├── .idea ├── .gitignore ├── inspectionProfiles │ └── profiles_settings.xml ├── modules.xml ├── misc.xml └── Douban_Bert.iml ├── static ├── images │ ├── 1.png │ ├── 231156ce864cdf68ce8ec4da608f6c5.jpg │ ├── 57cd2966e2199a959f6b956d2741ce4.jpg │ ├── 595d7f60e388652232aeda9a3f4f096.jpg │ ├── 7c62f9fc1b977eee788f421e5145fa7.jpg │ ├── c2054c88b61683933031860d6838197.jpg │ ├── cebeb11125c6c963b6a04fc48f61182.jpg │ ├── e8be7a6fb5c361ddba9dfcf8bbb8116.jpg │ └── cebeb11125c6c963b6a04fc48f61182-4.jpg ├── Home.css ├── Home.html ├── index.html └── jquery.js ├── __pycache__ ├── app.cpython-39.pyc ├── predict.cpython-39.pyc └── data_process.cpython-39.pyc ├── requirements.txt ├── templates ├── output.html ├── input.html ├── index.html └── Home.html ├── README.md ├── app.py ├── predict.py ├── data_process.py ├── cache └── logs │ ├── 2022-04-09-20-42.log │ └── 2022-04-09-23-54.log └── model.py /.idea/.gitignore: -------------------------------------------------------------------------------- 1 | # 默认忽略的文件 2 | /shelf/ 3 | /workspace.xml 4 | -------------------------------------------------------------------------------- /static/images/1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ezreal-Jing/Douban_BERT/HEAD/static/images/1.png -------------------------------------------------------------------------------- /__pycache__/app.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ezreal-Jing/Douban_BERT/HEAD/__pycache__/app.cpython-39.pyc -------------------------------------------------------------------------------- /__pycache__/predict.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ezreal-Jing/Douban_BERT/HEAD/__pycache__/predict.cpython-39.pyc -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | Flask==1.1.2 2 | matplotlib==3.4.3 3 | numpy==1.20.3 4 | pandas==1.3.4 5 | torch==1.10.1 6 | transformers==4.7.0 7 | -------------------------------------------------------------------------------- /__pycache__/data_process.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ezreal-Jing/Douban_BERT/HEAD/__pycache__/data_process.cpython-39.pyc -------------------------------------------------------------------------------- /static/images/231156ce864cdf68ce8ec4da608f6c5.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ezreal-Jing/Douban_BERT/HEAD/static/images/231156ce864cdf68ce8ec4da608f6c5.jpg -------------------------------------------------------------------------------- /static/images/57cd2966e2199a959f6b956d2741ce4.jpg: 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-------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ezreal-Jing/Douban_BERT/HEAD/static/images/c2054c88b61683933031860d6838197.jpg -------------------------------------------------------------------------------- /static/images/cebeb11125c6c963b6a04fc48f61182.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ezreal-Jing/Douban_BERT/HEAD/static/images/cebeb11125c6c963b6a04fc48f61182.jpg -------------------------------------------------------------------------------- /static/images/e8be7a6fb5c361ddba9dfcf8bbb8116.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ezreal-Jing/Douban_BERT/HEAD/static/images/e8be7a6fb5c361ddba9dfcf8bbb8116.jpg -------------------------------------------------------------------------------- /static/images/cebeb11125c6c963b6a04fc48f61182-4.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ezreal-Jing/Douban_BERT/HEAD/static/images/cebeb11125c6c963b6a04fc48f61182-4.jpg -------------------------------------------------------------------------------- /.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 6 | -------------------------------------------------------------------------------- /.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 7 | -------------------------------------------------------------------------------- /.idea/Douban_Bert.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /templates/output.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 豆瓣影评情感分析平台 6 | 7 | 8 |

豆瓣影评情感分析平台

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阁下输入的影评是：{{userinput}}

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我们使用BERT模型预测，认为它的情感是 {{classresult}}

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14 | 15 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 基于BERT的豆瓣影评情感分析 2 | ## 简介 3 | 4 | 本项目基于Huggingface开源的transformers库，实现对豆瓣电影短评的情感分类。 5 | 6 | ## 使用说明 7 | 8 | data_process.py——数据预处理 9 | 10 | model.py——模型训练、评估，其中使用了bert-base-chinese等预训练模型，可以从huggingface下载，也可以直接运行代码自动下载。ubuntu系统下模型自动下载路径：用户文件夹/.cache/huggingface/transformers 11 | 12 | predict.py——对模型进行封装，实现输入一个句子，输出一个标签 13 | 14 | app.py——使用flask框架将模型部署到网页 15 | 16 | ## 具体细节 17 | 18 | 待更新 19 | -------------------------------------------------------------------------------- /templates/input.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 豆瓣影评情感分析平台 6 | 7 | 8 |

豆瓣影评情感分析平台

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请输入影评：

