Comments (3)
yangheng95
commented on May 28, 2024
What are the labels in your dataset? Please paste the full console output here
from pyabsa.
Jai-Agarwal-04
commented on May 28, 2024
custom.apc.test.txt
pro.apc.train.txt
from pyabsa.
Jai-Agarwal-04
commented on May 28, 2024
[2023-12-26 04:41:19] (2.3.4) Set Model Device: cpu
[2023-12-26 04:41:19] (2.3.4) Device Name: Unknown
2023-12-26 04:41:19,870 INFO: PyABSA version: 2.3.4
2023-12-26 04:41:19,872 INFO: Transformers version: 4.36.0
2023-12-26 04:41:19,873 INFO: Torch version: 2.0.0+cpu+cudaNone
2023-12-26 04:41:19,874 INFO: Device: Unknown
2023-12-26 04:41:19,875 INFO: 100.pro in the trainer is not a exact path, will search dataset in current working directory
2023-12-26 04:41:19,877 INFO: You can set load_aug=True in a trainer to augment your dataset (English only yet) and improve performance.
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
- Avoid using tokenizers before the fork if possible
- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
2023-12-26 04:41:23,383 INFO: Load dataset from datasets/apc_datasets/100.pro/pro.train.dat.apc
preparing dataloader: 100%|██████████| 57/57 [00:00<00:00, 1175.34it/s]
2023-12-26 04:41:23,435 INFO: Dataset Label Details: {'Negative': 31, 'Neutral': 4, 'Positive': 22, 'Sum': 57}
2023-12-26 04:41:23,472 INFO: train data examples:
[{'ex_id': tensor(0), 'text_raw': 'Mr. Varghese said that he will continue to cooperate with theprobe.', 'text_spc': '[CLS] Mr. Varghese said that he will continue to cooperate with theprobe. [SEP] Varghese [SEP]', 'aspect': 'Varghese', 'aspect_position': tensor(0), 'lca_ids': tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000,
0.9444, 0.8889, 0.8333, 0.7778, 0.7222, 0.6667, 0.6111, 0.5556, 0.5000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
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0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]), 'lcf_vec': tensor(0), 'lcf_cdw_vec': tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000,
0.9444, 0.8889, 0.8333, 0.7778, 0.7222, 0.6667, 0.6111, 0.5556, 0.5000,
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0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]), 'lcf_cdm_vec': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0]), 'lcfs_vec': tensor(0), 'lcfs_cdw_vec': tensor(0), 'lcfs_cdm_vec': tensor(0), 'dlcf_vec': tensor(0), 'dlcfs_vec': tensor(0), 'depend_vec': tensor(0), 'depended_vec': tensor(0), 'spc_mask_vec': tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1.]), 'text_indices': tensor([ 1, 945, 260, 1407, 105479, 12525, 357, 272, 313,
296, 959, 264, 13535, 275, 262, 69532, 260, 2,
1407, 105479, 12525, 2, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0]), 'aspect_bert_indices': tensor(0), 'text_raw_bert_indices': tensor(0), 'polarity': tensor(1), 'cluster_ids': tensor([-100, -100, -100, 1, 1, 1, -100, -100, -100, -100, -100, -100,
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-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-100, -100, -100, -100, -100, -100, -100, -100]), 'side_ex_ids': tensor(0), 'left_lcf_cdm_vec': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0]), 'left_lcf_cdw_vec': tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000,
0.9444, 0.8889, 0.8333, 0.7778, 0.7222, 0.6667, 0.6111, 0.5556, 0.5000,
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0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]), 'left_spc_mask_vec': tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
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1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1.]), 'left_text_indices': tensor([ 1, 945, 260, 1407, 105479, 12525, 357, 272, 313,
296, 959, 264, 13535, 275, 262, 69532, 260, 2,
