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Multi-label Classification with BERT; Fine Grained Sentiment Analysis from AI challenger
| Date | Stars |
|---|---|
| 2026-07-24 | 592 |
| 2026-07-25 | 592 |
| 2026-07-28 | 592 |
| 2026-07-30 | 592 |
| 2026-08-06 | 592 |
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## Introduction
With this repository, you will able to train Multi-label Classification with BERT,
Deploy BERT for online prediction.
You can also find the a short tutorial of how to use bert with chinese: <a href='https://github.com/brightmart/sentiment_analysis_fine_grain/blob/master/README_bert_chinese_tutorial.md'>BERT short chinese tutorial</a>
You can find Introduction to <a href='https://challenger.ai/competition/fsauor2018'>fine grain sentiment from AI Challenger</a>
## Basic Ideas
Add something here.
## Experiment on New Models
<img src="https://github.com/brightmart/sentiment_analysis_fine_grain/blob/master/data/img/fine_grain.jpg" width="67%" height="67%" />
for more, check model/bert_cnn_fine_grain_model.py
## Performance
Model | TextCNN(No-pretrain)| TextCNN(Pretrain-Finetuning)| Bert(base_model_zh) | Bert(base_model_zh,pre-train on corpus)
--- | --- | --- | ----------- | -----------
F1 Score | 0.678 | 0.685 | ADD A NUMBER HERE | ADD A NUMBER HERE
----------------------------------------------------------------------------------------------
Notice: F1 Score is reported on validation set
<img src="https://github.com/brightmart/sentiment_analysis_fine_grain/blob/master/data/img/bert_sa.jpg" width="65%" height="65%" />
## Usage
### Bert for Multi-label Classificaiton [<a href='https://pan.baidu.com/s/1ZS4dAdOIAe3DaHiwCDrLKw'>data for fine-tuning and pre-train</a>]
export BERT_BASE_DIR=BERT_BASE_DIR/chinese_L-12_H-768_A-12
export TEXT_DIR=TEXT_DIR
nohup python run_classifier_multi_labels_bert.py
--task_name=sentiment_analysis
--do_train=true
--do_eval=true
--data_dir=$TEXT_DIR
--vocab_file=$BERT_BASE_DIR/vocab.txt
--bert_config_file=$BERT_BASE_DIR/bert_config.json
--init_checkpoint=$BERT_BASE_DIR/bert_model.ckpt
--max_seq_length=512
--train_batch_size=4
--learning_rate=2e-5
--num_train_epochs=3
--output_dir=./checkpoint_bert &
1.firstly, you need to download pre-trained model from google, and put to a folder(e.g.BERT_BASE_DIR)
chinese_L-12_H-768_A-12 from <a href='https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip'>bert</a>
2.secondly, you need to have training data(e.g. train.tsv) and validation data(e.g. dev.tsv), and put it under a
folder(e.g.TEXT_DIR ). you can also download data from here <a href='https://pan.baidu.com/s/1ZS4dAdOIAe3DaHiwCDrLKw'>data to train bert for AI challenger-Sentiment Analysis</a>.
it contains processed data you can run for both fine-tuning on sentiment analysis and pre-train with Bert.
it is generated by following this notebook step by step:
preprocess_char.ipynb
you can generate data by yourself as long as data format is compatible with
processor SentimentAnalysisFineGrainProcessor(alias as sentiment_analysis);
data format: label1,label2,label3\t here is sentence or sentences\t
it only contains two columns, the first one is target(one or multi-labels), the second one is input strings.
no need to tokenized.
sample:"0_1,1_-2,2_-2,3_-2,4_1,5_-2,6_-2,7_-2,8_1,9_1,10_-2,11_-2,12_-2,13_-2,14_-2,15_1,16_-2,17_-2,18_0,19_-2 浦东五莲路站，老饭店福瑞轩属于上海的本帮菜，交通方便，最近又重新装修，来拨草了，饭店活动满188元送50元钱，环境干净，简单。朋友提前一天来预订包房也没有订到，只有大堂，五点半到店基本上每个台子都客满了，都是附近居民，每道冷菜量都比以前小，味道还可以，热菜烤茄子，炒河虾仁，脆皮鸭，照牌鸡，小牛排，手撕腊味花菜等每道菜都很入味好吃，会员价划算，服务员人手太少，服务态度好，要能团购更好。可以用支付宝方便"
check sample data in ./BERT_BASE_DIR folder
for more detail, check create_model and SentimentAnalysisFineGrainProcessor from run_classifier.py
### Pre-train Bert model based on open-souced model, then do classification task
1. generate raw data: [ADD SOMETHING Excerpt of 6,612 characters
Read on GitHub
brightmart · https://www.CLUEbenchmarks.com · China
95
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:8b0b8e4dbae58943, topic:text-classification, topic:sentiment-analysis, name:sentiment analysis