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14 | 15 | -------------------------------------------------------------------------------- /app.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @Author : Ezreal 3 | # @File : app.py 4 | # @Project: Douban_Bert 5 | # @CreateTime : 2022/3/13 下午6:51:12 6 | # @Version：V 0.1 7 | from flask import Flask, render_template, request 8 | import time 9 | from datetime import timedelta 10 | import predict 11 | ''' 12 | 使用flask框架实现网页可视化 13 | ''' 14 | 15 | app = Flask(__name__) 16 | # 设置静态文件缓存过期时间 17 | app.send_file_max_age_default = timedelta(seconds=1) 18 | 19 | # @app.route('/resnet', methods=['POST', 'GET']) 20 | @app.route('/', methods=['POST', 'GET']) # 添加路由 21 | def upload(): 22 | if request.method == 'POST': 23 | 24 | user_input = request.form.get("name")#用户输入 25 | result = predict.predict(user_input)#调用predict进行预测 26 | 27 | return render_template( 28 | 'output.html', 29 | userinput=user_input,#用户输入 30 | classresult=result, 31 | val1=time.time() 32 | ) 33 | return render_template('index.html') 34 | 35 | if __name__ == '__main__': 36 | 37 | app.run(host='127.0.0.1', port=5000, debug=True) -------------------------------------------------------------------------------- /predict.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @Author : Ezreal 3 | # @File : predict.py 4 | # @Project: Douban_Bert 5 | # @CreateTime : 2022/3/13 上午12:08:22 6 | # @Version：V 0.1 7 | 8 | import torch 9 | from transformers import BertConfig, BertForSequenceClassification, BertTokenizer 10 | import torch.nn.functional as F 11 | ''' 12 | 封装模型 13 | ''' 14 | def to_input_id(sentence_input): 15 | tokenizer = BertTokenizer(vocab_file='bert-base-chinese/vocab.txt') 16 | return tokenizer.build_inputs_with_special_tokens(tokenizer.convert_tokens_to_ids(tokenizer.tokenize(sentence_input))) 17 | emotion_dict = {"很差":0, "较差":1, "一般":2, "还行":3, "力荐":4} 18 | 19 | def getDictKey(myDict, value): 20 | return [k for k, v in myDict.items() if v == value] 21 | 22 | def predict(text): 23 | # tokenizer = BertTokenizer(vocab_file='bert-base-chinese/vocab.txt') 24 | # config = BertConfig.from_pretrained('cache/config.json') 25 | model = BertForSequenceClassification.from_pretrained('cache') 26 | model.eval() 27 | 28 | 29 | sentence = text 30 | 31 | input_id = to_input_id(sentence) 32 | assert len(input_id) <= 512 33 | input_ids = torch.LongTensor(input_id).unsqueeze(0) 34 | 35 | # predict时，沒有label所以沒有loss 36 | outputs = model(input_ids) 37 | 38 | prediction = torch.max(F.softmax(outputs[0], dim=-1), dim=1)[1] # 返回索引值 39 | predict_label = prediction.data.cpu().numpy().squeeze() # 降维 40 | 41 | result = getDictKey(emotion_dict, predict_label) 42 | 43 | return result 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | if __name__ == "__main__": 52 | 53 | result = predict("燃爆") 54 | print(result) -------------------------------------------------------------------------------- /data_process.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @Author : Ezreal 3 | # @File : predict.py 4 | # @Project: Douban_Bert 5 | # @CreateTime : 2022/3/13 上午12:08:22 6 | # @Version：V 0.1 7 | ''' 8 | 数据预处理 9 | ''' 10 | import pandas as pd 11 | import torch 12 | from transformers import Trainer,TrainingArguments, BertTokenizer, BertModel, BertPreTrainedModel,BertConfig 13 | from torch.utils.data import Dataset, DataLoader 14 | import warnings 15 | warnings.filterwarnings('ignore') 16 | import sys 17 | sys.setrecursionlimit(3000) 18 | import re 19 | 20 | def tokenize(content): 21 | filters = ['\t','\n','\x97','\x96','#','$','%','&',':','，','。','\.','“','”','"','《','》'," ","@","、","-","（","）","0","1","2","3","4","5","6","7","8","9"] 22 | content = re.sub("|".join(filters),"",content) 23 | return content 24 | 25 | 26 | def read_data(data_dir): 27 | data = pd.read_csv(data_dir) 28 | data['comments'] = data['comments'].fillna('') 29 | return data 30 | 31 | def fill_paddings(data, maxlen): 32 | '''补全句长''' 33 | if len(data) < maxlen: 34 | pad_len = maxlen-len(data) 35 | paddings = [0 for _ in range(pad_len)] 36 | data = torch.tensor(data + paddings) 37 | else: 38 | data = torch.tensor(data[:maxlen]) 39 | return data 40 | 41 | class InputDataSet(): 42 | 43 | def __init__(self,data,tokenizer,max_len): 44 | self.data = data 45 | self.tokenizer = tokenizer 46 | self.max_len = max_len#最大句长 47 | 48 | def __len__(self,): 49 | return len(self.data) 50 | 51 | def __getitem__(self, item): 52 | text = str(self.data['comments'][item]) 53 | labels = self.data['rating'][item] 54 | labels = torch.tensor(labels, dtype=torch.long) 55 | 56 | ## 手动构建 57 | tokens = self.tokenizer.tokenize(text) 58 | tokens_ids = self.tokenizer.convert_tokens_to_ids(tokens) 59 | tokens_ids = [101] + tokens_ids + [102] 60 | input_ids = fill_paddings(tokens_ids,self.max_len) 61 | 62 | attention_mask = [1 for _ in range(len(tokens_ids))]#这里注意传入的是tokens_ids 63 | attention_mask = fill_paddings(attention_mask,self.max_len) 64 | 65 | token_type_ids = [0 for _ in range(len(tokens_ids))] 66 | token_type_ids = fill_paddings(token_type_ids,self.max_len) 67 | 68 | return { 69 | 'text':text, 70 | 'input_ids':input_ids, 71 | 'attention_mask':attention_mask, 72 | 'token_type_ids':token_type_ids, 73 | 