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0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0]), 'left_dist': tensor(0), 'right_lcf_cdm_vec': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0]), 'right_lcf_cdw_vec': tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000,
0.9444, 0.8889, 0.8333, 0.7778, 0.7222, 0.6667, 0.6111, 0.5556, 0.5000,
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0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]), 'right_spc_mask_vec': tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1.]), 'right_text_indices': tensor([ 1, 945, 260, 1407, 105479, 12525, 357, 272, 313,
296, 959, 264, 13535, 275, 262, 69532, 260, 2,
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0, 0, 0, 0, 0, 0, 0, 0]), 'right_dist': tensor(0)}, {'ex_id': tensor(1), 'text_raw': ' M.B. Raju, member of theparty’s Irinjalakuda area committee', 'text_spc': '[CLS] M.B. Raju, member of theparty’s Irinjalakuda area committee [SEP] M.B. Raju, [SEP]', 'aspect': 'M.B. Raju,', 'aspect_position': tensor(0), 'lca_ids': tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000,
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1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1.]), 'text_indices': tensor([ 1, 749, 260, 983, 260, 69963, 261, 1034, 265, 262,
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-100, -100, -100, -100, -100, -100, -100, -100]), 'side_ex_ids': tensor(0), 'left_lcf_cdm_vec': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0]), 'left_lcf_cdw_vec': tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000,
1.0000, 0.9524, 0.9048, 0.8571, 0.8095, 0.7619, 0.7143, 0.6667, 0.6190,
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1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
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1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1.]), 'right_text_indices': tensor([ 1, 749, 260, 983, 260, 69963, 261, 1034, 265, 262,
6173, 276, 268, 273, 10567, 81976, 1165, 20465, 537, 3066,
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2023-12-26 04:41:24,192 INFO: Load dataset from datasets/apc_datasets/100.pro/pro.test.dat.apc
preparing dataloader: 100%|██████████| 7/7 [00:00<00:00, 1000.82it/s]
2023-12-26 04:41:24,205 INFO: Dataset Label Details: {'Neutral': 1, 'Negative': 3, 'Positive': 3, 'Sum': 7}
2023-12-26 04:41:24,225 INFO: test data examples:
[{'ex_id': tensor(0), 'text_raw': 'Inspector Rita Yadav, in-charge of cyber crimestation, told that the police arrested Vishal from Bardhaman district of West Bengal on Monday', 'text_spc': '[CLS] Inspector Rita Yadav, in-charge of cyber crimestation, told that the police arrested Vishal from Bardhaman district of West Bengal on Monday [SEP] Rita Yadav, [SEP]', 'aspect': 'Rita Yadav,', 'aspect_position': tensor(0), 'lca_ids': tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 0.9667,
0.9333, 0.9000, 0.8667, 0.8333, 0.8000, 0.7667, 0.7333, 0.7000, 0.6667,
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2023-12-26 04:41:24,931 INFO: Load dataset from datasets/apc_datasets/100.pro/pro.valid.dat.apc
preparing dataloader: 100%|██████████| 2/2 [00:00<00:00, 653.32it/s]
2023-12-26 04:41:24,940 INFO: Dataset Label Details: {'Negative': 1, 'Positive': 1, 'Sum': 2}
2023-12-26 04:41:24,958 INFO: valid data examples:
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0, 0, 0, 0, 0, 0, 0, 0]), 'lcfs_vec': tensor(0), 'lcfs_cdw_vec': tensor(0), 'lcfs_cdm_vec': tensor(0), 'dlcf_vec': tensor(0), 'dlcfs_vec': tensor(0), 'depend_vec': tensor(0), 'depended_vec': tensor(0), 'spc_mask_vec': tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
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1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
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0, 0, 0, 0, 0, 0, 0, 0]), 'right_lcf_cdw_vec': tensor([0.3333, 0.3810, 0.4286, 0.4762, 0.5238, 0.5714, 0.6190, 0.6667, 0.7143,
