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10/11/2021 19:52:33 - INFO - __main__ - Namespace(adam_epsilon=1e-08, append_another_bos=True, checkpoint='', clean_up_spaces=False, dataset='webnlg', debug=False, do_lowercase=False, do_predict=False, do_pretrain=False, do_train=True, eval_period=200, gradient_accumulation_steps=1, knowledge_file='knowledge-full.json', learning_rate=1e-06, length_penalty=1.0, mask_prob='[0.4,0.2,0.2,0.4,0.2]', max_edge_length=60, max_grad_norm=1.0, max_input_length=256, max_node_length=50, max_output_length=128, model_name='bart', model_path='out/jointgt_bart_webnlg_local/run_9', no_lr_decay=False, num_beams=10, num_train_epochs=100.0, num_workers=8, output_dir='out/jointgt_bart_webnlg_local', pos_model_path='out/jointgt_bart_webnlg_local/run_9/pos', predict_batch_size=16, predict_file='data/webnlg/test', prefix='save', remove_bos=False, save_period=500, seed=42, task_ratio='[0.4,0.4,0.2]', tokenizer_path='pretrain_model/jointgt_bart', train_batch_size=32, train_file='data/webnlg/train', wait_step=30, warmup_proportion=0.01, warmup_steps=1000, weight_decay=0.0)
10/11/2021 19:52:33 - INFO - __main__ - out/jointgt_bart_webnlg_local
10/11/2021 19:52:33 - INFO - __main__ - Using 2 gpus
10/11/2021 19:52:33 - INFO - transformers.tokenization_utils_base - Model name 'pretrain_model/jointgt_bart' not found in model shortcut name list (facebook/bart-base, facebook/bart-large, facebook/bart-large-mnli, facebook/bart-large-cnn, facebook/bart-large-xsum, yjernite/bart_eli5). Assuming 'pretrain_model/jointgt_bart' is a path, a model identifier, or url to a directory containing tokenizer files.
10/11/2021 19:52:33 - INFO - transformers.tokenization_utils_base - Didn't find file pretrain_model/jointgt_bart/added_tokens.json. We won't load it.
10/11/2021 19:52:33 - INFO - transformers.tokenization_utils_base - Didn't find file pretrain_model/jointgt_bart/special_tokens_map.json. We won't load it.
10/11/2021 19:52:33 - INFO - transformers.tokenization_utils_base - Didn't find file pretrain_model/jointgt_bart/tokenizer_config.json. We won't load it.
10/11/2021 19:52:33 - INFO - transformers.tokenization_utils_base - Didn't find file pretrain_model/jointgt_bart/tokenizer.json. We won't load it.
10/11/2021 19:52:33 - INFO - transformers.tokenization_utils_base - loading file pretrain_model/jointgt_bart/vocab.json
10/11/2021 19:52:33 - INFO - transformers.tokenization_utils_base - loading file pretrain_model/jointgt_bart/merges.txt
10/11/2021 19:52:33 - INFO - transformers.tokenization_utils_base - loading file None
10/11/2021 19:52:33 - INFO - transformers.tokenization_utils_base - loading file None
10/11/2021 19:52:33 - INFO - transformers.tokenization_utils_base - loading file None
10/11/2021 19:52:33 - INFO - transformers.tokenization_utils_base - loading file None
10/11/2021 19:52:33 - INFO - transformers.configuration_utils - loading configuration file out/jointgt_bart_webnlg_local/run_9/config.json
10/11/2021 19:52:33 - INFO - transformers.configuration_utils - Model config BartConfig {
"activation_dropout": 0.1,
"activation_function": "gelu",
"add_bias_logits": false,
"add_final_layer_norm": false,
"architectures": [
"MyBartForConditionalGeneration"
],
"attention_dropout": 0.1,
"bos_token_id": 0,
"classif_dropout": 0.0,
"d_model": 768,
"decoder_attention_heads": 12,
"decoder_ffn_dim": 3072,
"decoder_layerdrop": 0.0,
"decoder_layers": 6,
"decoder_start_token_id": 2,
"dropout": 0.1,
"early_stopping": true,
"encoder_attention_heads": 12,
"encoder_ffn_dim": 3072,
"encoder_layerdrop": 0.0,
"encoder_layers": 6,
"eos_token_id": 2,
"extra_pos_embeddings": 2,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1",
"2": "LABEL_2"
},
"init_std": 0.02,
"is_encoder_decoder": true,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1,
"LABEL_2": 2
},
"max_position_embeddings": 1024,
"model_type": "bart",
"no_repeat_ngram_size": 3,
"normalize_before": false,
"normalize_embedding": true,
"num_beams": 4,
"num_hidden_layers": 6,
"pad_token_id": 1,
"scale_embedding": false,
"static_position_embeddings": false,
"task_specific_params": {
"summarization": {
"length_penalty": 1.0,
"max_length": 128,
"min_length": 12,
"num_beams": 4
},
"summarization_cnn": {
"length_penalty": 2.0,
"max_length": 142,
"min_length": 56,
"num_beams": 4
},
"summarization_xsum": {
"length_penalty": 1.0,
"max_length": 62,
"min_length": 11,
"num_beams": 6
}
},
"vocab_size": 50265
}
10/11/2021 19:52:33 - INFO - transformers.modeling_utils - loading weights file out/jointgt_bart_webnlg_local/run_9/pytorch_model.bin
10/11/2021 19:52:39 - INFO - transformers.modeling_utils - All model checkpoint weights were used when initializing MyBartForConditionalGeneration.
10/11/2021 19:52:39 - INFO - transformers.modeling_utils - All the weights of MyBartForConditionalGeneration were initialized from the model checkpoint at out/jointgt_bart_webnlg_local/run_9.
If your task is similar to the task the model of the ckeckpoint was trained on, you can already use MyBartForConditionalGeneration for predictions without further training.
10/11/2021 19:52:39 - INFO - transformers.configuration_utils - loading configuration file out/jointgt_bart_webnlg_local/run_9/pos/config.json
10/11/2021 19:52:39 - INFO - transformers.configuration_utils - Model config BartConfig {
"activation_dropout": 0.1,
"activation_function": "gelu",
"add_bias_logits": false,
"add_final_layer_norm": false,
"architectures": [
"BartForConditionalGeneration"
],
"attention_dropout": 0.1,
"bos_token_id": 0,
"classif_dropout": 0.0,
"d_model": 768,
"decoder_attention_heads": 12,
"decoder_ffn_dim": 3072,
"decoder_layerdrop": 0.0,
"decoder_layers": 6,
"decoder_start_token_id": 2,
"dropout": 0.1,
"early_stopping": true,
"encoder_attention_heads": 12,
"encoder_ffn_dim": 3072,
"encoder_layerdrop": 0.0,
"encoder_layers": 6,
"eos_token_id": 2,
"extra_pos_embeddings": 2,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1",
"2": "LABEL_2"
},
"init_std": 0.02,
"is_encoder_decoder": true,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1,
"LABEL_2": 2
},
"max_position_embeddings": 1024,
"model_type": "bart",
"no_repeat_ngram_size": 3,
"normalize_before": false,
"normalize_embedding": true,
"num_beams": 4,
"num_hidden_layers": 6,
"pad_token_id": 1,
"scale_embedding": false,
"static_position_embeddings": false,
"task_specific_params": {
"summarization": {
"length_penalty": 1.0,
"max_length": 128,
"min_length": 12,
"num_beams": 4
},
"summarization_cnn": {
"length_penalty": 2.0,
"max_length": 142,
"min_length": 56,
"num_beams": 4
},
"summarization_xsum": {
"length_penalty": 1.0,
"max_length": 62,
"min_length": 11,
"num_beams": 6
}
},
"vocab_size": 50265
}
10/11/2021 19:52:39 - INFO - transformers.modeling_utils - loading weights file out/jointgt_bart_webnlg_local/run_9/pos/pytorch_model.bin
10/11/2021 19:52:42 - INFO - transformers.modeling_utils - All model checkpoint weights were used when initializing BartForConditionalGeneration.