'labels':labels-1 74 | 75 | } 76 | 77 | 78 | if __name__ == '__main__': 79 | train_dir = 'data/train.csv' 80 | dev_dir = 'data/test.csv' 81 | model_dir = 'bert-base-chinese' 82 | train = read_data(train_dir) 83 | test = read_data(dev_dir) 84 | tokenizer = BertTokenizer.from_pretrained(model_dir) 85 | train_dataset = InputDataSet(train,tokenizer=tokenizer, max_len=128) 86 | train_dataloader = DataLoader(train_dataset,batch_size=4) 87 | batch = next(iter(train_dataloader)) 88 | 89 | print(batch) 90 | print(batch['input_ids'].shape) 91 | print(batch['attention_mask'].shape) 92 | print(batch['token_type_ids'].shape) 93 | print(batch['labels'].shape) 94 | 95 | 96 | 97 | 98 | 99 | 100 | -------------------------------------------------------------------------------- /cache/logs/2022-04-09-20-42.log: -------------------------------------------------------------------------------- 1 | [2022-04-09 20:42:17,019][line: 219] ==> creating ./cache/logs/2022-04-09-20-42.log 2 | [2022-04-09 20:42:17,019][line: 52] ==> Train batch size = 64 3 | [2022-04-09 20:42:17,019][line: 53] ==> Total steps = 7500 4 | [2022-04-09 20:42:17,019][line: 54] ==> Training Start! 5 | [2022-04-09 20:44:43,498][line: 86] ==> ====Epoch:[1/10] avg_train_loss=0.89097==== 6 | [2022-04-09 20:44:43,498][line: 87] ==> ====Training epoch took: 02:26==== 7 | [2022-04-09 20:44:43,498][line: 88] ==> Running Validation... 8 | [2022-04-09 20:44:58,290][line: 93] ==> ====Epoch:[1/10] avg_val_loss=0.93396 avg_val_acc=0.71551==== 9 | [2022-04-09 20:44:58,291][line: 94] ==> ====Validation epoch took: 02:41==== 10 | [2022-04-09 20:44:58,291][line: 95] ==> 11 | [2022-04-09 20:47:23,092][line: 86] ==> ====Epoch:[2/10] avg_train_loss=0.43712==== 12 | [2022-04-09 20:47:23,092][line: 87] ==> ====Training epoch took: 02:24==== 13 | [2022-04-09 20:47:23,092][line: 88] ==> Running Validation... 14 | [2022-04-09 20:47:37,913][line: 93] ==> ====Epoch:[2/10] avg_val_loss=0.55599 avg_val_acc=0.86835==== 15 | [2022-04-09 20:47:37,913][line: 94] ==> ====Validation epoch took: 02:39==== 16 | [2022-04-09 20:47:37,913][line: 95] ==> 17 | [2022-04-09 20:50:04,162][line: 86] ==> ====Epoch:[3/10] avg_train_loss=0.27576==== 18 | [2022-04-09 20:50:04,162][line: 87] ==> ====Training epoch took: 02:26==== 19 | [2022-04-09 20:50:04,162][line: 88] ==> Running Validation... 20 | [2022-04-09 20:50:18,979][line: 93] ==> ====Epoch:[3/10] avg_val_loss=0.50120 avg_val_acc=0.89412==== 21 | [2022-04-09 20:50:18,979][line: 94] ==> ====Validation epoch took: 02:41==== 22 | [2022-04-09 20:50:18,979][line: 95] ==> 23 | [2022-04-09 20:52:43,696][line: 86] ==> ====Epoch:[4/10] avg_train_loss=0.22478==== 24 | [2022-04-09 20:52:43,697][line: 87] ==> ====Training epoch took: 02:24==== 25 | [2022-04-09 20:52:43,697][line: 88] ==> Running Validation... 26 | [2022-04-09 20:52:58,597][line: 93] ==> ====Epoch:[4/10] avg_val_loss=0.45129 avg_val_acc=0.91597==== 27 | [2022-04-09 20:52:58,597][line: 94] ==> ====Validation epoch took: 02:39==== 28 | [2022-04-09 20:52:58,597][line: 95] ==> 29 | [2022-04-09 20:55:23,741][line: 86] ==> ====Epoch:[5/10] avg_train_loss=0.18825==== 30 | [2022-04-09 20:55:23,741][line: 87] ==> ====Training epoch took: 02:25==== 31 | [2022-04-09 20:55:23,755][line: 88] ==> Running Validation... 32 | [2022-04-09 20:55:38,623][line: 93] ==> ====Epoch:[5/10] avg_val_loss=0.42481 avg_val_acc=0.92470==== 33 | [2022-04-09 20:55:38,623][line: 94] ==> ====Validation epoch took: 02:40==== 34 | [2022-04-09 20:55:38,623][line: 95] ==> 35 | [2022-04-09 20:58:03,976][line: 86] ==> ====Epoch:[6/10] avg_train_loss=0.16516==== 36 | [2022-04-09 20:58:03,977][line: 87] ==> ====Training epoch took: 02:25==== 37 | [2022-04-09 20:58:03,977][line: 88] ==> Running Validation... 38 | [2022-04-09 20:58:19,368][line: 93] ==> ====Epoch:[6/10] avg_val_loss=0.41008 avg_val_acc=0.92711==== 39 | [2022-04-09 20:58:19,369][line: 94] ==> ====Validation epoch took: 02:40==== 40 | [2022-04-09 20:58:19,369][line: 95] ==> 41 | [2022-04-09 21:00:43,387][line: 86] ==> ====Epoch:[7/10] avg_train_loss=0.14631==== 42 | [2022-04-09 21:00:43,387][line: 87] ==> ====Training epoch took: 02:24==== 43 | [2022-04-09 21:00:43,387][line: 88] ==> Running Validation... 44 | [2022-04-09 21:00:57,862][line: 93] ==> ====Epoch:[7/10] avg_val_loss=0.37470 avg_val_acc=0.93609==== 45 | [2022-04-09 21:00:57,862][line: 94] ==> ====Validation epoch took: 02:38==== 46 | [2022-04-09 21:00:57,862][line: 95] ==> 47 | [2022-04-09 21:03:20,793][line: 86] ==> ====Epoch:[8/10] avg_train_loss=0.12686==== 48 | [2022-04-09 21:03:20,793][line: 87] ==> ====Training epoch took: 02:22==== 49 | [2022-04-09 21:03:20,793][line: 88] ==> Running Validation... 50 | [2022-04-09 21:03:35,310][line: 93] ==> ====Epoch:[8/10] avg_val_loss=0.37030 avg_val_acc=0.93551==== 51 | [2022-04-09 21:03:35,310][line: 94] ==> ====Validation epoch took: 02:37==== 52 | [2022-04-09 21:03:35,310][line: 95] ==> 53 | [2022-04-09 21:06:03,032][line: 86] ==> ====Epoch:[9/10] avg_train_loss=0.12283==== 54 | [2022-04-09 21:06:03,032][line: 87] ==> ====Training epoch took: 02:27==== 55 | [2022-04-09 21:06:03,032][line: 88] ==> Running Validation... 