google / text2text Goto Github PK

# cd to your workspace
# 1. Clone the text2text code to your workspace 'text2text' directory.
# 2. Create an empty 'WORKSPACE' file in your workspace.
# 3. Preapre the config file wrt the model you wish to run and put it in the
#    config directory.

ls -R
.:
text2text  WORKSPACE

./text2text:
batch_reader  beam_search.py  BUILD  config  data  data.py  decode.py  
__init__.py  library.py  main.py  metrics.py  model  README.md

./text2text/batch_reader:
copynet_batcher.py  __init__.py  vocab_batcher.py

./text2text/config:
cfg_copynet.py  cfg_seq2seq.py  cfg_bow2seq.py
 __init__.py 

./text2text/model:
copynet.py  __init__.py  seq2seq.py  bow2seq.py

./text2text/data:
data  data_convert_example.py  text_data  vocab


bazel build -c opt --copt=-mavx --config=cuda text2text:main

# Run the training.
bazel-bin/text2text/main \
    --mode=train \
    --config="cfg_seq2seq" \
    --log_root="text2text/log_root" \
    --override="eval_interval_secs=0" \
    --logtostderr

# Run the eval. Try to avoid running on the same machine as training.
bazel-bin/text2text/main \
    --mode=eval \
    --config="cfg_seq2seq" \
    --log_root="text2text/log_root" \
    --logtostderr

# Run the decode. Run it when the model is mostly converged.
bazel-bin/text2text/main \
  --mode=decode \
    --config="cfg_seq2seq" \
    --log_root="text2text/log_root" \
    --logtostderr