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model: DynamicBot | ||
dataset: Cornell | ||
model_params: | ||
attention_size: 256 # (optional even with AttentionDecoder) | ||
batch_size: 128 | ||
base_cell: GRUCell | ||
encoder.class: BidirectionalEncoder | ||
base_cell: LSTMCell | ||
num_layers: 2 | ||
attention_mechanism: LuongAttention | ||
decoder.class: AttentionDecoder | ||
num_layers: 1 | ||
ckpt_dir: out/cornell/bi_attn | ||
steps_per_ckpt: 250 | ||
encoder.class: BidirectionalEncoder | ||
ckpt_dir: out/cornell | ||
dataset_params: | ||
data_dir: /home/brandon/Datasets/cornell # The only truly 'mandatory' parameter. | ||
vocab_size: 52000 # Approximately the true number of unique words in the dataset. | ||
max_seq_len: 20 | ||
optimize_params: True |
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dataset: data.Reddit | ||
dataset_params: | ||
config_path: configs/example_reddit.yml | ||
data_dir: /home/brandon/Datasets/reddit | ||
max_seq_len: 25 | ||
vocab_size: 80000 | ||
model: DynamicBot | ||
dataset: Reddit | ||
model_params: | ||
base_cell: GRUCell | ||
batch_size: 64 | ||
ckpt_dir: out/reddit/talkativeAttention | ||
decoder.class: AttentionDecoder | ||
batch_size: 128 | ||
embed_size: 128 | ||
num_layers: 1 | ||
reset_model: true | ||
steps_per_ckpt: 200 | ||
ckpt_dir: out/reddit/basicReddit | ||
dataset_params: | ||
data_dir: /home/brandon/Datasets/reddit | ||
max_seq_len: 15 | ||
vocab_size: 80000 # HUGE dataset = huge vocabulary. | ||
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model: DynamicBot | ||
dataset: Ubuntu | ||
model_params: | ||
base_cell: GRUCell | ||
ckpt_dir: out/ubuntu | ||
decoder.class: BasicDecoder | ||
encoder.class: BasicEncoder | ||
num_layers: 2 | ||
state_size: 512 | ||
dataset_params: | ||
data_dir: /home/brandon/Datasets/ubuntu | ||
vocab_size: 60000 # Should probably be higher. Ubuntu is noisy. | ||
max_seq_len: 12 # Any longer, and output quality is a challenge. |