56 | [2022-04-09 21:06:17,720][line: 93] ==> ====Epoch:[9/10] avg_val_loss=0.35997 avg_val_acc=0.93725==== 57 | [2022-04-09 21:06:17,720][line: 94] ==> ====Validation epoch took: 02:42==== 58 | [2022-04-09 21:06:17,720][line: 95] ==> 59 | [2022-04-09 21:08:43,587][line: 86] ==> ====Epoch:[10/10] avg_train_loss=0.12423==== 60 | [2022-04-09 21:08:43,588][line: 87] ==> ====Training epoch took: 02:25==== 61 | [2022-04-09 21:08:43,588][line: 88] ==> Running Validation... 62 | [2022-04-09 21:08:58,257][line: 93] ==> ====Epoch:[10/10] avg_val_loss=0.36056 avg_val_acc=0.94033==== 63 | [2022-04-09 21:08:58,257][line: 94] ==> ====Validation epoch took: 02:40==== 64 | [2022-04-09 21:08:58,257][line: 95] ==> 65 | [2022-04-09 21:08:58,634][line: 127] ==> 66 | [2022-04-09 21:08:58,634][line: 128] ==> Training Completed! 67 | -------------------------------------------------------------------------------- /cache/logs/2022-04-09-23-54.log: -------------------------------------------------------------------------------- 1 | [2022-04-09 23:54:22,802][line: 225] ==> creating ./cache/logs/2022-04-09-23-54.log 2 | [2022-04-09 23:54:22,802][line: 54] ==> Train batch size = 64 3 | [2022-04-09 23:54:22,802][line: 55] ==> Total steps = 7500 4 | [2022-04-09 23:54:22,802][line: 56] ==> Training Start! 5 | [2022-04-09 23:56:49,171][line: 88] ==> ====Epoch:[1/10] avg_train_loss=0.79052==== 6 | [2022-04-09 23:56:49,171][line: 89] ==> ====Training epoch took: 02:26==== 7 | [2022-04-09 23:56:49,171][line: 90] ==> Running Validation... 8 | [2022-04-09 23:57:04,788][line: 95] ==> ====Epoch:[1/10] avg_val_loss=0.98110 avg_val_acc=0.70512==== 9 | [2022-04-09 23:57:04,788][line: 96] ==> ====Validation epoch took: 02:41==== 10 | [2022-04-09 23:57:04,788][line: 97] ==> 11 | [2022-04-09 23:59:29,837][line: 88] ==> ====Epoch:[2/10] avg_train_loss=0.37617==== 12 | [2022-04-09 23:59:29,837][line: 89] ==> ====Training epoch took: 02:25==== 13 | [2022-04-09 23:59:29,837][line: 90] ==> Running Validation... 14 | [2022-04-09 23:59:45,039][line: 95] ==> ====Epoch:[2/10] avg_val_loss=0.68817 avg_val_acc=0.83162==== 15 | [2022-04-09 23:59:45,039][line: 96] ==> ====Validation epoch took: 02:40==== 16 | [2022-04-09 23:59:45,039][line: 97] ==> 17 | [2022-04-10 00:02:08,939][line: 88] ==> ====Epoch:[3/10] avg_train_loss=0.26341==== 18 | [2022-04-10 00:02:08,939][line: 89] ==> ====Training epoch took: 02:23==== 19 | [2022-04-10 00:02:08,940][line: 90] ==> Running Validation... 20 | [2022-04-10 00:02:24,183][line: 95] ==> ====Epoch:[3/10] avg_val_loss=0.60514 avg_val_acc=0.87126==== 21 | [2022-04-10 00:02:24,183][line: 96] ==> ====Validation epoch took: 02:39==== 22 | [2022-04-10 00:02:24,183][line: 97] ==> 23 | [2022-04-10 00:04:48,402][line: 88] ==> ====Epoch:[4/10] avg_train_loss=0.21501==== 24 | [2022-04-10 00:04:48,402][line: 89] ==> ====Training epoch took: 02:24==== 25 | [2022-04-10 00:04:48,402][line: 90] ==> Running Validation... 26 | [2022-04-10 00:05:03,613][line: 95] ==> ====Epoch:[4/10] avg_val_loss=0.48300 avg_val_acc=0.90010==== 27 | [2022-04-10 00:05:03,614][line: 96] ==> ====Validation epoch took: 02:39==== 28 | [2022-04-10 00:05:03,614][line: 97] ==> 29 | [2022-04-10 00:07:27,687][line: 88] ==> ====Epoch:[5/10] avg_train_loss=0.17764==== 30 | [2022-04-10 00:07:27,687][line: 89] ==> ====Training epoch took: 02:24==== 31 | [2022-04-10 00:07:27,687][line: 90] ==> Running Validation... 32 | [2022-04-10 00:07:42,916][line: 95] ==> ====Epoch:[5/10] avg_val_loss=0.44431 avg_val_acc=0.91182==== 33 | [2022-04-10 00:07:42,916][line: 96] ==> ====Validation epoch took: 02:39==== 34 | [2022-04-10 00:07:42,916][line: 97] ==> 35 | [2022-04-10 00:10:09,527][line: 88] ==> ====Epoch:[6/10] avg_train_loss=0.16060==== 36 | [2022-04-10 00:10:09,527][line: 89] ==> ====Training epoch took: 02:26==== 37 | [2022-04-10 00:10:09,527][line: 90] ==> Running Validation... 38 | [2022-04-10 00:10:24,916][line: 95] ==> ====Epoch:[6/10] avg_val_loss=0.40690 avg_val_acc=0.92429==== 39 | [2022-04-10 00:10:24,916][line: 96] ==> ====Validation epoch took: 02:42==== 40 | [2022-04-10 00:10:24,916][line: 97] ==> 41 | [2022-04-10 00:12:50,498][line: 88] ==> ====Epoch:[7/10] avg_train_loss=0.13307==== 42 | [2022-04-10 00:12:50,498][line: 89] ==> ====Training epoch took: 02:25==== 43 | [2022-04-10 00:12:50,498][line: 90] ==> Running Validation... 44 | [2022-04-10 00:13:05,770][line: 95] ==> ====Epoch:[7/10] avg_val_loss=0.36382 avg_val_acc=0.93775==== 45 | [2022-04-10 00:13:05,770][line: 96] ==> ====Validation epoch took: 02:40==== 46 | [2022-04-10 00:13:05,770][line: 97] ==> 47 | [2022-04-10 00:15:29,989][line: 88] ==> ====Epoch:[8/10] avg_train_loss=0.11915==== 48 | [2022-04-10 00:15:29,989][line: 89] ==> ====Training epoch took: 02:24==== 49 | [2022-04-10 00:15:29,989][line: 90] ==> Running Validation... 