人工智能大作业-基于ALBERT+机器学习算法实现文本分类python源码+项目说明+文本数据集.zip

# albert_zh An Implementation of <a href="https://arxiv.org/pdf/1909.11942.pdf">A Lite Bert For Self-Supervised Learning Language Representations</a> with TensorFlow ALBert is based on Bert, but with some improvements. It achieves state of the art performance on main benchmarks with 30% parameters less. For albert_base_zh it only has ten percentage parameters compare of original bert model, and main accuracy is retained. Different version of ALBERT pre-trained model for Chinese, including TensorFlow, PyTorch and Keras, is available now. 海量中文语料上预训练ALBERT模型：参数更少，效果更好。预训练小模型也能拿下13项NLP任务，ALBERT三大改造登顶GLUE基准 更多数据集、基线模型、不同任务上模型效果的详细对比，见<a href="https://github.com/chineseGLUE/chineseGLUE">中文任务基准测评chineseGLUE</a> <img src="https://github.com/brightmart/albert_zh/blob/master/resources/albert_tiny_compare_s.jpg" width="90%" height="70%" /> 模型下载 Download Pre-trained Models of Chinese ----------------------------------------------- 1、<a href="https://storage.googleapis.com/albert_zh/albert_tiny.zip">albert_tiny_zh</a>, 文件大小16M、参数为1.8M 训练和推理预测速度提升约10倍，精度基本保留，模型大小为bert的1/25；语义相似度数据集LCQMC测试集上达到85.4%，相比bert_base仅下降1.5个点。 lcqmc训练使用如下参数： --max_seq_length=128 --train_batch_size=64 --learning_rate=1e-4 --num_train_epochs=5 albert_tiny使用同样的大规模中文语料数据，层数仅为4层、hidden size等向量维度大幅减少。 【使用场景】任务相对比较简单一些或实时性要求高的任务，如语义相似度等句子对任务、分类任务；比较难的任务如阅读理解等，可以使用其他大模型。 2、<a href="https://storage.googleapis.com/albert_zh/albert_large_zh.zip">albert_large_zh</a>,参数量，层数24，文件大小为64M 参数量和模型大小为bert_base的六分之一；在口语化描述相似性数据集LCQMC的测试集上相比bert_base上升0.2个点 3、<a href="https://storage.googleapis.com/albert_zh/albert_base_zh_additional_36k_steps.zip">albert_base_zh(额外训练了1.5亿个实例即 36k steps * batch_size 4096)</a>; <a href="https://storage.googleapis.com/albert_zh/albert_base_zh.zip"> albert_base_zh(小模型体验版)</a>, 参数量12M, 层数12，大小为40M 参数量为bert_base的十分之一，模型大小也十分之一；在口语化描述相似性数据集LCQMC的测试集上相比bert_base下降约0.6~1个点； 相比未预训练，albert_base提升14个点 4、<a href="https://storage.googleapis.com/albert_zh/albert_xlarge_zh_177k.zip">albert_xlarge_zh_177k </a>; <a href="https://storage.googleapis.com/albert_zh/albert_xlarge_zh_183k.zip">albert_xlarge_zh_183k(优先尝试)</a>参数量，层数24，文件大小为230M 参数量和模型大小为bert_base的二分之一；需要一张大的显卡；完整测试对比将后续添加；batch_size不能太小，否则可能影响精度 Updates ----------------------------------------------- **\*\*\*\*\* 2019-10-15: albert_tiny_zh, 10 times fast than bert base for training and inference, accuracy remains \*\*\*\*\*** **\*\*\*\*\* 2019-10-07: more models of albert \*\*\*\*\*** add albert_xlarge_zh; albert_base_zh_additional_steps, training with more instances **\*\*\*\*\* 2019-10-04: PyTorch and Keras versions of albert were supported \*\*\*\*\*** a.Convert to PyTorch version and do your tasks through <a href="https://github.com/lonePatient/albert_pytorch">albert_pytorch</a> b.Load pre-trained model with keras using one line of