10/11/2021 19:52:42 - INFO - transformers.modeling_utils - All the weights of BartForConditionalGeneration were initialized from the model checkpoint at out/jointgt_bart_webnlg_local/run_9/pos.
If your task is similar to the task the model of the ckeckpoint was trained on, you can already use BartForConditionalGeneration for predictions without further training.
10/11/2021 19:52:46 - INFO - __main__ - Namespace(adam_epsilon=1e-08, append_another_bos=True, checkpoint='', clean_up_spaces=False, dataset='webnlg', debug=False, do_lowercase=False, do_predict=False, do_pretrain=False, do_train=True, eval_period=200, gradient_accumulation_steps=1, knowledge_file='knowledge-full.json', learning_rate=1e-06, length_penalty=1.0, mask_prob='[0.4,0.2,0.2,0.4,0.2]', max_edge_length=60, max_grad_norm=1.0, max_input_length=256, max_node_length=50, max_output_length=128, model_name='bart', model_path='out/jointgt_bart_webnlg_local/run_9', no_lr_decay=False, num_beams=10, num_train_epochs=100.0, num_workers=8, output_dir='out/jointgt_bart_webnlg_local', pos_model_path='out/jointgt_bart_webnlg_local/run_9/pos', predict_batch_size=16, predict_file='data/webnlg/test', prefix='save', remove_bos=False, save_period=500, seed=42, task_ratio='[0.4,0.4,0.2]', tokenizer_path='pretrain_model/jointgt_bart', train_batch_size=32, train_file='data/webnlg/train', wait_step=30, warmup_proportion=0.01, warmup_steps=1000, weight_decay=0.0)
10/11/2021 19:52:46 - INFO - __main__ - out/jointgt_bart_webnlg_local
10/11/2021 19:52:46 - INFO - __main__ - Using 2 gpus
10/11/2021 19:52:46 - INFO - transformers.tokenization_utils_base - Model name 'pretrain_model/jointgt_bart' not found in model shortcut name list (facebook/bart-base, facebook/bart-large, facebook/bart-large-mnli, facebook/bart-large-cnn, facebook/bart-large-xsum, yjernite/bart_eli5). Assuming 'pretrain_model/jointgt_bart' is a path, a model identifier, or url to a directory containing tokenizer files.
10/11/2021 19:52:46 - INFO - transformers.tokenization_utils_base - Didn't find file pretrain_model/jointgt_bart/added_tokens.json. We won't load it.
10/11/2021 19:52:46 - INFO - transformers.tokenization_utils_base - Didn't find file pretrain_model/jointgt_bart/special_tokens_map.json. We won't load it.
10/11/2021 19:52:46 - INFO - transformers.tokenization_utils_base - Didn't find file pretrain_model/jointgt_bart/tokenizer_config.json. We won't load it.
10/11/2021 19:52:46 - INFO - transformers.tokenization_utils_base - Didn't find file pretrain_model/jointgt_bart/tokenizer.json. We won't load it.
10/11/2021 19:52:46 - INFO - transformers.tokenization_utils_base - loading file pretrain_model/jointgt_bart/vocab.json
10/11/2021 19:52:46 - INFO - transformers.tokenization_utils_base - loading file pretrain_model/jointgt_bart/merges.txt
10/11/2021 19:52:46 - INFO - transformers.tokenization_utils_base - loading file None
10/11/2021 19:52:46 - INFO - transformers.tokenization_utils_base - loading file None
10/11/2021 19:52:46 - INFO - transformers.tokenization_utils_base - loading file None
10/11/2021 19:52:46 - INFO - transformers.tokenization_utils_base - loading file None
10/11/2021 19:52:46 - INFO - transformers.configuration_utils - loading configuration file out/jointgt_bart_webnlg_local/run_9/config.json
10/11/2021 19:52:46 - INFO - transformers.configuration_utils - Model config BartConfig {
"activation_dropout": 0.1,
"activation_function": "gelu",
"add_bias_logits": false,
"add_final_layer_norm": false,
"architectures": [
"MyBartForConditionalGeneration"
],
"attention_dropout": 0.1,
"bos_token_id": 0,
"classif_dropout": 0.0,
"d_model": 768,
"decoder_attention_heads": 12,
"decoder_ffn_dim": 3072,
"decoder_layerdrop": 0.0,
"decoder_layers": 6,
"decoder_start_token_id": 2,
"dropout": 0.1,
"early_stopping": true,
"encoder_attention_heads": 12,
"encoder_ffn_dim": 3072,
"encoder_layerdrop": 0.0,
"encoder_layers": 6,
"eos_token_id": 2,
"extra_pos_embeddings": 2,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1",
"2": "LABEL_2"
},
"init_std": 0.02,
"is_encoder_decoder": true,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1,
"LABEL_2": 2
},
"max_position_embeddings": 1024,
"model_type": "bart",
"no_repeat_ngram_size": 3,
"normalize_before": false,
"normalize_embedding": true,
"num_beams": 4,
"num_hidden_layers": 6,
"pad_token_id": 1,
"scale_embedding": false,
"static_position_embeddings": false,
"task_specific_params": {
"summarization": {
"length_penalty": 1.0,
"max_length": 128,
"min_length": 12,
"num_beams": 4
},
"summarization_cnn": {
"length_penalty": 2.0,
"max_length": 142,
"min_length": 56,
"num_beams": 4
},
"summarization_xsum": {
"length_penalty": 1.0,
"max_length": 62,
"min_length": 11,
"num_beams": 6
}
},
"vocab_size": 50265
}
10/11/2021 19:52:46 - INFO - transformers.modeling_utils - loading weights file out/jointgt_bart_webnlg_local/run_9/pytorch_model.bin
10/11/2021 19:52:53 - INFO - transformers.modeling_utils - All model checkpoint weights were used when initializing MyBartForConditionalGeneration.
10/11/2021 19:52:53 - INFO - transformers.modeling_utils - All the weights of MyBartForConditionalGeneration were initialized from the model checkpoint at out/jointgt_bart_webnlg_local/run_9.
