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/content/drive/MyDrive/LuanVan/Bart-BRIO/brio_project-main/BRIO/main.py
Namespace(cuda=True, gpuid=[0], evaluate=False, do_reranking=False, do_generation=False, log=True, port=12355, model_pt='', config='', batch_size=1, epoch=1, report_freq=100, accumulate_step=8, margin=0.001, gold_margin=0, gold_weight=0, mle_weight=0.1, rank_weight=10, model_type='vinai/bartpho-word-base', warmup_steps=10000, normalize=True, grad_norm=0, seed=970903, no_gold=False, pretrained='./finetuned_model_v3/eval_bartpho_final', max_lr=0.002, scale=0.5, score_mode='log', datatype='diverse', dataset='cooking_bart', max_len=120, max_num=6, smooth=0.01, total_len=1024, length_penalty=2.0, do_sample=True, gen_max_len=140, gen_min_len=55, is_pegasus=False, adding=0, eval_interval=1000, num_beams=6)
BRIO(
(model): MBartScorer(
(model): CustomMBartModel(
(shared): Embedding(64001, 768, padding_idx=1)
(encoder): MBartEncoder(
(embed_tokens): Embedding(64001, 768, padding_idx=1)
(embed_positions): MBartLearnedPositionalEmbedding(1026, 768)
(layers): ModuleList(
(0-5): 6 x MBartEncoderLayer(
(self_attn): MBartAttention(
(k_proj): Linear(in_features=768, out_features=768, bias=True)
(v_proj): Linear(in_features=768, out_features=768, bias=True)
(q_proj): Linear(in_features=768, out_features=768, bias=True)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(activation_fn): GELUActivation()
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
)
(layernorm_embedding): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
(decoder): MBartDecoder(
(embed_tokens): Embedding(64001, 768, padding_idx=1)
(embed_positions): MBartLearnedPositionalEmbedding(1026, 768)
(layers): ModuleList(
(0-5): 6 x MBartDecoderLayer(
(self_attn): MBartAttention(
(k_proj): Linear(in_features=768, out_features=768, bias=True)
(v_proj): Linear(in_features=768, out_features=768, bias=True)
(q_proj): Linear(in_features=768, out_features=768, bias=True)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(activation_fn): GELUActivation()
(self_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(encoder_attn): MBartAttention(
(k_proj): Linear(in_features=768, out_features=768, bias=True)
(v_proj): Linear(in_features=768, out_features=768, bias=True)
(q_proj): Linear(in_features=768, out_features=768, bias=True)
(out_proj): Linear(in_features=768, out_features=768, bias=True)
)
(encoder_attn_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(final_layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
)
(layernorm_embedding): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
(layer_norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
)
)
(lm_head): Linear(in_features=768, out_features=64001, bias=False)
)
)
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