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1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
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1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1.]), 'right_text_indices': tensor([ 1, 313, 6408, 9044, 393, 424, 2367, 263, 1431, 69672,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), 'right_dist': tensor(0)}, {'ex_id': tensor(1), 'text_raw': ' Arvind Kejriwal has written to the Enforcement Directorate(ED) that the summons sent to him was unlawful and politicallymotivated, according to AAPsources.', 'text_spc': '[CLS] Arvind Kejriwal has written to the Enforcement Directorate(ED) that the summons sent to him was unlawful and politicallymotivated, according to AAPsources. [SEP] Arvind Kejriwal [SEP]', 'aspect': 'Arvind Kejriwal', 'aspect_position': tensor(0), 'lca_ids': tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 0.9677, 0.9355, 0.9032,
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1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
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2023-12-26 04:41:25,180 INFO: Model Architecture:
APCEnsembler(
(models): ModuleList(
(0): FAST_LSA_T_V2(
(bert4global): DebertaV2Model(
(embeddings): DebertaV2Embeddings(
(word_embeddings): Embedding(128100, 1024, padding_idx=0)
(LayerNorm): LayerNorm((1024,), eps=1e-07, elementwise_affine=True)
(dropout): StableDropout()
)
(encoder): DebertaV2Encoder(
(layer): ModuleList(
(0-23): 24 x DebertaV2Layer(
(attention): DebertaV2Attention(
(self): DisentangledSelfAttention(
(query_proj): Linear(in_features=1024, out_features=1024, bias=True)
(key_proj): Linear(in_features=1024, out_features=1024, bias=True)
(value_proj): Linear(in_features=1024, out_features=1024, bias=True)
(pos_dropout): StableDropout()
(dropout): StableDropout()
)
(output): DebertaV2SelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): LayerNorm((1024,), eps=1e-07, elementwise_affine=True)
(dropout): StableDropout()
)
)
(intermediate): DebertaV2Intermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
(intermediate_act_fn): GELUActivation()
)
(output): DebertaV2Output(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): LayerNorm((1024,), eps=1e-07, elementwise_affine=True)
(dropout): StableDropout()
)
)
)
(rel_embeddings): Embedding(512, 1024)
(LayerNorm): LayerNorm((1024,), eps=1e-07, elementwise_affine=True)
)
)
(dropout): Dropout(p=0, inplace=False)
(post_encoder): Encoder(
(encoder): ModuleList(
(0): SelfAttention(
(SA): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
(tanh): Tanh()
)
(post_encoder_): Encoder(
(encoder): ModuleList(
(0): SelfAttention(
(SA): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
(tanh): Tanh()
)
(bert_pooler): BertPooler(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(activation): Tanh()
)
(CDW_LSA): LSA(
(encoder): Encoder(
(encoder): ModuleList(
(0): SelfAttention(
(SA): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
(tanh): Tanh()
)
(encoder_left): Encoder(
(encoder): ModuleList(
(0): SelfAttention(
(SA): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
(tanh): Tanh()
)
(encoder_right): Encoder(
(encoder): ModuleList(
(0): SelfAttention(
(SA): BertSelfAttention(
(query): Linear(in_features=1024, out_features=1024, bias=True)
(key): Linear(in_features=1024, out_features=1024, bias=True)
(value): Linear(in_features=1024, out_features=1024, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
(tanh): Tanh()
)
(linear_window_3h): Linear(in_features=3072, out_features=1024, bias=True)
(linear_window_2h): Linear(in_features=2048, out_features=1024, bias=True)
)
(post_linear): Linear(in_features=2048, out_features=1024, bias=True)
(dense): Linear(in_features=1024, out_features=2, bias=True)
)
)
(bert): DebertaV2Model(