codes through <a href="https://github.com/bojone/bert4keras">bert4keras</a> c.Use albert with TensorFlow 2.0: Use or load pre-trained model with tf2.0 through <a href="https://github.com/kpe/bert-for-tf2">bert-for-tf2</a> Releasing albert_xlarge on 6th Oct **\*\*\*\*\* 2019-10-02: albert_large_zh,albert_base_zh \*\*\*\*\*** Relesed albert_base_zh with only 10% parameters of bert_base, a small model(40M) & training can be very fast. Relased albert_large_zh with only 16% parameters of bert_base(64M) **\*\*\*\*\* 2019-09-28: codes and test functions \*\*\*\*\*** Add codes and test functions for three main changes of albert from bert ALBERT模型介绍 Introduction of ALBERT ----------------------------------------------- ALBERT模型是BERT的改进版，与最近其他State of the art的模型不同的是，这次是预训练小模型，效果更好、参数更少。 它对BERT进行了三个改造 Three main changes of ALBert from Bert： 1）词嵌入向量参数的因式分解 Factorized embedding parameterization O(V * H) to O(V * E + E * H) 如以ALBert_xxlarge为例，V=30000, H=4096, E=128 那么原先参数为V * H= 30000 * 4096 = 1.23亿个参数，现在则为V * E + E * H = 30000*128+128*4096 = 384万 + 52万 = 436万， 词嵌入相关的参数变化前是变换后的28倍。 2）跨层参数共享 Cross-Layer Parameter Sharing 参数共享能显著减少参数。共享可以分为全连接层、注意力层的参数共享；注意力层的参数对效果的减弱影响小一点。 3）段落连续性任务 Inter-sentence coherence loss. 使用段落连续性任务。正例，使用从一个文档中连续的两个文本段落；负例，使用从一个文档中连续的两个文本段落，但位置调换了。 避免使用原有的NSP任务，原有的任务包含隐含了预测主题这类过于简单的任务。 We maintain that inter-sentence modeling is an important aspect of language understanding, but we propose a loss based primarily on coherence. That is, for ALBERT, we use a sentence-order prediction (SOP) loss, which avoids topic prediction and instead focuses on modeling inter-sentence coherence. The SOP loss uses as positive examples the same technique as BERT (two consecutive segments from the same document), and as negative examples the same two consecutive segments but with their order swapped. This forces the model to learn finer-grained distinctions about discourse-level coherence properties. 其他变化，还有 Other changes： 1）去掉了dropout Remove dropout to enlarge capacity of model. 最大的模型，训练了1百万步后，还是没有过拟合训练数据。说明模型的容量还可以更大，就移除了dropout （dropout可以认为是随机的去掉网络中的一部分，同时使网络变小一些） We also note that, even after training for 1M steps, our largest models still do not overfit to their training data. As a result, we decide to remove dropout to further increase our model capacity. 其他型号的模型，在我们的实现中我们还是会保留原始的dropout的比例，防止模型对训练数据的过拟合。 2）为加快训练速度，使用LAMB做为优化器 Use LAMB as optimizer, to train with big batch size 使用了大的batch_size来训练(4096)。 LAMB优化器使得我们可以训练，特别大的批次batch_size，如高达6万。 3）使用n-gram(uni-gram,bi-gram, tri-gram）来做遮蔽语言模型 Use n-gram as make language model 即以不同的概率使用n-gram,uni-gram的概率最大，bi-gram其次，tri-gram概率最小。 本项目中目前使用的是在中文上做whole word mask，稍后会更新一下与n-gram mask的效果对比。n-gram从spanBERT中来。 训练语料/训练配置 Training Data & Configuration ----------------------------------------------- 30g中文语料，超过100亿汉字，包括多个百科、新闻、互动社区。 预训练序列长度sequence_length设置为512，批次batch_size为4096，训