update

Author: JustinLin610

README.md

@@ -1,57 +1,57 @@
-# Semantic-Unit-for-Multi-label-Text-Classification
-Code for the article "Semantic-Unit-Based Dilated Convolution for Multi-Label Text Classification" (EMNLP 2018).
-
-***********************************************************
-
-## Requirements
-* Ubuntu 16.0.4
-* Python 3.5
-* Pytorch 0.4.1 (updated)
-
-**************************************************************
-
-## Data
-Our preprocessed RCV1-V2 dataset can be retrieved through [this link](https://drive.google.com/open?id=1oQ5_gPoRwAl7UGWTDNu4qATNtJ1l1kXd).
-
-***************************************************************
-
-## Preprocessing
-```
-python3 preprocess.py -load_data path_to_data -save_data path_to_store_data 
-```
-Remember to put the data into a folder and name them *train.src*, *train.tgt*, *valid.src*, *valid.tgt*, *test.src* and *test.tgt*, and make a new folder inside called *data*
-
-***************************************************************
-
-## Training
-```
-python3 train.py -log log_name -config config_yaml -gpus id
-```
-Create your own yaml file for hyperparameter setting.
-
-****************************************************************
-
-## Evaluation
-```
-python3 train.py -log log_name -config config_yaml -gpus id -restore checkpoint -mode eval
-```
-
-*******************************************************************
-
-# Citation
-If you use this code for your research, please kindly cite our paper:
-```
-@inproceedings{DBLP:conf/emnlp/LinSYM018,
-  author    = {Junyang Lin and
-               Qi Su and
-               Pengcheng Yang and
-               Shuming Ma and
-               Xu Sun},
-  title     = {Semantic-Unit-Based Dilated Convolution for Multi-Label Text Classification},
-  booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural
-               Language Processing, Brussels, Belgium, October 31 - November 4, 2018},
-  pages     = {4554--4564},
-  year      = {2018}
-}
-```
-
+# Semantic-Unit-for-Multi-label-Text-Classification
+Code for the article "Semantic-Unit-Based Dilated Convolution for Multi-Label Text Classification" (EMNLP 2018).
+
+***********************************************************
+
+## Requirements
+* Ubuntu 16.0.4
+* Python 3.5
+* Pytorch 0.4.1 (updated)
+
+**************************************************************
+
+## Data
+Our preprocessed RCV1-V2 dataset can be retrieved through [this link](https://drive.google.com/open?id=1oQ5_gPoRwAl7UGWTDNu4qATNtJ1l1kXd). (The json file of label set for evaluation is added for convenience.)
+
+***************************************************************
+
+## Preprocessing
+```
+python3 preprocess.py -load_data path_to_data -save_data path_to_store_data (-src_filter 500)
+```
+Remember to put the data (plain text file) into a folder and name them *train.src*, *train.tgt*, *valid.src*, *valid.tgt*, *test.src* and *test.tgt*, and make a new folder inside called *data*. 
+
+***************************************************************
+
+## Training
+```
+python3 train.py -log log_name -config config_yaml -gpus id (-label_dict_file path to your label set)
+```
+Create your own yaml file for hyperparameter setting.
+
+****************************************************************
+
+## Evaluation
+```
+python3 train.py -log log_name -config config_yaml -gpus id -restore checkpoint -mode eval
+```
+
+*******************************************************************
+
+# Citation
+If you use this code for your research, please kindly cite our paper:
+```
+@inproceedings{DBLP:conf/emnlp/LinSYM018,
+  author    = {Junyang Lin and
+               Qi Su and
+               Pengcheng Yang and
+               Shuming Ma and
+               Xu Sun},
+  title     = {Semantic-Unit-Based Dilated Convolution for Multi-Label Text Classification},
+  booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural
+               Language Processing, Brussels, Belgium, October 31 - November 4, 2018},
+  pages     = {4554--4564},
+  year      = {2018}
+}
+```
+

semantic-unit-based/config.yaml

@@ -1,28 +1,28 @@
-data: '/home/linjunyang/multilabel_rcv/data/'
-logF: 'experiments/rcv/'
-epoch: 20
-batch_size: 64
-optim: 'adam'
-cell: 'lstm'
-attention: 'luong_gate'
-learning_rate: 0.0003
-max_grad_norm: 10
-learning_rate_decay: 0.5
-start_decay_at: 2
-emb_size: 512
-hidden_size: 512
-dec_num_layers: 2
-enc_num_layers: 2
-bidirectional: True
-dropout: 0.2
-max_time_step: 30
-eval_interval: 500
-save_interval: 1000
-unk: False
-schedule: False
-schesamp: False
-length_norm: True
-metrics: ['hamming_loss', 'macro_f1', 'micro_f1']
-shared_vocab: False
-beam_size: 5
+data: '/home/linjunyang/multilabel_rcv/data/'
+logF: 'experiments/rcv/'
+epoch: 20
+batch_size: 64
+optim: 'adam'
+cell: 'lstm'
+attention: 'luong_gate'
+learning_rate: 0.0003
+max_grad_norm: 10
+learning_rate_decay: 0.5
+start_decay_at: 2
+emb_size: 512
+hidden_size: 512
+dec_num_layers: 2
+enc_num_layers: 2
+bidirectional: True
+dropout: 0.2
+max_time_step: 30
+eval_interval: 500
+save_interval: 1000
+unk: False
+schedule: False
+schesamp: False
+length_norm: True
+metrics: ['hamming_loss', 'macro_f1', 'micro_f1']
+shared_vocab: False
+beam_size: 5
 dilated: True