(embeddings): DebertaV2Embeddings(
(word_embeddings): Embedding(128100, 1024, padding_idx=0)
(LayerNorm): LayerNorm((1024,), eps=1e-07, elementwise_affine=True)
(dropout): StableDropout()
)
(encoder): DebertaV2Encoder(
(layer): ModuleList(
(0-23): 24 x DebertaV2Layer(
(attention): DebertaV2Attention(
(self): DisentangledSelfAttention(
(query_proj): Linear(in_features=1024, out_features=1024, bias=True)
(key_proj): Linear(in_features=1024, out_features=1024, bias=True)
(value_proj): Linear(in_features=1024, out_features=1024, bias=True)
(pos_dropout): StableDropout()
(dropout): StableDropout()
)
(output): DebertaV2SelfOutput(
(dense): Linear(in_features=1024, out_features=1024, bias=True)
(LayerNorm): LayerNorm((1024,), eps=1e-07, elementwise_affine=True)
(dropout): StableDropout()
)
)
(intermediate): DebertaV2Intermediate(
(dense): Linear(in_features=1024, out_features=4096, bias=True)
(intermediate_act_fn): GELUActivation()
)
(output): DebertaV2Output(
(dense): Linear(in_features=4096, out_features=1024, bias=True)
(LayerNorm): LayerNorm((1024,), eps=1e-07, elementwise_affine=True)
(dropout): StableDropout()
)
)
)
(rel_embeddings): Embedding(512, 1024)
(LayerNorm): LayerNorm((1024,), eps=1e-07, elementwise_affine=True)
)
)
(dense): Linear(in_features=2, out_features=2, bias=True)
)
2023-12-26 04:41:25,181 INFO: ABSADatasetsVersion:None --> Calling Count:0
2023-12-26 04:41:25,182 INFO: MV:<metric_visualizer.metric_visualizer.MetricVisualizer object at 0x78df6ab262c0> --> Calling Count:0
2023-12-26 04:41:25,183 INFO: PyABSAVersion:2.3.4 --> Calling Count:1
2023-12-26 04:41:25,184 INFO: SRD:3 --> Calling Count:132
2023-12-26 04:41:25,185 INFO: TorchVersion:2.0.0+cpu+cudaNone --> Calling Count:1
2023-12-26 04:41:25,186 INFO: TransformersVersion:4.36.0 --> Calling Count:1
2023-12-26 04:41:25,186 INFO: auto_device:True --> Calling Count:3
2023-12-26 04:41:25,187 INFO: batch_size:16 --> Calling Count:3
2023-12-26 04:41:25,188 INFO: cache_dataset:False --> Calling Count:1
2023-12-26 04:41:25,188 INFO: checkpoint_save_mode:1 --> Calling Count:4
2023-12-26 04:41:25,191 INFO: cross_validate_fold:-1 --> Calling Count:1
2023-12-26 04:41:25,191 INFO: dataset_file:{'train': ['datasets/apc_datasets/100.pro/pro.train.dat.apc'], 'test': ['datasets/apc_datasets/100.pro/pro.test.dat.apc'], 'valid': ['datasets/apc_datasets/100.pro/pro.valid.dat.apc']} --> Calling Count:17
2023-12-26 04:41:25,192 INFO: dataset_name:100.pro --> Calling Count:3
2023-12-26 04:41:25,193 INFO: dca_layer:3 --> Calling Count:0
2023-12-26 04:41:25,194 INFO: dca_p:1 --> Calling Count:0
2023-12-26 04:41:25,195 INFO: deep_ensemble:False --> Calling Count:0
2023-12-26 04:41:25,196 INFO: device:cpu --> Calling Count:3
2023-12-26 04:41:25,197 INFO: device_name:Unknown --> Calling Count:1
2023-12-26 04:41:25,197 INFO: dlcf_a:2 --> Calling Count:0
2023-12-26 04:41:25,198 INFO: dropout:0 --> Calling Count:1
2023-12-26 04:41:25,199 INFO: dynamic_truncate:True --> Calling Count:132
2023-12-26 04:41:25,199 INFO: embed_dim:1024 --> Calling Count:7
2023-12-26 04:41:25,200 INFO: eta:1 --> Calling Count:2
2023-12-26 04:41:25,201 INFO: eta_lr:0.1 --> Calling Count:1
2023-12-26 04:41:25,201 INFO: evaluate_begin:0 --> Calling Count:0
2023-12-26 04:41:25,202 INFO: from_checkpoint:None --> Calling Count:0
2023-12-26 04:41:25,203 INFO: hidden_dim:1024 --> Calling Count:0
2023-12-26 04:41:25,203 INFO: index_to_label:{0: 'Negative', 1: 'Positive', 2: 'Positive'} --> Calling Count:4
2023-12-26 04:41:25,204 INFO: inference_model:None --> Calling Count:0
2023-12-26 04:41:25,205 INFO: initializer:xavier_uniform_ --> Calling Count:0
2023-12-26 04:41:25,207 INFO: inputs_cols:['lcf_cdm_vec', 'lcf_cdw_vec', 'left_lcf_cdm_vec', 'left_lcf_cdw_vec', 'right_lcf_cdm_vec', 'right_lcf_cdw_vec', 'spc_mask_vec', 'text_indices'] --> Calling Count:996
