76 lines
1.3 KiB
YAML
76 lines
1.3 KiB
YAML
# train config
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msg_info: 'bert_dp04'
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random_seed: 2942435
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num_epochs: 100
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batch_size: 32
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init_lr: 0.0012
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decay_lr: 0.8
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minimum_lr: 0.0002
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patience: 60
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grad_clipping: 10.0
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unk_ratio: 0.5
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weight_decay: 5e-5
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# embedding config
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use_sentence_vec: true
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sent_vec_project: 0
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sent_vec_project_activation: 'linear'
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#dp_sent_vec: 0.55
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# concat sentence vector after BiLSTM
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# if cat_sent_after_rnn is false, we concat sent_vec and word_embed, before BiLSTM
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cat_sent_after_rnn: false
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encoder_project: 0 #200
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# use random initialized word embedding
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random_word_embed: false
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# use pre-trained word embedding
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use_pretrain_embed: true
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# tuning pre-trained embedding
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pretrain_embed_tune: true
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word_embed_trainable: true
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word_embed_dim: 100
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word_project_dim: 200
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use_char_rnn: true
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char_embed_dim: 40
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char_rnn_dim: 40
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dp_emb: 0.43
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use_pos: false
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pos_embed_dim: 30
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# LSTM and stack LSTM config
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use_rnn_encoder: true
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encoder_layer: 1
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rnn_dim: 140
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dp_rnn: 0.23
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grn_dim: 180
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grn_steps: 5
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lmda_rnn_dim: 120
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part_ent_rnn_dim: 100
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action_rnn_dim: 60
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out_rnn_dim: 60
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dp_state: 0.15
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dp_state_h: 0.1
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dp_buffer: 0.
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dp_stack: 0.15
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# output config
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output_hidden_dim: 160
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dp_out: 0.2
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# for tree rnn config
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action_embed_dim: 50
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trigger_embed_dim: 20
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entity_embed_dim: 20
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argument_embed_dim: 20
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edge_embed_dim: 30
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use_arg_type_tree: true
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