update readme

README.md

@@ -1,5 +1,93 @@
 # BERT4Rec
 
 
+## Usage
 
-TBD
+**Requirements**
+
+* python 2.7+
+* Tensorflow 1.12 (GPU version)
+* CUDA compatible with TF 1.12
+
+**Run**
+
+For simplicity, here we take ml-1m as an example:
+
+``` bash
+./run_ml-1m.sh
+```
+include two part command:
+generated masked training data
+``` bash
+python -u gen_data_fin.py \
+    --dataset_name=${dataset_name} \
+    --max_seq_length=${max_seq_length} \
+    --max_predictions_per_seq=${max_predictions_per_seq} \
+    --mask_prob=${mask_prob} \
+    --dupe_factor=${dupe_factor} \
+    --masked_lm_prob=${masked_lm_prob} \
+    --prop_sliding_window=${prop_sliding_window} \
+    --signature=${signature} \
+    --pool_size=${pool_size} \
+```
+
+train the model
+``` bash
+CUDA_VISIBLE_DEVICES=0 python -u run.py \
+    --train_input_file=./data/${dataset_name}${signature}.train.tfrecord \
+    --test_input_file=./data/${dataset_name}${signature}.test.tfrecord \
+    --vocab_filename=./data/${dataset_name}${signature}.vocab \
+    --user_history_filename=./data/${dataset_name}${signature}.his \
+    --checkpointDir=${CKPT_DIR}/${dataset_name} \
+    --signature=${signature}-${dim} \
+    --do_train=True \
+    --do_eval=True \
+    --bert_config_file=./bert_train/bert_config_${dataset_name}_${dim}.json \
+    --batch_size=${batch_size} \
+    --max_seq_length=${max_seq_length} \
+    --max_predictions_per_seq=${max_predictions_per_seq} \
+    --num_train_steps=${num_train_steps} \
+    --num_warmup_steps=100 \
+    --learning_rate=1e-4
+```
+
+### hyper-parameter settings
+json in `bert_train` like `bert_config_ml-1m_64.json`
+
+```json
+{
+  "attention_probs_dropout_prob": 0.2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.2,
+  "hidden_size": 64,
+  "initializer_range": 0.02,
+  "intermediate_size": 256,
+  "max_position_embeddings": 200,
+  "num_attention_heads": 2,
+  "num_hidden_layers": 2,
+  "type_vocab_size": 2,
+  "vocab_size": 3420
+}
+```
+
+
+## Reference
+
+```TeX
+@inproceedings{Sun:2019:BSR:3357384.3357895,
+ author = {Sun, Fei and Liu, Jun and Wu, Jian and Pei, Changhua and Lin, Xiao and Ou, Wenwu and Jiang, Peng},
+ title = {BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer},
+ booktitle = {Proceedings of the 28th ACM International Conference on Information and Knowledge Management},
+ series = {CIKM '19},
+ year = {2019},
+ isbn = {978-1-4503-6976-3},
+ location = {Beijing, China},
+ pages = {1441--1450},
+ numpages = {10},
+ url = {http://doi.acm.org/10.1145/3357384.3357895},
+ doi = {10.1145/3357384.3357895},
+ acmid = {3357895},
+ publisher = {ACM},
+ address = {New York, NY, USA}
+} 
+```

run_beauty.sh

@@ -4,7 +4,7 @@ max_seq_length=50
 max_predictions_per_seq=30
 masked_lm_prob=0.6
 
-dim=16
+dim=64
 batch_size=256
 num_train_steps=400000