwise scoring来进行最终的关系分类。具体公式如下: xheadi=W(1)head(RELU(W(0)headxKi))xihead=Whead(1)(RELU(Whead(0)xiK)) xtaili=W(...
For example,BERT-pair-NLI_Mtask onSentiHooddataset: CUDA_VISIBLE_DEVICES=0,1,2,3 python run_classifier_TABSA.py \ --task_name sentihood_NLI_M \ --data_dir data/sentihood/bert-pair/ \ --vocab_file uncased_L-12_H-768_A-12/vocab.txt \ --bert_config_file uncased_L-12_H-768_A-...
Subject-predicatepredicatesentenceSubject-predicatepredicatesentenceAsentencethatisapredicateisapredicate.Thisisakindofsentencethatisverycharacter..
对比学习的思想很简单,难点就是如何找到合适的三元组(sa,sp,sn)来训练模型,其中sp和sa属于同一个"类别",被称为正样本,sn和sa不是同一个"类别",被称为负样本,这里的类别是广义上的类别,可以是文本分类的类别,甚至也可以是每个句子单独对应一个类别(instance classification),典型的工作是恺明大神的MoCo [6]系...