If your task is similar to the task the model of the ckeckpoint was trained on, you can already use MyBartForConditionalGeneration for predictions without further training.
10/11/2021 19:52:53 - INFO - transformers.configuration_utils - loading configuration file out/jointgt_bart_webnlg_local/run_9/pos/config.json
10/11/2021 19:52:53 - INFO - transformers.configuration_utils - Model config BartConfig {
"activation_dropout": 0.1,
"activation_function": "gelu",
"add_bias_logits": false,
"add_final_layer_norm": false,
"architectures": [
"BartForConditionalGeneration"
],
"attention_dropout": 0.1,
"bos_token_id": 0,
"classif_dropout": 0.0,
"d_model": 768,
"decoder_attention_heads": 12,
"decoder_ffn_dim": 3072,
"decoder_layerdrop": 0.0,
"decoder_layers": 6,
"decoder_start_token_id": 2,
"dropout": 0.1,
"early_stopping": true,
"encoder_attention_heads": 12,
"encoder_ffn_dim": 3072,
"encoder_layerdrop": 0.0,
"encoder_layers": 6,
"eos_token_id": 2,
"extra_pos_embeddings": 2,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1",
"2": "LABEL_2"
},
"init_std": 0.02,
"is_encoder_decoder": true,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1,
"LABEL_2": 2
},
"max_position_embeddings": 1024,
"model_type": "bart",
"no_repeat_ngram_size": 3,
"normalize_before": false,
"normalize_embedding": true,
"num_beams": 4,
"num_hidden_layers": 6,
"pad_token_id": 1,
"scale_embedding": false,
"static_position_embeddings": false,
"task_specific_params": {
"summarization": {
"length_penalty": 1.0,
"max_length": 128,
"min_length": 12,
"num_beams": 4
},
"summarization_cnn": {
"length_penalty": 2.0,
"max_length": 142,
"min_length": 56,
"num_beams": 4
},
"summarization_xsum": {
"length_penalty": 1.0,
"max_length": 62,
"min_length": 11,
"num_beams": 6
}
},
"vocab_size": 50265
}
10/11/2021 19:52:53 - INFO - transformers.modeling_utils - loading weights file out/jointgt_bart_webnlg_local/run_9/pos/pytorch_model.bin
10/11/2021 19:52:55 - INFO - transformers.modeling_utils - All model checkpoint weights were used when initializing BartForConditionalGeneration.
10/11/2021 19:52:55 - INFO - transformers.modeling_utils - All the weights of BartForConditionalGeneration were initialized from the model checkpoint at out/jointgt_bart_webnlg_local/run_9/pos.
If your task is similar to the task the model of the ckeckpoint was trained on, you can already use BartForConditionalGeneration for predictions without further training.
10/11/2021 19:52:57 - INFO - __main__ - Starting training!
10/11/2021 19:54:20 - INFO - __main__ - Namespace(adam_epsilon=1e-08, append_another_bos=True, checkpoint='', clean_up_spaces=False, dataset='webnlg', debug=False, do_lowercase=False, do_predict=False, do_pretrain=False, do_train=True, eval_period=200, gradient_accumulation_steps=1, knowledge_file='knowledge-full.json', learning_rate=1e-06, length_penalty=1.0, mask_prob='[0.4,0.2,0.2,0.4,0.2]', max_edge_length=60, max_grad_norm=1.0, max_input_length=256, max_node_length=50, max_output_length=128, model_name='bart', model_path='out/jointgt_bart_webnlg_local/run_9', no_lr_decay=False, num_beams=10, num_train_epochs=100.0, num_workers=8, output_dir='out/jointgt_bart_webnlg_local', pos_model_path='out/jointgt_bart_webnlg_local/run_9/pos', predict_batch_size=16, predict_file='data/webnlg/test', prefix='save', remove_bos=False, save_period=500, seed=42, task_ratio='[0.4,0.4,0.2]', tokenizer_path='pretrain_model/jointgt_bart', train_batch_size=32, train_file='data/webnlg/train', wait_step=30, warmup_proportion=0.01, warmup_steps=1000, weight_decay=0.0)
10/11/2021 19:54:20 - INFO - __main__ - out/jointgt_bart_webnlg_local
10/11/2021 19:54:20 - INFO - __main__ - Using 2 gpus
10/11/2021 19:54:20 - INFO - transformers.tokenization_utils_base - Model name 'pretrain_model/jointgt_bart' not found in model shortcut name list (facebook/bart-base, facebook/bart-large, facebook/bart-large-mnli, facebook/bart-large-cnn, facebook/bart-large-xsum, yjernite/bart_eli5). Assuming 'pretrain_model/jointgt_bart' is a path, a model identifier, or url to a directory containing tokenizer files.
10/11/2021 19:54:20 - INFO - transformers.tokenization_utils_base - Didn't find file pretrain_model/jointgt_bart/added_tokens.json. We won't load it.
10/11/2021 19:54:20 - INFO - transformers.tokenization_utils_base - Didn't find file pretrain_model/jointgt_bart/special_tokens_map.json. We won't load it.
10/11/2021 19:54:20 - INFO - transformers.tokenization_utils_base - Didn't find file pretrain_model/jointgt_bart/tokenizer_config.json. We won't load it.
10/11/2021 19:54:20 - INFO - transformers.tokenization_utils_base - Didn't find file pretrain_model/jointgt_bart/tokenizer.json. We won't load it.
10/11/2021 19:54:20 - INFO - transformers.tokenization_utils_base - loading file pretrain_model/jointgt_bart/vocab.json
10/11/2021 19:54:20 - INFO - transformers.tokenization_utils_base - loading file pretrain_model/jointgt_bart/merges.txt
10/11/2021 19:54:20 - INFO - transformers.tokenization_utils_base - loading file None
10/11/2021 19:54:20 - INFO - transformers.tokenization_utils_base - loading file None
10/11/2021 19:54:20 - INFO - transformers.tokenization_utils_base - loading file None
10/11/2021 19:54:20 - INFO - transformers.tokenization_utils_base - loading file None
10/11/2021 19:54:20 - INFO - transformers.configuration_utils - loading configuration file out/jointgt_bart_webnlg_local/run_9/config.json
10/11/2021 19:54:20 - INFO - transformers.configuration_utils - Model config BartConfig {
"activation_dropout": 0.1,
"activation_function": "gelu",
"add_bias_logits": false,
"add_final_layer_norm": false,
"architectures": [
"MyBartForConditionalGeneration"
],
"attention_dropout": 0.1,
"bos_token_id": 0,
"classif_dropout": 0.0,
"d_model": 768,
"decoder_attention_heads": 12,
"decoder_ffn_dim": 3072,
"decoder_layerdrop": 0.0,
"decoder_layers": 6,
"decoder_start_token_id": 2,
"dropout": 0.1,
"early_stopping": true,
"encoder_attention_heads": 12,
"encoder_ffn_dim": 3072,
"encoder_layerdrop": 0.0,
"encoder_layers": 6,
"eos_token_id": 2,
"extra_pos_embeddings": 2,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1",
"2": "LABEL_2"
},
"init_std": 0.02,
"is_encoder_decoder": true,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1,
"LABEL_2": 2
},
"max_position_embeddings": 1024,
"model_type": "bart",
"no_repeat_ngram_size": 3,
"normalize_before": false,
"normalize_embedding": true,
"num_beams": 4,
"num_hidden_layers": 6,
"pad_token_id": 1,
"scale_embedding": false,
"static_position_embeddings": false,
"task_specific_params": {
"summarization": {
"length_penalty": 1.0,
"max_length": 128,
"min_length": 12,
"num_beams": 4
},
"summarization_cnn": {
"length_penalty": 2.0,
"max_length": 142,
"min_length": 56,
"num_beams": 4
},
"summarization_xsum": {
"length_penalty": 1.0,
"max_length": 62,
"min_length": 11,
"num_beams": 6
}
},
"vocab_size": 50265
}
10/11/2021 19:54:20 - INFO - transformers.modeling_utils - loading weights file out/jointgt_bart_webnlg_local/run_9/pytorch_model.bin
10/11/2021 19:54:27 - INFO - transformers.modeling_utils - All model checkpoint weights were used when initializing MyBartForConditionalGeneration.
