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songheqi ProHiryu Accenture Advanced Technology Centers, China Shanghai, China https://prohiryu.github.io/ The rest is silence

ProHiryu/bert-chinese-ner 681

使用预训练语言模型BERT做中文NER

ProHiryu/albert-chinese-ner 220

使用预训练语言模型ALBERT做中文NER

ProHiryu/FaceRecoginition 17

利用opencv+keras+python实现人脸识别系统

ProHiryu/api-wanplus-crawler 6

A small custom lol-esports-api of wanplus.com

ProHiryu/esports-api-lol 6

Use the api from http://esports-api.com/docs/leagueoflegends

ProHiryu/Deep-Learning 3

Deep Learning Specialization by Andrew Ng on Coursera

ProHiryu/Mac-Options 2

Record some Mac options

ProHiryu/Propheta 2

a little project

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issue closedProHiryu/albert-chinese-ner

如何训练

如果应用到专业领域,请问是否需要自己标注部分数据才能训练呢

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loveaguai

issue commentProHiryu/albert-chinese-ner

如何训练

专业领域必须要专业数据集,如果较少可以参考distant supervision相关内容

loveaguai

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issue closedProHiryu/albert-chinese-ner

为什么现在的这个albert-chinese-ner没有以前的bert-chinese-ner准确率高呢?你对比过吗?

你好,我用同样的参数跑了一下albert-chinese-ner和上一个bert-chinese-ner,但是跑出来的结果后者好于前者,论文上不是说albert比bert好一些吗?为什么不是这样呢?下面是我跑出来的结果:

albert-chinese-ner跑的命令和结果如下: 用的预训练向量地址:https://storage.googleapis.com/albert_zh/albert_base_zh.zip 命令: python3 albert_ner.py --task_name ner --do_train true --do_eval true --data_dir data --vocab_file albert_config/vocab.txt --bert_config_file ../../albert_base_zh/albert_config_base.json --max_seq_length 128 --train_batch_size 64 --learning_rate 2e-5 --num_train_epochs 3 --output_dir albert_base_ner_checkpoints 结果: INFO:tensorflow:***** Eval results albert_base_ner_checkpoints/model.ckpt-2374 ***** INFO:tensorflow: eval_f = 0.9456498 INFO:tensorflow: eval_precision = 0.95313764 INFO:tensorflow: eval_recall = 0.93832487 INFO:tensorflow: global_step = 2374 INFO:tensorflow: loss = 11.592924

bert-chinese-ner跑的命令和结果如下: 用的预训练向量地址:https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip 命令: python3 BERT_NER.py --task_name ner --do_train true --do_eval true --data_dir data --vocab_file vocab.txt --bert_config_file checkpoint/bert_config.json --init_checkpoint checkpoint/bert_model.ckpt --max_seq_length 128 --train_batch_size 64 --learning_rate 2e-5 --num_train_epochs 3 --output_dir output/result_dir/ 结果: INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2374: output/result_dir/model.ckpt-2374 INFO:tensorflow:evaluation_loop marked as finished INFO:tensorflow:***** Eval results ***** INFO:tensorflow: eval_f = 0.96657985 INFO:tensorflow: eval_precision = 0.9670561 INFO:tensorflow: eval_recall = 0.96620923 INFO:tensorflow: global_step = 2374 INFO:tensorflow: loss = 8.526184

训练时间bert也比albert快,训练时每一个examples/sec耗时如下 albert耗时平均: INFO:tensorflow:examples/sec: 66.2794 bert耗时平均: INFO:tensorflow:examples/sec: 58.3886

为什么会出现这样的结果呢?

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issue commentProHiryu/albert-chinese-ner

为什么现在的这个albert-chinese-ner没有以前的bert-chinese-ner准确率高呢?你对比过吗?

不知道bert是否更新了模型,可能基于新的语料有新的预训练模型

sxk000

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issue commentProHiryu/albert-chinese-ner

为什么现在的这个albert-chinese-ner没有以前的bert-chinese-ner准确率高呢?你对比过吗?

这个可能跟不同的模型参数和训练参数有关,虽然我看你写出来的都是一样,但是不代表每个参数都一致,还有bert基础模型的更新时间可能也会影响,不过如果相差不大也没什么关系

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issue commentProHiryu/albert-chinese-ner

为什么现在的这个albert-chinese-ner没有以前的bert-chinese-ner准确率高呢?你对比过吗?

尝试了几次呢? 是否每次都出现这种结果?

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issue commentProHiryu/bert-chinese-ner

数据集

#41

mingxixixi

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issue closedProHiryu/bert-chinese-ner

数据集

你好,实验中用的是什么数据呢?

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mingxixixi

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