dynmt-py
Neural machine translation implementation using dynet's python bindings.
Example Usage:
python dynmt.py --dynet-autobatch 0 --dynet-devices GPU:1 --dynet-mem 12000 \
--input-dim=500 --hidden-dim=1024 --epochs=100 --lstm-layers=1 --optimization=ADADELTA \
--batch-size=60 --beam-size=5 --vocab 30000 --plot --eval-after=10000 \
train_source.txt train_target.txt dev_source.txt dev_target.txt test_source.txt test_target.txt path/to/model/dir
Options:
| Name | Description |
|---|---|
| -h --help | shows a help message and exits |
| --dynet-mem MEM | allocates MEM bytes for dynet (see dynet's documentation for more details) |
| --dynet-gpus GPUS | how many gpus to use (see dynet's documentation for more details) |
| --dynet-devices DEV | CPU/GPU ids to use (see dynet's documentation for more details) |
| --dynet-autobatch AUTO | switch auto-batching on (see dynet's documentation for more details) |
| --input-dim=INPUT | input embeddings dimension [default: 300] |
| --hidden-dim=HIDDEN | LSTM hidden layer dimension [default: 100] |
| --epochs=EPOCHS | amount of training epochs [default: 1] |
| --layers=LAYERS | amount of layers in LSTM [default: 1] |
| --optimization=OPTIMIZATION | chosen optimization method (ADAM/SGD/ADAGRAD/MOMENTUM/ADADELTA) [default: ADADELTA] |
| --reg=REGULARIZATION | regularization parameter for optimization [default: 0] |
| --learning=LEARNING | learning rate parameter for optimization [default: 0.0001] |
| --batch-size=BATCH | batch size [default: 1] |
| --beam-size=BEAM | beam size in beam search [default: 5] |
| --vocab-size=VOCAB | max vocabulary size [default: 99999] |
| --eval-after=EVALAFTER | amount of train batches to wait before evaluation [default: 1000] |
| --max-len=MAXLEN | max train sequence length [default: 50] |
| --max-pred=MAXPRED | max predicted sequence length [default: 50] |
| --grad-clip=GRADCLIP | gradient clipping threshold [default: 5.0] |
| --max-patience=MAXPATIENCE | amount of checkpoints without improvement on dev before early stopping [default: 100] |
| --plot | plot a learning curve while training each model |
| --override | override existing model with the same name, if exists |
| --ensemble=ENSEMBLE | ensemble model paths separated by a comma |
| --last-state | only use last encoder state |
| --eval | skip training, do only evaluation |
Arguments (must be given in this order):
| Name | Description |
|---|---|
| TRAIN_INPUTS_PATH | train inputs path |
| TRAIN_OUTPUTS_PATH | train outputs path |
| DEV_INPUTS_PATH | development inputs path |
| DEV_OUTPUTS_PATH | development outputs path |
| TEST_INPUTS_PATH | test inputs path |
| TEST_OUTPUTS_PATH | test outputs path |
| RESULTS_PATH | results file path |