10/11/2021 19:54:27 - INFO - transformers.modeling_utils - All the weights of MyBartForConditionalGeneration were initialized from the model checkpoint at out/jointgt_bart_webnlg_local/run_9.
If your task is similar to the task the model of the ckeckpoint was trained on, you can already use MyBartForConditionalGeneration for predictions without further training.
10/11/2021 19:54:27 - INFO - transformers.configuration_utils - loading configuration file out/jointgt_bart_webnlg_local/run_9/pos/config.json
10/11/2021 19:54:27 - INFO - transformers.configuration_utils - Model config BartConfig {
"activation_dropout": 0.1,
"activation_function": "gelu",
"add_bias_logits": false,
"add_final_layer_norm": false,
"architectures": [
"BartForConditionalGeneration"
],
"attention_dropout": 0.1,
"bos_token_id": 0,
"classif_dropout": 0.0,
"d_model": 768,
"decoder_attention_heads": 12,
"decoder_ffn_dim": 3072,
"decoder_layerdrop": 0.0,
"decoder_layers": 6,
"decoder_start_token_id": 2,
"dropout": 0.1,
"early_stopping": true,
"encoder_attention_heads": 12,
"encoder_ffn_dim": 3072,
"encoder_layerdrop": 0.0,
"encoder_layers": 6,
"eos_token_id": 2,
"extra_pos_embeddings": 2,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1",
"2": "LABEL_2"
},
"init_std": 0.02,
"is_encoder_decoder": true,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1,
"LABEL_2": 2
},
"max_position_embeddings": 1024,
"model_type": "bart",
"no_repeat_ngram_size": 3,
"normalize_before": false,
"normalize_embedding": true,
"num_beams": 4,
"num_hidden_layers": 6,
"pad_token_id": 1,
"scale_embedding": false,
"static_position_embeddings": false,
"task_specific_params": {
"summarization": {
"length_penalty": 1.0,
"max_length": 128,
"min_length": 12,
"num_beams": 4
},
"summarization_cnn": {
"length_penalty": 2.0,
"max_length": 142,
"min_length": 56,
"num_beams": 4
},
"summarization_xsum": {
"length_penalty": 1.0,
"max_length": 62,
"min_length": 11,
"num_beams": 6
}
},
"vocab_size": 50265
}
10/11/2021 19:54:27 - INFO - transformers.modeling_utils - loading weights file out/jointgt_bart_webnlg_local/run_9/pos/pytorch_model.bin
10/11/2021 19:54:30 - INFO - transformers.modeling_utils - All model checkpoint weights were used when initializing BartForConditionalGeneration.
10/11/2021 19:54:30 - INFO - transformers.modeling_utils - All the weights of BartForConditionalGeneration were initialized from the model checkpoint at out/jointgt_bart_webnlg_local/run_9/pos.
If your task is similar to the task the model of the ckeckpoint was trained on, you can already use BartForConditionalGeneration for predictions without further training.
10/11/2021 19:54:33 - INFO - __main__ - Starting training!
10/11/2021 20:02:47 - INFO - __main__ - Saved prediction in out/jointgt_bart_webnlg_local/savepredictions_0_200.txt
10/11/2021 20:02:49 - INFO - __main__ - 当前的评估值: bleu-4:8.817759648266229, ROUGE_L:31.97016790986223, CIDEr:0.028117769756660103, Chrf:0.2475941043909518
10/11/2021 20:02:49 - INFO - __main__ - Step 200 Train loss 7.62 Learning rate 2.00e-06 BLEU 881.78% on epoch=0
10/11/2021 20:02:49 - INFO - __main__ - 当前最好的：-1.00
10/11/2021 20:02:49 - INFO - transformers.configuration_utils - Configuration saved in out/jointgt_bart_webnlg_local/run_0/config.json
10/11/2021 20:02:51 - INFO - transformers.modeling_utils - Model weights saved in out/jointgt_bart_webnlg_local/run_0/pytorch_model.bin
10/11/2021 20:02:51 - INFO - transformers.configuration_utils - Configuration saved in out/jointgt_bart_webnlg_local/run_0/pos/config.json
10/11/2021 20:02:52 - INFO - transformers.modeling_utils - Model weights saved in out/jointgt_bart_webnlg_local/run_0/pos/pytorch_model.bin
10/11/2021 20:02:52 - INFO - __main__ - Saving model with best BLEU: -100.00% -> 881.78% on epoch=0, global_step=200
10/11/2021 20:08:15 - INFO - __main__ - Saved prediction in out/jointgt_bart_webnlg_local/savepredictions_1_400.txt
10/11/2021 20:08:17 - INFO - __main__ - 当前的评估值: bleu-4:2.9235352556827654, ROUGE_L:31.948581557005294, CIDEr:0.007269921106825235, Chrf:0.2339334410959491
10/11/2021 20:08:17 - INFO - __main__ - Step 400 Train loss 4.62 Learning rate 4.00e-06 BLEU 292.35% on epoch=1
10/11/2021 20:08:17 - INFO - __main__ - 当前最好的：8.82
10/11/2021 20:13:51 - INFO - __main__ - Saved prediction in out/jointgt_bart_webnlg_local/savepredictions_2_600.txt
10/11/2021 20:13:52 - INFO - __main__ - 当前的评估值: bleu-4:3.7415378613673598, ROUGE_L:31.95397907513957, CIDEr:0.0063936244566629675, Chrf:0.23456997253570058
10/11/2021 20:13:52 - INFO - __main__ - Step 600 Train loss 3.94 Learning rate 6.00e-06 BLEU 374.15% on epoch=1
10/11/2021 20:13:52 - INFO - __main__ - 当前最好的：8.82