2023-12-26 04:41:25,208 INFO: l2reg:1e-06 --> Calling Count:2
2023-12-26 04:41:25,209 INFO: label_to_index:{'Negative': 0, 'Neutral': 1, 'Positive': 1} --> Calling Count:1
2023-12-26 04:41:25,209 INFO: lcf:cdw --> Calling Count:3
2023-12-26 04:41:25,212 INFO: learning_rate:2e-05 --> Calling Count:1
2023-12-26 04:41:25,213 INFO: load_aug:False --> Calling Count:1
2023-12-26 04:41:25,214 INFO: log_step:5 --> Calling Count:0
2023-12-26 04:41:25,215 INFO: logger:<_Logger fast_lsa_t_v2 (INFO)> --> Calling Count:16
2023-12-26 04:41:25,215 INFO: lsa:True --> Calling Count:0
2023-12-26 04:41:25,216 INFO: max_seq_len:80 --> Calling Count:792
2023-12-26 04:41:25,217 INFO: model:<class 'pyabsa.tasks.AspectPolarityClassification.models.lcf.fast_lsa_t_v2.FAST_LSA_T_V2'> --> Calling Count:6
2023-12-26 04:41:25,217 INFO: model_name:fast_lsa_t_v2 --> Calling Count:134
2023-12-26 04:41:25,218 INFO: model_path_to_save:checkpoints --> Calling Count:0
2023-12-26 04:41:25,219 INFO: num_epoch:1 --> Calling Count:0
2023-12-26 04:41:25,220 INFO: optimizer:adamw --> Calling Count:1
2023-12-26 04:41:25,220 INFO: output_dim:2 --> Calling Count:3
2023-12-26 04:41:25,221 INFO: overwrite_cache:False --> Calling Count:0
2023-12-26 04:41:25,224 INFO: path_to_save:None --> Calling Count:1
2023-12-26 04:41:25,225 INFO: patience:99999 --> Calling Count:0
2023-12-26 04:41:25,227 INFO: pretrained_bert:microsoft/deberta-v3-large --> Calling Count:5
2023-12-26 04:41:25,228 INFO: save_mode:1 --> Calling Count:0
2023-12-26 04:41:25,229 INFO: seed:1291 --> Calling Count:6
2023-12-26 04:41:25,231 INFO: sigma:0.3 --> Calling Count:0
2023-12-26 04:41:25,233 INFO: similarity_threshold:1 --> Calling Count:3
2023-12-26 04:41:25,234 INFO: spacy_model:en_core_web_sm --> Calling Count:5
2023-12-26 04:41:25,236 INFO: srd_alignment:True --> Calling Count:0
2023-12-26 04:41:25,237 INFO: task_code:APC --> Calling Count:1
2023-12-26 04:41:25,238 INFO: task_name:Aspect-based Sentiment Classification --> Calling Count:0
2023-12-26 04:41:25,239 INFO: tokenizer:DebertaV2TokenizerFast(name_or_path='microsoft/deberta-v3-large', vocab_size=128000, model_max_length=1000000000000000019884624838656, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'bos_token': '[CLS]', 'eos_token': '[SEP]', 'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'}, clean_up_tokenization_spaces=True), added_tokens_decoder={
0: AddedToken("[PAD]", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
1: AddedToken("[CLS]", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
2: AddedToken("[SEP]", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
3: AddedToken("[UNK]", rstrip=False, lstrip=False, single_word=False, normalized=True, special=True),
128000: AddedToken("[MASK]", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),
} --> Calling Count:0
2023-12-26 04:41:25,240 INFO: use_amp:False --> Calling Count:1
2023-12-26 04:41:25,241 INFO: use_bert_spc:True --> Calling Count:0
2023-12-26 04:41:25,242 INFO: use_syntax_based_SRD:False --> Calling Count:0
2023-12-26 04:41:25,243 INFO: warmup_step:-1 --> Calling Count:0
2023-12-26 04:41:25,246 INFO: window:lr --> Calling Count:0
2023-12-26 04:41:25,256 INFO: ***** Running training for Aspect-based Sentiment Classification *****
2023-12-26 04:41:25,258 INFO: Training set examples = 57
2023-12-26 04:41:25,261 INFO: Test set examples = 7
2023-12-26 04:41:25,262 INFO: Total params = 458150922, Trainable params = 458150922, Non-trainable params = 0
2023-12-26 04:41:25,263 INFO: Batch size = 16
2023-12-26 04:41:25,264 INFO: Num steps = 0
Epoch:0 | Loss:0: 0%| | 0/4 [00:00<?, ?it/s]We strongly recommend passing in an attention_mask since your input_ids may be padded. See https://huggingface.co/docs/transformers/troubleshooting#incorrect-output-when-padding-tokens-arent-masked.