50 | [2022-04-10 00:15:45,234][line: 95] ==> ====Epoch:[8/10] avg_val_loss=0.34609 avg_val_acc=0.94141==== 51 | [2022-04-10 00:15:45,234][line: 96] ==> ====Validation epoch took: 02:39==== 52 | [2022-04-10 00:15:45,234][line: 97] ==> 53 | [2022-04-10 00:18:10,119][line: 88] ==> ====Epoch:[9/10] avg_train_loss=0.10920==== 54 | [2022-04-10 00:18:10,119][line: 89] ==> ====Training epoch took: 02:24==== 55 | [2022-04-10 00:18:10,119][line: 90] ==> Running Validation... 56 | [2022-04-10 00:18:25,673][line: 95] ==> ====Epoch:[9/10] avg_val_loss=0.34493 avg_val_acc=0.94440==== 57 | [2022-04-10 00:18:25,673][line: 96] ==> ====Validation epoch took: 02:40==== 58 | [2022-04-10 00:18:25,673][line: 97] ==> 59 | [2022-04-10 00:21:08,030][line: 88] ==> ====Epoch:[10/10] avg_train_loss=0.10598==== 60 | [2022-04-10 00:21:08,030][line: 89] ==> ====Training epoch took: 02:42==== 61 | [2022-04-10 00:21:08,030][line: 90] ==> Running Validation... 62 | [2022-04-10 00:21:25,618][line: 95] ==> ====Epoch:[10/10] avg_val_loss=0.34799 avg_val_acc=0.94423==== 63 | [2022-04-10 00:21:25,619][line: 96] ==> ====Validation epoch took: 02:59==== 64 | [2022-04-10 00:21:25,619][line: 97] ==> 65 | [2022-04-10 00:21:25,876][line: 127] ==> 66 | [2022-04-10 00:21:25,876][line: 128] ==> Training Completed! 67 | -------------------------------------------------------------------------------- /model.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | # @Author : Ezreal 3 | # @File : predict.py 4 | # @Project: Douban_Bert 5 | # @CreateTime : 2022/3/13 上午12:08:22 6 | # @Version：V 0.1 7 | ''' 8 | 模型训练和评估 9 | ''' 10 | import numpy as np 11 | from torch import nn 12 | import time 13 | import os 14 | import torch 15 | import logging 16 | from torch.optim import AdamW 17 | from transformers import BertTokenizer,BertForSequenceClassification, get_linear_schedule_with_warmup 18 | from torch.utils.data import DataLoader 19 | from transformers.utils.notebook import format_time 20 | from data_process import InputDataSet,read_data 21 | import matplotlib.pyplot as plt 22 | 23 | 24 | device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') 25 | 26 | def train(batch_size,EPOCHS): 27 | # model = BertForSequenceClassification.from_pretrained("bert-base-chinese", num_labels=5)使用bert-base-chinese预训练模型 28 | 29 | model = BertForSequenceClassification.from_pretrained("hfl/chinese-bert-wwm", num_labels=5)#使用chinese-bert-wwm预训练模型 30 | 31 | # model = BertForSequenceClassification.from_pretrained("hfl/chinese-roberta-wwm-ext", num_labels=5)#使用hfl/chinese-roberta-wwm-ext预训练模型 32 | 33 | train = read_data('data/train.csv') 34 | val = read_data('data/test.csv') 35 | # tokenizer = BertTokenizer.from_pretrained('bert-base-chinese') 36 | tokenizer = BertTokenizer.from_pretrained('chinese-bert-wwm') 37 | 38 | train_dataset = InputDataSet(train, tokenizer, 64) 39 | val_dataset = InputDataSet(val, tokenizer, 64) 40 | 41 | train_dataloader = DataLoader(train_dataset,batch_size) 42 | val_dataloader = DataLoader(val_dataset,batch_size) 43 | 44 | optimizer = AdamW(model.parameters(), lr=2e-5) 45 | 46 | total_steps = len(train_dataloader) * EPOCHS # len(dataset)*epochs / batchsize 47 | scheduler = get_linear_schedule_with_warmup(optimizer, 48 | num_warmup_steps=0, 49 | num_training_steps=total_steps) 50 | total_t0 = time.time() 51 | 52 | log = log_creater(output_dir='./cache/logs/') 53 | 54 | log.info(" Train batch size = {}".format(batch_size)) 55 | log.info(" Total steps = {}".format(total_steps)) 56 | log.info(" Training Start!") 57 | 58 | train_loss = [] 59 | test_loss = [] 60 | test_acc = [] 61 | 62 | 63 | for epoch in range(EPOCHS): 64 | total_train_loss = 0 65 | t0 = time.time() 66 | model.to(device) 67 | model.train() 68 | for step, batch in enumerate(train_dataloader): 69 | 70 | input_ids = batch['input_ids'].to(device) 71 | attention_mask = batch['attention_mask'].to(device) 72 | token_type_ids = batch['token_type_ids'].to(device) 73 | labels = batch['labels'].to(device) 74 | model.zero_grad() 75 | 76 | outputs = model(input_ids,attention_mask=attention_mask,token_type_ids=token_type_ids,labels=labels) 77 | 78 | loss = outputs.loss 79 | total_train_loss += loss.item() 80 | 81 | loss.backward() 82 | nn.utils.clip_grad_norm_(model.parameters(), 1.0) 83 | optimizer.step() 84 | scheduler.step() 85 | avg_train_loss = total_train_loss / len(train_dataloader) 86 | train_time = format_time(time.time() - t0) 87 | 88 | log.info('====Epoch:[{}/{}] avg_train_loss={:.5f}===='.format(epoch+1,EPOCHS,avg_train_loss)) 89 | log.info('====Training epoch took: {:}===='.format(train_time)) 90 | log.info('Running Validation...') 91 | 92 | model.eval()#这里调用了eval方法 93 | avg_val_loss, avg_val_acc = evaluate(model, val_dataloader) 94 | val_time = format_time(time.time() - t0) 95 | log.info('====Epoch:[{}/{}] avg_val_loss={:.5f} avg_val_acc={:.5f}===='.format(epoch+1,EPOCHS,avg_val_loss,avg_val_acc)) 96 | log.info('====Validation epoch took: {:}===='.format(val_time)) 97 | log.info('') 98 | 99 | if epoch == EPOCHS-1:#保存模型 100 | 101 | output_dir = "./cache/"#定义保存路径 102 | ''' 103 | 第一种保存方法，不推荐这样写 104 | ''' 105 | # model_to_save = model.module if hasattr(model, 'module') else model 106 | # # 如果使用预定义的名称保存，则可以使用`from_pretrained`加载 107 | # output_model_file = os.path.join(output_dir, WEIGHTS_NAME) 108 | # output_config_file = os.path.join(output_dir, CONFIG_NAME) 109 | # 110 | # torch.save(model_to_save.state_dict(), output_model_file) 111 | # model_to_save.config.to_json_file(output_config_file) 112 | # tokenizer.save_vocabulary(output_dir) 113 | 114 | 115 | model_to_save = model.module if hasattr(model, 'module') else model 116 | model_to_save.save_pretrained('cache') 117 | print('Model Saved!') 