10/11/2021 20:24:16 - INFO - __main__ - Saved prediction in out/jointgt_bart_webnlg_local/savepredictions_3_800.txt
10/11/2021 20:24:19 - INFO - __main__ - 当前的评估值: bleu-4:10.429631604965714, ROUGE_L:30.721522346737945, CIDEr:0.017875324881684147, Chrf:0.2660374181944362
10/11/2021 20:24:19 - INFO - __main__ - Step 800 Train loss 3.60 Learning rate 8.00e-06 BLEU 1042.96% on epoch=2
10/11/2021 20:24:19 - INFO - __main__ - 当前最好的：8.82
10/11/2021 20:24:19 - INFO - transformers.configuration_utils - Configuration saved in out/jointgt_bart_webnlg_local/run_3/config.json
10/11/2021 20:24:20 - INFO - transformers.modeling_utils - Model weights saved in out/jointgt_bart_webnlg_local/run_3/pytorch_model.bin
10/11/2021 20:24:20 - INFO - transformers.configuration_utils - Configuration saved in out/jointgt_bart_webnlg_local/run_3/pos/config.json
10/11/2021 20:24:21 - INFO - transformers.modeling_utils - Model weights saved in out/jointgt_bart_webnlg_local/run_3/pos/pytorch_model.bin
10/11/2021 20:24:21 - INFO - __main__ - Saving model with best BLEU: 881.78% -> 1042.96% on epoch=2, global_step=800
10/11/2021 20:37:41 - INFO - __main__ - Saved prediction in out/jointgt_bart_webnlg_local/savepredictions_4_1000.txt
10/11/2021 20:37:46 - INFO - __main__ - 当前的评估值: bleu-4:4.968528428714735, ROUGE_L:24.82608154693004, CIDEr:0.01072990377119645, Chrf:0.28137514986733364
10/11/2021 20:37:46 - INFO - __main__ - Step 1000 Train loss 3.32 Learning rate 1.00e-05 BLEU 496.85% on epoch=2
10/11/2021 20:37:46 - INFO - __main__ - 当前最好的：10.43
10/11/2021 20:50:48 - INFO - __main__ - Saved prediction in out/jointgt_bart_webnlg_local/savepredictions_5_1200.txt
10/11/2021 20:50:54 - INFO - __main__ - 当前的评估值: bleu-4:5.213296382929742, ROUGE_L:24.71773525751841, CIDEr:0.01223839863126114, Chrf:0.2784353047992718
10/11/2021 20:50:54 - INFO - __main__ - Step 1200 Train loss 3.10 Learning rate 9.94e-06 BLEU 521.33% on epoch=3
10/11/2021 20:50:54 - INFO - __main__ - 当前最好的：10.43
10/11/2021 21:04:14 - INFO - __main__ - Saved prediction in out/jointgt_bart_webnlg_local/savepredictions_6_1400.txt
10/11/2021 21:04:19 - INFO - __main__ - 当前的评估值: bleu-4:5.056468209140468, ROUGE_L:24.28844051024223, CIDEr:0.010967298337375626, Chrf:0.2735400177593141
10/11/2021 21:04:19 - INFO - __main__ - Step 1400 Train loss 2.95 Learning rate 9.88e-06 BLEU 505.65% on epoch=3
10/11/2021 21:04:19 - INFO - __main__ - 当前最好的：10.43
10/11/2021 21:05:00 - INFO - __main__ - Namespace(adam_epsilon=1e-08, append_another_bos=True, checkpoint='', clean_up_spaces=False, dataset='webnlg', debug=False, do_lowercase=False, do_predict=False, do_pretrain=False, do_train=True, eval_period=800, gradient_accumulation_steps=1, knowledge_file='knowledge-full.json', learning_rate=1e-06, length_penalty=1.0, mask_prob='[0.4,0.2,0.2,0.4,0.2]', max_edge_length=60, max_grad_norm=1.0, max_input_length=256, max_node_length=50, max_output_length=128, model_name='bart', model_path='out/jointgt_bart_webnlg_local/run_9', no_lr_decay=False, num_beams=10, num_train_epochs=100.0, num_workers=8, output_dir='out/jointgt_bart_webnlg_local', pos_model_path='out/jointgt_bart_webnlg_local/run_9/pos', predict_batch_size=16, predict_file='data/webnlg/test', prefix='save', remove_bos=False, save_period=500, seed=42, task_ratio='[0.4,0.4,0.2]', tokenizer_path='pretrain_model/jointgt_bart', train_batch_size=32, train_file='data/webnlg/train', wait_step=30, warmup_proportion=0.01, warmup_steps=2000, weight_decay=0.0)
10/11/2021 21:05:00 - INFO - __main__ - out/jointgt_bart_webnlg_local
10/11/2021 21:05:00 - INFO - __main__ - Using 2 gpus
10/11/2021 21:05:00 - INFO - transformers.tokenization_utils_base - Model name 'pretrain_model/jointgt_bart' not found in model shortcut name list (facebook/bart-base, facebook/bart-large, facebook/bart-large-mnli, facebook/bart-large-cnn, facebook/bart-large-xsum, yjernite/bart_eli5). Assuming 'pretrain_model/jointgt_bart' is a path, a model identifier, or url to a directory containing tokenizer files.
10/11/2021 21:05:00 - INFO - transformers.tokenization_utils_base - Didn't find file pretrain_model/jointgt_bart/added_tokens.json. We won't load it.
10/11/2021 21:05:00 - INFO - transformers.tokenization_utils_base - Didn't find file pretrain_model/jointgt_bart/special_tokens_map.json. We won't load it.
10/11/2021 21:05:00 - INFO - transformers.tokenization_utils_base - Didn't find file pretrain_model/jointgt_bart/tokenizer_config.json. We won't load it.
10/11/2021 21:05:00 - INFO - transformers.tokenization_utils_base - Didn't find file pretrain_model/jointgt_bart/tokenizer.json. We won't load it.
