Text-Classification-of-Product-Titles-pytorch

Classification for products on e-commerce platform according to product names.

Solution for The 10th National College Students Service Outsourcing Innovation and Entrepreneurship Competition (【A01】2018 网络零售平台商品分类【浪潮】, 第十届中国大学生服务外包创新创业大赛).

Requirements

• Python 3
• torch==1.4.0 with CUDA
• jieba
• AlfredWGA/nlputils
• tqdm

Dataset

Training set contains 500,000 product names and 1258 labels. (Download from here).
Pre-trained Chinese word embeddings are obtained from this repo Embedding/Chinese-Word-Vectors.
Word segementation are done by jieba.

Pre-processing

Place all data files in the path specified in config.py.

python preprocess.py 

Train our models

We implemented TextCNN, BiLSTM and BiGRU for text classification. We use character level (no word segmentation) and word level input.

python src/train.py --help
usage: train.py [-h] [-lr LR] [--batch_size BATCH_SIZE]
                [--model {textcnn,textbilstm,textbigru}] [--mode {char,word}]
                [-e EPOCH]

optional arguments:
-h, --help            show this help message and exit
-lr LR, --lr LR       Learning rate. Default 1e-3.
--batch_size BATCH_SIZE
                        Batch size. Default 128.
--model {textcnn,textbilstm,textbigru}
                        The classification model. Default textcnn.
--mode {char,word}    Type of the embedding input.
-e EPOCH, --epoch EPOCH
                        Number of training epoch.

Expected outputs:

2020-02-06 17:40:47,792 - __main__ - INFO - {'lr': 0.001, 'batch_size': 128, 'model': 'textcnn', 'mode': 'char', 'epoch': 100}
2020-02-06 17:40:49,145 - __main__ - INFO - Train set size: 400000, valid set size 100000
2020-02-06 17:40:51,248 - __main__ - INFO - Model modules:
2020-02-06 17:40:51,250 - __main__ - INFO - 0 -> ('embedding', Embedding(767, 300, padding_idx=0))
2020-02-06 17:40:51,251 - __main__ - INFO - 1 -> ('conv1d_2', Conv1d(300, 150, kernel_size=(2,), stride=(1,)))
2020-02-06 17:40:51,254 - __main__ - INFO - 2 -> ('conv1d_3', Conv1d(300, 150, kernel_size=(3,), stride=(1,)))
2020-02-06 17:40:51,257 - __main__ - INFO - 3 -> ('conv1d_4', Conv1d(300, 150, kernel_size=(4,), stride=(1,)))
2020-02-06 17:40:51,258 - __main__ - INFO - 4 -> ('relu', ReLU())
2020-02-06 17:40:51,259 - __main__ - INFO - 5 -> ('fc1', Linear(in_features=450, out_features=256, bias=True))
2020-02-06 17:40:51,260 - __main__ - INFO - 6 -> ('dropout', Dropout(p=0.5, inplace=False))
2020-02-06 17:40:51,261 - __main__ - INFO - 7 -> ('fc2', Linear(in_features=256, out_features=1258, bias=True))
2020-02-06 17:40:51,262 - __main__ - INFO - Total params: 1,074,312
2020-02-06 17:40:51,264 - __main__ - INFO - Trainable params: 1,074,312
2020-02-06 17:42:40,828 - __main__ - INFO - Epoch 0, valid loss 1.01316, valid acc 0.7712