118 | 119 | 120 | #将数据保存到列表 121 | train_loss.append(avg_train_loss) 122 | test_loss.append(avg_val_loss) 123 | test_acc.append(avg_val_acc) 124 | 125 | 126 | 127 | log.info('') 128 | log.info(' Training Completed!') 129 | print('Total training took {:} (h:mm:ss)'.format(format_time(time.time() - total_t0))) 130 | #简单可视化 131 | x1 = range(0,EPOCHS) 132 | y1 = train_loss 133 | plt.plot(x1, y1) 134 | plt.title("train loss of chinese-bert-wwm") 135 | plt.xlabel("epoches") 136 | plt.ylabel("train loss") 137 | plt.savefig('./cache/wwm_1.png') 138 | plt.close() 139 | # plt.show() 140 | 141 | x2 = range(0,EPOCHS) 142 | y2 = test_loss 143 | plt.plot(x2, y2) 144 | plt.title("test loss of chinese-bert-wwm") 145 | plt.xlabel("epoches") 146 | plt.ylabel("test loss") 147 | plt.savefig('./cache/wwm_2.png') 148 | plt.close() 149 | # plt.show() 150 | 151 | x3 = range(0,EPOCHS) 152 | y3 = test_acc 153 | plt.plot(x3, y3) 154 | plt.title("test acc of chinese-bert-wwm") 155 | plt.xlabel("epoches") 156 | plt.ylabel("test acc") 157 | plt.savefig('./cache/wwm_3.png') 158 | plt.close() 159 | # plt.show() 160 | 161 | 162 | 163 | 164 | 165 | 166 | def evaluate(model,val_dataloader): 167 | total_val_loss = 0 168 | corrects = [] 169 | for batch in val_dataloader: 170 | input_ids = batch['input_ids'].to(device) 171 | attention_mask = batch['attention_mask'].to(device) 172 | token_type_ids = batch['token_type_ids'].to(device) 173 | labels = batch['labels'].to(device) 174 | 175 | with torch.no_grad(): 176 | outputs = model(input_ids,attention_mask=attention_mask,token_type_ids=token_type_ids,labels=labels) 177 | 178 | logits = torch.argmax(outputs.logits,dim=1) 179 | ## 把每个batch预测的准确率加入到一个list中 180 | ## 在加入之前，preds和labels变成cpu的格式 181 | preds = logits.detach().cpu().numpy() 182 | labels_ids = labels.to('cpu').numpy() 183 | corrects.append((preds == labels_ids).mean()) ## [0.8,0.7,0.9] 184 | ## 返回loss 185 | loss = outputs.loss 186 | ## 把每个batch的loss加入 total_val_loss 187 | ## 总共有len(val_dataloader)个batch 188 | total_val_loss += loss.item() 189 | 190 | avg_val_loss = total_val_loss / len(val_dataloader) 191 | avg_val_acc = np.mean(corrects) 192 | 193 | return avg_val_loss, avg_val_acc 194 | 195 | #训练日志，复用性很高的代码 196 | def log_creater(output_dir): 197 | if not os.path.exists(output_dir): 198 | os.makedirs(output_dir) 199 | log_name = '{}.log'.format(time.strftime('%Y-%m-%d-%H-%M')) 200 | final_log_file = os.path.join(output_dir, log_name) 201 | # creat a log 202 | log = logging.getLogger('train_log') 203 | log.setLevel(logging.DEBUG) 204 | 205 | # FileHandler 206 | file = logging.FileHandler(final_log_file) 207 | file.setLevel(logging.DEBUG) 208 | 209 | # StreamHandler 210 | stream = logging.StreamHandler() 211 | stream.setLevel(logging.DEBUG) 212 | 213 | # Formatter 214 | formatter = logging.Formatter( 215 | '[%(asctime)s][line: %(lineno)d] ==> %(message)s') 216 | 217 | # setFormatter 218 | file.setFormatter(formatter) 219 | stream.setFormatter(formatter) 220 | 221 | # addHandler 222 | log.addHandler(file) 223 | log.addHandler(stream) 224 | 225 | log.info('creating {}'.format(final_log_file)) 226 | return log 227 | 228 | 229 | if __name__ == '__main__': 230 | train(batch_size=64,EPOCHS=10) 231 | -------------------------------------------------------------------------------- /static/Home.css: -------------------------------------------------------------------------------- 1 | .u-section-1 { 2 | background-image: none; 3 | } 4 | 5 | .u-section-1 .u-sheet-1 { 6 | min-height: 624px; 7 | } 8 | 9 | .u-section-1 .u-text-1 { 10 | font-size: 2.25rem; 11 | margin: 60px auto 0 20px; 12 | } 13 | 14 | .u-section-1 .u-layout-wrap-1 { 15 | margin-top: 25px; 16 | margin-bottom: 0; 17 | } 18 | 19 | .u-section-1 .u-layout-cell-1 { 20 | min-height: 99px; 21 | } 22 | 23 | .u-section-1 .u-container-layout-1 { 24 | padding: 20px 15px; 25 | } 26 | 27 | .u-section-1 .u-form-1 { 28 | height: 47px; 29 | margin: 0 25px 0 5px; 30 | } 31 | 32 | .u-section-1 .u-layout-cell-2 { 33 | min-height: 99px; 34 | background-image: none; 35 | --animation-custom_in-translate_x: 300px; 36 | --animation-custom_in-translate_y: 0px; 37 | --animation-custom_in-opacity: 0; 38 | --animation-custom_in-rotate: 0deg; 39 | --animation-custom_in-scale: 1; 40 | } 41 | 42 | .u-section-1 .u-container-layout-2 { 43 | padding: 27px 30px; 44 | } 45 | 46 | .u-section-1 .u-social-icons-1 { 47 | height: 45px; 48 | min-height: 16px; 49 | width: 210px; 50 | min-width: 94px; 51 | margin: 0 auto; 52 | } 53 | 54 | .u-section-1 .u-icon-1 { 55 | height: 100%; 56 | } 57 | 58 | .u-section-1 .u-icon-2 { 59 | height: 