10/11/2021 21:05:00 - INFO - transformers.tokenization_utils_base - loading file pretrain_model/jointgt_bart/vocab.json
10/11/2021 21:05:00 - INFO - transformers.tokenization_utils_base - loading file pretrain_model/jointgt_bart/merges.txt
10/11/2021 21:05:00 - INFO - transformers.tokenization_utils_base - loading file None
10/11/2021 21:05:00 - INFO - transformers.tokenization_utils_base - loading file None
10/11/2021 21:05:00 - INFO - transformers.tokenization_utils_base - loading file None
10/11/2021 21:05:00 - INFO - transformers.tokenization_utils_base - loading file None
10/11/2021 21:05:00 - INFO - transformers.configuration_utils - loading configuration file out/jointgt_bart_webnlg_local/run_9/config.json
10/11/2021 21:05:00 - INFO - transformers.configuration_utils - Model config BartConfig {
"activation_dropout": 0.1,
"activation_function": "gelu",
"add_bias_logits": false,
"add_final_layer_norm": false,
"architectures": [
"MyBartForConditionalGeneration"
],
"attention_dropout": 0.1,
"bos_token_id": 0,
"classif_dropout": 0.0,
"d_model": 768,
"decoder_attention_heads": 12,
"decoder_ffn_dim": 3072,
"decoder_layerdrop": 0.0,
"decoder_layers": 6,
"decoder_start_token_id": 2,
"dropout": 0.1,
"early_stopping": true,
"encoder_attention_heads": 12,
"encoder_ffn_dim": 3072,
"encoder_layerdrop": 0.0,
"encoder_layers": 6,
"eos_token_id": 2,
"extra_pos_embeddings": 2,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1",
"2": "LABEL_2"
},
"init_std": 0.02,
"is_encoder_decoder": true,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1,
"LABEL_2": 2
},
"max_position_embeddings": 1024,
"model_type": "bart",
"no_repeat_ngram_size": 3,
"normalize_before": false,
"normalize_embedding": true,
"num_beams": 4,
"num_hidden_layers": 6,
"pad_token_id": 1,
"scale_embedding": false,
"static_position_embeddings": false,
"task_specific_params": {
"summarization": {
"length_penalty": 1.0,
"max_length": 128,
"min_length": 12,
"num_beams": 4
},
"summarization_cnn": {
"length_penalty": 2.0,
"max_length": 142,
"min_length": 56,
"num_beams": 4
},
"summarization_xsum": {
"length_penalty": 1.0,
"max_length": 62,
"min_length": 11,
"num_beams": 6
}
},
"vocab_size": 50265
}
10/11/2021 21:05:00 - INFO - transformers.modeling_utils - loading weights file out/jointgt_bart_webnlg_local/run_9/pytorch_model.bin
10/11/2021 21:05:09 - INFO - transformers.modeling_utils - All model checkpoint weights were used when initializing MyBartForConditionalGeneration.
10/11/2021 21:05:09 - INFO - transformers.modeling_utils - All the weights of MyBartForConditionalGeneration were initialized from the model checkpoint at out/jointgt_bart_webnlg_local/run_9.
If your task is similar to the task the model of the ckeckpoint was trained on, you can already use MyBartForConditionalGeneration for predictions without further training.
10/11/2021 21:05:09 - INFO - transformers.configuration_utils - loading configuration file out/jointgt_bart_webnlg_local/run_9/pos/config.json
10/11/2021 21:05:09 - INFO - transformers.configuration_utils - Model config BartConfig {
"activation_dropout": 0.1,
"activation_function": "gelu",
"add_bias_logits": false,
"add_final_layer_norm": false,
"architectures": [
"BartForConditionalGeneration"
],
"attention_dropout": 0.1,
"bos_token_id": 0,
"classif_dropout": 0.0,
"d_model": 768,
"decoder_attention_heads": 12,
"decoder_ffn_dim": 3072,
"decoder_layerdrop": 0.0,
"decoder_layers": 6,
"decoder_start_token_id": 2,
"dropout": 0.1,
"early_stopping": true,
"encoder_attention_heads": 12,
"encoder_ffn_dim": 3072,
"encoder_layerdrop": 0.0,
"encoder_layers": 6,
"eos_token_id": 2,
"extra_pos_embeddings": 2,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1",
"2": "LABEL_2"
},
"init_std": 0.02,
"is_encoder_decoder": true,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1,
"LABEL_2": 2
},
"max_position_embeddings": 1024,
"model_type": "bart",
"no_repeat_ngram_size": 3,
"normalize_before": false,
"normalize_embedding": true,
"num_beams": 4,
"num_hidden_layers": 6,
"pad_token_id": 1,
"scale_embedding": false,
"static_position_embeddings": false,
"task_specific_params": {
"summarization": {
"length_penalty": 1.0,
"max_length": 128,
"min_length": 12,
"num_beams": 4
},
"summarization_cnn": {
"length_penalty": 2.0,
"max_length": 142,
"min_length": 56,
"num_beams": 4
},
"summarization_xsum": {
"length_penalty": 1.0,
"max_length": 62,
"min_length": 11,
"num_beams": 6
}
},
"vocab_size": 50265
}
10/11/2021 21:05:09 - INFO - transformers.modeling_utils - loading weights file out/jointgt_bart_webnlg_local/run_9/pos/pytorch_model.bin
10/11/2021 21:05:13 - INFO - __main__ - Namespace(adam_epsilon=1e-08, append_another_bos=True, checkpoint='', clean_up_spaces=False, dataset='webnlg', debug=False, do_lowercase=False, do_predict=False, do_pretrain=False, do_train=True, eval_period=800, gradient_accumulation_steps=1, knowledge_file='knowledge-full.json', learning_rate=1e-06, length_penalty=1.0, mask_prob='[0.4,0.2,0.2,0.4,0.2]', max_edge_length=60, max_grad_norm=1.0, max_input_length=256, max_node_length=50, max_output_length=128, model_name='bart', model_path='out/jointgt_bart_webnlg_local/run_9', no_lr_decay=False, num_beams=10, num_train_epochs=100.0, num_workers=8, output_dir='out/jointgt_bart_webnlg_local', pos_model_path='out/jointgt_bart_webnlg_local/run_9/pos', predict_batch_size=16, predict_file='data/webnlg/test', prefix='save', remove_bos=False, save_period=500, seed=42, task_ratio='[0.4,0.4,0.2]', tokenizer_path='pretrain_model/jointgt_bart', train_batch_size=32, train_file='data/webnlg/train', wait_step=30, warmup_proportion=0.01, warmup_steps=2000, weight_decay=0.0)
10/11/2021 21:05:13 - INFO - __main__ - out/jointgt_bart_webnlg_local
10/11/2021 21:05:13 - INFO - __main__ - Using 2 gpus
10/11/2021 21:05:13 - INFO - transformers.tokenization_utils_base - Model name 'pretrain_model/jointgt_bart' not found in model shortcut name list (facebook/bart-base, facebook/bart-large, facebook/bart-large-mnli, facebook/bart-large-cnn, facebook/bart-large-xsum, yjernite/bart_eli5). Assuming 'pretrain_model/jointgt_bart' is a path, a model identifier, or url to a directory containing tokenizer files.
10/11/2021 21:05:13 - INFO - transformers.tokenization_utils_base - Didn't find file pretrain_model/jointgt_bart/added_tokens.json. We won't load it.
10/11/2021 21:05:13 - INFO - transformers.tokenization_utils_base - Didn't find file pretrain_model/jointgt_bart/special_tokens_map.json. We won't load it.
10/11/2021 21:05:13 - INFO - transformers.tokenization_utils_base - Didn't find file pretrain_model/jointgt_bart/tokenizer_config.json. We won't load it.
10/11/2021 21:05:13 - INFO - transformers.tokenization_utils_base - Didn't find file pretrain_model/jointgt_bart/tokenizer.json. We won't load it.
10/11/2021 21:05:13 - INFO - transformers.tokenization_utils_base - loading file pretrain_model/jointgt_bart/vocab.json
10/11/2021 21:05:13 - INFO - transformers.tokenization_utils_base - loading file pretrain_model/jointgt_bart/merges.txt
10/11/2021 21:05:13 - INFO - transformers.tokenization_utils_base - loading file None
10/11/2021 21:05:13 - INFO - transformers.tokenization_utils_base - loading file None
10/11/2021 21:05:13 - INFO - transformers.tokenization_utils_base - loading file None
10/11/2021 21:05:13 - INFO - transformers.tokenization_utils_base - loading file None
10/11/2021 21:05:13 - INFO - transformers.configuration_utils - loading configuration file out/jointgt_bart_webnlg_local/run_9/config.json
10/11/2021 21:05:13 - INFO - transformers.configuration_utils - Model config BartConfig {
"activation_dropout": 0.1,
"activation_function": "gelu",
"add_bias_logits": false,
"add_final_layer_norm": false,
"architectures": [
"MyBartForConditionalGeneration"
],
"attention_dropout": 0.1,
"bos_token_id": 0,
"classif_dropout": 0.0,
"d_model": 768,
"decoder_attention_heads": 12,
"decoder_ffn_dim": 3072,
"decoder_layerdrop": 0.0,
"decoder_layers": 6,
"decoder_start_token_id": 2,
"dropout": 0.1,
"early_stopping": true,
"encoder_attention_heads": 12,
"encoder_ffn_dim": 3072,
"encoder_layerdrop": 0.0,
"encoder_layers": 6,
"eos_token_id": 2,
"extra_pos_embeddings": 2,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1",
"2": "LABEL_2"
},
"init_std": 0.02,
"is_encoder_decoder": true,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1,
"LABEL_2": 2
},
"max_position_embeddings": 1024,
"model_type": "bart",
"no_repeat_ngram_size": 3,
"normalize_before": false,
"normalize_embedding": true,
"num_beams": 4,
"num_hidden_layers": 6,
"pad_token_id": 1,
"scale_embedding": false,
"static_position_embeddings": false,
"task_specific_params": {
"summarization": {
"length_penalty": 1.0,
"max_length": 128,
"min_length": 12,
"num_beams": 4
},
"summarization_cnn": {
"length_penalty": 2.0,
"max_length": 142,
"min_length": 56,
"num_beams": 4
},
"summarization_xsum": {
"length_penalty": 1.0,
"max_length": 62,
"min_length": 11,
"num_beams": 6
}
},
"vocab_size": 50265
}
10/11/2021 21:05:13 - INFO - transformers.modeling_utils - loading weights file out/jointgt_bart_webnlg_local/run_9/pytorch_model.bin
10/11/2021 21:05:21 - INFO - transformers.modeling_utils - All model checkpoint weights were used when initializing MyBartForConditionalGeneration.
10/11/2021 21:05:21 - INFO - transformers.modeling_utils - All the weights of MyBartForConditionalGeneration were initialized from the model checkpoint at out/jointgt_bart_webnlg_local/run_9.
If your task is similar to the task the model of the ckeckpoint was trained on, you can already use MyBartForConditionalGeneration for predictions without further training.
10/11/2021 21:05:22 - INFO - transformers.configuration_utils - loading configuration file out/jointgt_bart_webnlg_local/run_9/pos/config.json
10/11/2021 21:05:22 - INFO - transformers.configuration_utils - Model config BartConfig {
"activation_dropout": 0.1,
"activation_function": "gelu",
"add_bias_logits": false,
"add_final_layer_norm": false,
"architectures": [
"BartForConditionalGeneration"
],
"attention_dropout": 0.1,
"bos_token_id": 0,
"classif_dropout": 0.0,
"d_model": 768,
"decoder_attention_heads": 12,
"decoder_ffn_dim": 3072,
"decoder_layerdrop": 0.0,
"decoder_layers": 6,
"decoder_start_token_id": 2,
"dropout": 0.1,
"early_stopping": true,
"encoder_attention_heads": 12,
"encoder_ffn_dim": 3072,
"encoder_layerdrop": 0.0,
"encoder_layers": 6,
"eos_token_id": 2,
"extra_pos_embeddings": 2,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1",
"2": "LABEL_2"
},
"init_std": 0.02,
"is_encoder_decoder": true,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1,
"LABEL_2": 2
},
"max_position_embeddings": 1024,
"model_type": "bart",
"no_repeat_ngram_size": 3,
"normalize_before": false,
"normalize_embedding": true,
"num_beams": 4,
"num_hidden_layers": 6,
"pad_token_id": 1,
"scale_embedding": false,
"static_position_embeddings": false,
"task_specific_params": {
"summarization": {
"length_penalty": 1.0,
"max_length": 128,
"min_length": 12,
"num_beams": 4
},
"summarization_cnn": {
"length_penalty": 2.0,
"max_length": 142,
"min_length": 56,
"num_beams": 4
},
"summarization_xsum": {
"length_penalty": 1.0,
"max_length": 62,
"min_length": 11,
"num_beams": 6
}
},
"vocab_size": 50265
}
10/11/2021 21:05:22 - INFO - transformers.modeling_utils - loading weights file out/jointgt_bart_webnlg_local/run_9/pos/pytorch_model.bin
10/11/2021 21:05:26 - INFO - transformers.modeling_utils - All model checkpoint weights were used when initializing BartForConditionalGeneration.
10/11/2021 21:05:26 - INFO - transformers.modeling_utils - All the weights of BartForConditionalGeneration were initialized from the model checkpoint at out/jointgt_bart_webnlg_local/run_9/pos.
If your task is similar to the task the model of the ckeckpoint was trained on, you can already use BartForConditionalGeneration for predictions without further training.
10/11/2021 21:05:28 - INFO - __main__ - Starting training!