100%; 60 | } 61 | 62 | .u-section-1 .u-icon-3 { 63 | height: 100%; 64 | } 65 | 66 | .u-section-1 .u-icon-4 { 67 | height: 0; 68 | } 69 | 70 | .u-section-1 .u-text-2 { 71 | font-size: 0.875rem; 72 | margin: 52px 750px 0 25px; 73 | } 74 | 75 | .u-section-1 .u-image-1 { 76 | width: 100px; 77 | height: 99px; 78 | background-image: url("images/cebeb11125c6c963b6a04fc48f61182.jpg"); 79 | background-position: 50% 50%; 80 | margin: 100px 979px 0 61px; 81 | } 82 | 83 | .u-section-1 .u-text-3 { 84 | font-weight: 700; 85 | margin: -70px auto 60px 199px; 86 | } 87 | 88 | @media (max-width: 1199px) { 89 | .u-section-1 .u-sheet-1 { 90 | min-height: 607px; 91 | } 92 | 93 | .u-section-1 .u-text-1 { 94 | font-weight: normal; 95 | } 96 | 97 | .u-section-1 .u-layout-cell-1 { 98 | min-height: 82px; 99 | } 100 | 101 | .u-section-1 .u-form-1 { 102 | margin-right: 0; 103 | margin-left: 0; 104 | } 105 | 106 | .u-section-1 .u-layout-cell-2 { 107 | min-height: 82px; 108 | } 109 | 110 | .u-section-1 .u-text-2 { 111 | margin-right: 575px; 112 | margin-left: 0; 113 | } 114 | 115 | .u-section-1 .u-image-1 { 116 | width: 99px; 117 | margin-right: 840px; 118 | margin-left: 0; 119 | } 120 | } 121 | 122 | @media (max-width: 991px) { 123 | .u-section-1 .u-sheet-1 { 124 | min-height: 625px; 125 | } 126 | 127 | .u-section-1 .u-layout-cell-1 { 128 | min-height: 100px; 129 | } 130 | 131 | .u-section-1 .u-layout-cell-2 { 132 | min-height: 63px; 133 | } 134 | 135 | .u-section-1 .u-text-2 { 136 | margin-right: 355px; 137 | } 138 | 139 | .u-section-1 .u-image-1 { 140 | margin-right: 620px; 141 | } 142 | } 143 | 144 | @media (max-width: 767px) { 145 | .u-section-1 .u-sheet-1 { 146 | min-height: 748px; 147 | } 148 | 149 | .u-section-1 .u-container-layout-1 { 150 | padding-left: 10px; 151 | padding-right: 10px; 152 | } 153 | 154 | .u-section-1 .u-layout-cell-2 { 155 | min-height: 123px; 156 | } 157 | 158 | .u-section-1 .u-container-layout-2 { 159 | padding-left: 10px; 160 | padding-right: 10px; 161 | } 162 | 163 | .u-section-1 .u-text-2 { 164 | margin-right: 175px; 165 | } 166 | 167 | .u-section-1 .u-image-1 { 168 | margin-right: 440px; 169 | } 170 | 171 | .u-section-1 .u-text-3 { 172 | margin-left: 149px; 173 | } 174 | } 175 | 176 | @media (max-width: 575px) { 177 | .u-section-1 .u-sheet-1 { 178 | min-height: 702px; 179 | } 180 | 181 | .u-section-1 .u-layout-cell-2 { 182 | min-height: 77px; 183 | } 184 | 185 | .u-section-1 .u-text-2 { 186 | margin-right: 0; 187 | } 188 | 189 | .u-section-1 .u-image-1 { 190 | margin-right: 240px; 191 | } 192 | 193 | .u-section-1 .u-text-3 { 194 | margin-left: 94px; 195 | } 196 | } .u-section-2 { 197 | min-height: 708px; 198 | } 199 | 200 | .u-section-2 .u-layout-wrap-1 { 201 | margin-top: 0; 202 | margin-bottom: 0; 203 | } 204 | 205 | .u-section-2 .u-layout-cell-1 { 206 | min-height: 708px; 207 | background-image: none; 208 | } 209 | 210 | .u-section-2 .u-container-layout-1 { 211 | padding: 30px 0; 212 | } 213 | 214 | .u-section-2 .u-layout-cell-2 { 215 | min-height: 708px; 216 | background-image: none; 217 | } 218 | 219 | .u-section-2 .u-container-layout-2 { 220 | padding: 30px 0; 221 | } 222 | 223 | .u-section-2 .u-group-1 { 224 | width: 472px; 225 | min-height: 613px; 226 | margin: 22px auto 0 -236px; 227 | } 228 | 229 | .u-section-2 .u-container-layout-3 { 230 | padding-bottom: 30px; 231 | } 232 | 233 | .u-section-2 .u-text-1 { 234 | text-transform: uppercase; 235 | font-size: 6rem; 236 | font-weight: 700; 237 | letter-spacing: 2px; 238 | margin: 106px 0 0; 239 | } 240 | 241 | .u-section-2 .u-image-1 { 242 | min-height: 708px; 243 | background-image: url("images/7c62f9fc1b977eee788f421e5145fa7.jpg"); 244 | background-position: 50% 50%; 245 | --animation-custom_in-translate_x: 300px; 246 | --animation-custom_in-translate_y: 0px; 247 | --animation-custom_in-opacity: 0; 248 | --animation-custom_in-rotate: 0deg; 249 | --animation-custom_in-scale: 1; 250 | } 251 | 252 | .u-section-2 .u-container-layout-4 { 253 | padding: 30px; 254 | } 255 | 256 | @media (max-width: 1199px) { 257 | .u-section-2 { 258 | min-height: 705px; 259 | } 260 | 261 | .u-section-2 .u-layout-cell-1 { 262 | min-height: 705px; 263 | } 264 | 265 | .u-section-2 .u-layout-cell-2 { 266 | min-height: 705px; 267 | } 268 | 269 | .u-section-2 .u-group-1 { 270 | width: 469px; 271 | min-height: 623px; 272 | margin-left: -265px; 273 | } 274 | 275 | .u-section-2 .u-image-1 { 276 | min-height: 705px; 277 | } 278 | } 279 | 280 | @media (max-width: 991px) { 281 | .u-section-2 { 282 | min-height: 644px; 283 | } 284 | 285 | .u-section-2 .u-layout-cell-1 { 286 | min-height: 644px; 287 | } 288 | 289 | .u-section-2 .u-layout-cell-2 { 290 | min-height: 644px; 291 | } 292 | 293 | .u-section-2 .u-group-1 { 294 | width: 381px; 295 | min-height: 560px; 296 | margin-left: -225px; 297 | } 298 | 299 | .u-section-2 .u-container-layout-3 { 300 | padding-bottom: 20px; 301 | } 302 | 303 | .u-section-2 .u-text-1 { 304 | font-size: 4.5rem; 305 | width: auto; 306 | } 307 | 308 | .u-section-2 .u-image-1 { 309 | min-height: 644px; 310 | } 311 | } 312 | 