10/11/2021 21:23:10 - INFO - __main__ - Saved prediction in out/jointgt_bart_webnlg_local/savepredictions_0_800.txt
10/11/2021 21:23:13 - INFO - __main__ - 当前的评估值: bleu-4:60.72644692266208, ROUGE_L:78.97701018776158, CIDEr:5.209459622638824, Chrf:0.7903397635840651
10/11/2021 21:23:13 - INFO - __main__ - Step 800 Train loss 0.43 Learning rate 4.00e-06 BLEU 6072.64% on epoch=2
10/11/2021 21:23:13 - INFO - __main__ - 当前最好的：-1.00
10/11/2021 21:23:13 - INFO - transformers.configuration_utils - Configuration saved in out/jointgt_bart_webnlg_local/run_0/config.json
10/11/2021 21:23:14 - INFO - transformers.modeling_utils - Model weights saved in out/jointgt_bart_webnlg_local/run_0/pytorch_model.bin
10/11/2021 21:23:14 - INFO - transformers.configuration_utils - Configuration saved in out/jointgt_bart_webnlg_local/run_0/pos/config.json
10/11/2021 21:23:14 - INFO - transformers.modeling_utils - Model weights saved in out/jointgt_bart_webnlg_local/run_0/pos/pytorch_model.bin
10/11/2021 21:23:14 - INFO - __main__ - Saving model with best BLEU: -100.00% -> 6072.64% on epoch=2, global_step=800
10/11/2021 21:23:41 - INFO - __main__ - Namespace(adam_epsilon=1e-08, append_another_bos=True, checkpoint='', clean_up_spaces=False, dataset='webnlg', debug=False, do_lowercase=False, do_predict=False, do_pretrain=False, do_train=True, eval_period=200, gradient_accumulation_steps=1, knowledge_file='knowledge-full.json', learning_rate=1e-06, length_penalty=1.0, mask_prob='[0.4,0.2,0.2,0.4,0.2]', max_edge_length=60, max_grad_norm=1.0, max_input_length=256, max_node_length=50, max_output_length=128, model_name='bart', model_path='out/jointgt_bart_webnlg_local/run_9', no_lr_decay=False, num_beams=10, num_train_epochs=100.0, num_workers=8, output_dir='out/jointgt_bart_webnlg_local', pos_model_path='out/jointgt_bart_webnlg_local/run_9/pos', predict_batch_size=16, predict_file='data/webnlg/test', prefix='save', remove_bos=False, save_period=500, seed=42, task_ratio='[0.4,0.4,0.2]', tokenizer_path='pretrain_model/jointgt_bart', train_batch_size=32, train_file='data/webnlg/train', wait_step=30, warmup_proportion=0.01, warmup_steps=2000, weight_decay=0.0)
10/11/2021 21:23:41 - INFO - __main__ - out/jointgt_bart_webnlg_local
10/11/2021 21:23:41 - INFO - __main__ - Using 2 gpus
10/11/2021 21:23:41 - INFO - transformers.tokenization_utils_base - Model name 'pretrain_model/jointgt_bart' not found in model shortcut name list (facebook/bart-base, facebook/bart-large, facebook/bart-large-mnli, facebook/bart-large-cnn, facebook/bart-large-xsum, yjernite/bart_eli5). Assuming 'pretrain_model/jointgt_bart' is a path, a model identifier, or url to a directory containing tokenizer files.
10/11/2021 21:23:41 - INFO - transformers.tokenization_utils_base - Didn't find file pretrain_model/jointgt_bart/added_tokens.json. We won't load it.
10/11/2021 21:23:41 - INFO - transformers.tokenization_utils_base - Didn't find file pretrain_model/jointgt_bart/special_tokens_map.json. We won't load it.
10/11/2021 21:23:41 - INFO - transformers.tokenization_utils_base - Didn't find file pretrain_model/jointgt_bart/tokenizer_config.json. We won't load it.
10/11/2021 21:23:41 - INFO - transformers.tokenization_utils_base - Didn't find file pretrain_model/jointgt_bart/tokenizer.json. We won't load it.
10/11/2021 21:23:41 - INFO - transformers.tokenization_utils_base - loading file pretrain_model/jointgt_bart/vocab.json
10/11/2021 21:23:41 - INFO - transformers.tokenization_utils_base - loading file pretrain_model/jointgt_bart/merges.txt
10/11/2021 21:23:41 - INFO - transformers.tokenization_utils_base - loading file None
10/11/2021 21:23:41 - INFO - transformers.tokenization_utils_base - loading file None
10/11/2021 21:23:41 - INFO - transformers.tokenization_utils_base - loading file None
10/11/2021 21:23:41 - INFO - transformers.tokenization_utils_base - loading file None
10/11/2021 21:23:42 - INFO - transformers.configuration_utils - loading configuration file out/jointgt_bart_webnlg_local/run_9/config.json
10/11/2021 21:23:42 - INFO - transformers.configuration_utils - Model config BartConfig {
"activation_dropout": 0.1,
"activation_function": "gelu",
"add_bias_logits": false,
"add_final_layer_norm": false,
"architectures": [
"MyBartForConditionalGeneration"
],
"attention_dropout": 0.1,
"bos_token_id": 0,
"classif_dropout": 0.0,
"d_model": 768,
"decoder_attention_heads": 12,
"decoder_ffn_dim": 3072,
"decoder_layerdrop": 0.0,
"decoder_layers": 6,
"decoder_start_token_id": 2,
"dropout": 0.1,
"early_stopping": true,
"encoder_attention_heads": 12,
"encoder_ffn_dim": 3072,
"encoder_layerdrop": 0.0,
"encoder_layers": 6,
"eos_token_id": 2,
"extra_pos_embeddings": 2,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1",
"2": "LABEL_2"
},
"init_std": 0.02,
"is_encoder_decoder": true,
"label2id": {
"LABEL_0": 0,
"LABEL_1": 1,
"LABEL_2": 2
},
"max_position_embeddings": 1024,
"model_type": "bart",
"no_repeat_ngram_size": 3,
"normalize_before": false,
"normalize_embedding": true,
"num_beams": 4,
"num_hidden_layers": 6,
"pad_token_id": 1,
"scale_embedding": false,
"static_position_embeddings": false,
"task_specific_params": {
"summarization": {
"length_penalty": 1.0,
"max_length": 128,
"min_length": 12,
"num_beams": 4
},
"summarization_cnn": {
"length_penalty": 2.0,
"max_length": 142,
"min_length": 56,
"num_beams": 4
},
"summarization_xsum": {
"length_penalty": 1.0,
"max_length": 62,
"min_length": 11,
"num_beams": 6
}
},
"vocab_size": 50265
}
10/11/2021 21:23:42 - INFO - transformers.modeling_utils - loading weights file out/jointgt_bart_webnlg_local/run_9/pytorch_model.bin
10/11/2021 21:23:50 - INFO - transformers.modeling_utils - All model checkpoint weights were used when initializing MyBartForConditionalGeneration.
10/11/2021 21:23:50 - INFO - transformers.modeling_utils - All the weights of MyBartForConditionalGeneration were initialized from the model checkpoint at out/jointgt_bart_webnlg_local/run_9.
If your task is similar to the task the model of the ckeckpoint was trained on, you can already use MyBartForConditionalGeneration for predictions without further training.
10/11/2021 21:23:50 - INFO - transformers.configuration_utils - loading configuration file out/jointgt_bart_webnlg_local/run_9/pos/config.json
10/11/2021 21:23:50 - INFO - transformers.configuration_utils - Model config BartConfig {
"activation_dropout": 0.1,
"activation_function": "gelu",
"add_bias_logits": false,
"add_final_layer_norm": false,
"architectures": [
"BartForConditionalGeneration"
],
"attention_dropout": 0.1,
"bos_token_id": 0,
"classif_dropout": 0.0,
"d_model": 768,
"decoder_attention_heads": 12,
"decoder_ffn_dim": 3072,
"decoder_layerdrop": 0.0,
"decoder_layers": 6,
"decoder_start_token_id": 2,
"dropout": 0.1,
"early_stopping": true,
"encoder_attention_heads": 12,
"encoder_ffn_dim": 3072,
"encoder_layerdrop": 0.0,
"encoder_layers": 6,
"eos_token_id": 2,
"extra_pos_embeddings": 2,
"id2label": {
"0": "LABEL_0",
"1": "LABEL_1",
"2": "LABEL_2"
},
"init_std": 0.02,
"is_encoder_decoder": true,
"label2id": {