313 | @media (max-width: 767px) { 314 | .u-section-2 { 315 | min-height: 1872px; 316 | } 317 | 318 | .u-section-2 .u-layout-cell-1 { 319 | min-height: 180px; 320 | } 321 | 322 | .u-section-2 .u-layout-cell-2 { 323 | min-height: 478px; 324 | } 325 | 326 | .u-section-2 .u-container-layout-2 { 327 | padding-top: 0; 328 | padding-bottom: 0; 329 | } 330 | 331 | .u-section-2 .u-group-1 { 332 | width: 554px; 333 | margin-top: -60px; 334 | margin-left: auto; 335 | } 336 | 337 | .u-section-2 .u-image-1 { 338 | min-height: 1073px; 339 | } 340 | 341 | .u-section-2 .u-container-layout-4 { 342 | padding-left: 10px; 343 | padding-right: 10px; 344 | } 345 | } 346 | 347 | @media (max-width: 575px) { 348 | .u-section-2 { 349 | min-height: 1223px; 350 | } 351 | 352 | .u-section-2 .u-layout-cell-1 { 353 | min-height: 113px; 354 | } 355 | 356 | .u-section-2 .u-layout-cell-2 { 357 | min-height: 500px; 358 | } 359 | 360 | .u-section-2 .u-group-1 { 361 | width: 340px; 362 | } 363 | 364 | .u-section-2 .u-image-1 { 365 | min-height: 676px; 366 | } 367 | }.u-section-3 .u-sheet-1 { 368 | min-height: 667px; 369 | } 370 | 371 | .u-section-3 .u-layout-wrap-1 { 372 | margin-top: 60px; 373 | margin-bottom: -20px; 374 | } 375 | 376 | .u-section-3 .u-layout-cell-1 { 377 | min-height: 467px; 378 | background-image: none; 379 | } 380 | 381 | .u-section-3 .u-container-layout-1 { 382 | padding: 0 27px 30px; 383 | } 384 | 385 | .u-section-3 .u-image-1 { 386 | width: 213px; 387 | height: 213px; 388 | background-image: url("images/cebeb11125c6c963b6a04fc48f61182-4.jpg"); 389 | background-position: 50% 50%; 390 | margin: -95px auto 0; 391 | } 392 | 393 | .u-section-3 .u-text-1 { 394 | font-size: 1.875rem; 395 | text-transform: uppercase; 396 | font-weight: 700; 397 | font-family: Georgia, serif; 398 | margin: 24px auto 0; 399 | } 400 | 401 | .u-section-3 .u-social-icons-1 { 402 | height: 46px; 403 | min-height: 16px; 404 | width: 220px; 405 | min-width: 100px; 406 | margin: 58px auto 0; 407 | } 408 | 409 | .u-section-3 .u-icon-3 { 410 | height: 100%; 411 | } 412 | 413 | .u-section-3 .u-icon-4 { 414 | height: 0; 415 | } 416 | 417 | .u-section-3 .u-layout-cell-2 { 418 | min-height: 467px; 419 | --animation-custom_in-translate_x: 300px; 420 | --animation-custom_in-translate_y: 0px; 421 | --animation-custom_in-opacity: 0; 422 | --animation-custom_in-rotate: 0deg; 423 | --animation-custom_in-scale: 1; 424 | } 425 | 426 | .u-section-3 .u-container-layout-2 { 427 | padding: 30px 60px; 428 | } 429 | 430 | .u-section-3 .u-text-2 { 431 | font-size: 6rem; 432 | font-weight: 700; 433 | text-transform: uppercase; 434 | margin: 1px auto 0 0; 435 | } 436 | 437 | .u-section-3 .u-text-3 { 438 | margin-top: 19px; 439 | font-size: 1rem; 440 | margin-bottom: 0; 441 | } 442 | 443 | .u-section-3 .u-btn-1 { 444 | border-style: solid; 445 | font-weight: 700; 446 | text-transform: uppercase; 447 | font-size: 1rem; 448 | letter-spacing: 1px; 449 | margin: 58px auto 0 0; 450 | padding: 18px 46px 20px 45px; 451 | } 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padding-left: 10px; 689 | padding-right: 10px; 690 | } 691 | 692 | .u-section-4 .u-container-layout-5 { 693 | padding-left: 10px; 694 | padding-right: 10px; 695 | } 696 | 697 | .u-section-4 .u-image-5 { 698 | min-height: 684px; 699 | } 700 | 701 | .u-section-4 .u-container-layout-6 { 702 | padding-left: 10px; 703 | padding-right: 10px; 704 | } 705 | } 706 | 707 | @media (max-width: 575px) { 708 | .u-section-4 .u-sheet-1 { 709 | min-height: 1359px; 710 | } 711 | 712 | .u-section-4 .u-image-1 { 713 | min-height: 317px; 714 | } 715 | 716 | .u-section-4 .u-image-2 { 717 | min-height: 190px; 718 | } 719 | 720 | .u-section-4 .u-image-3 { 721 | min-height: 294px; 722 | } 723 | 724 | .u-section-4 .u-image-4 { 725 | min-height: 217px; 726 | } 727 | 728 | .u-section-4 .u-image-5 { 729 | min-height: 431px; 730 | } 731 | } -------------------------------------------------------------------------------- /templates/index.html: 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60 | 115 | 138 | 179 | 233 | 234 | 235 | 238 |
239 | 240 | Best Website Design 241 | 242 |

243 | created with 244 |

245 | 246 | HTML Designer 247 | . 248 |
249 | 250 | -------------------------------------------------------------------------------- /templates/Home.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | Home 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 27 | 28 | 29 | 30 | 31 |
32 | 59 |
60 | 115 | 138 | 179 | 233 | 234 | 235 | 238 |
239 | 240 | Best Website Design 241 | 242 |

243 | created with 244 |

245 | 246 | HTML Designer 247 | . 248 |
249 | 250 | -------------------------------------------------------------------------------- /static/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | Home 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 27 | 28 | 29 | 30 | 31 |
32 | 59 |
60 | 115 | 138 | 179 | 233 | 234 | 235 | 238 |
239 | 240 | Best Website Design 241 | 242 |

243 | created with 244 |

245 | 246 | HTML Designer 247 | . 248 |
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