Epoch:0 | Loss:0: 0%| | 0/4 [00:09<?, ?it/s]
IndexError Traceback (most recent call last)
Cell In[17], line 18
16 config.lsa = True
17 config.seed = [random.randint(0, 10000) for _ in range(3)]
---> 18 trainer = APC.APCTrainer(
19 config=config,
20 dataset=my_dataset,
21 #from_checkpoint="/kaggle/working/fast_lcf_bert_English_acc_84.65_f1_82.39",
22 # if you want to resume training from our pretrained checkpoints, you can pass the checkpoint name here
23 auto_device=DeviceTypeOption.AUTO,
24 path_to_save=None, # set a path to save checkpoints, if it is None, save checkpoints at 'checkpoints' folder
25 checkpoint_save_mode=ModelSaveOption.SAVE_MODEL_STATE_DICT,
26 load_aug=False,
27 # there are some augmentation dataset for integrated datasets, you use them by setting load_aug=True to improve performance
28 )
File /opt/conda/lib/python3.10/site-packages/pyabsa/tasks/AspectPolarityClassification/trainer/apc_trainer.py:69, in APCTrainer.init(self, config, dataset, from_checkpoint, checkpoint_save_mode, auto_device, path_to_save, load_aug)
64 self.config.task_code = TaskCodeOption.Aspect_Polarity_Classification
65 self.config.task_name = TaskNameOption().get(
66 TaskCodeOption.Aspect_Polarity_Classification
67 )
---> 69 self._run()
File /opt/conda/lib/python3.10/site-packages/pyabsa/framework/trainer_class/trainer_template.py:241, in Trainer._run(self)
239 self.config.seed = s
240 if self.config.checkpoint_save_mode:
--> 241 model_path.append(self.training_instructor(self.config).run())
242 else:
243 # always return the last trained model if you don't save trained model
244 model = self.inference_model_class(
245 checkpoint=self.training_instructor(self.config).run()
246 )
File /opt/conda/lib/python3.10/site-packages/pyabsa/tasks/AspectPolarityClassification/instructor/apc_instructor.py:702, in APCTrainingInstructor.run(self)
699 def run(self):
700 # Loss and Optimizer
701 criterion = nn.CrossEntropyLoss()
--> 702 return self._train(criterion)
File /opt/conda/lib/python3.10/site-packages/pyabsa/framework/instructor_class/instructor_template.py:372, in BaseTrainingInstructor._train(self, criterion)
369 return self._k_fold_train_and_evaluate(criterion)
370 # Train and evaluate the model if there is only one validation dataloader
371 else:
--> 372 return self._train_and_evaluate(criterion)
File /opt/conda/lib/python3.10/site-packages/pyabsa/tasks/AspectPolarityClassification/instructor/apc_instructor.py:135, in APCTrainingInstructor._train_and_evaluate(self, criterion)
133 loss = outputs["loss"]
134 else:
--> 135 loss = criterion(outputs["logits"], targets)
137 if self.config.auto_device == DeviceTypeOption.ALL_CUDA:
138 loss = loss.mean()
File /opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1501, in Module._call_impl(self, *args, **kwargs)
1496 # If we don't have any hooks, we want to skip the rest of the logic in
1497 # this function, and just call forward.
1498 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1499 or _global_backward_pre_hooks or _global_backward_hooks
1500 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1501 return forward_call(*args, **kwargs)
1502 # Do not call functions when jit is used
1503 full_backward_hooks, non_full_backward_hooks = [], []
File /opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py:1174, in CrossEntropyLoss.forward(self, input, target)
1173 def forward(self, input: Tensor, target: Tensor) -> Tensor:
-> 1174 return F.cross_entropy(input, target, weight=self.weight,
1175 ignore_index=self.ignore_index, reduction=self.reduction,
1176 label_smoothing=self.label_smoothing)
File /opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:3029, in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction, label_smoothing)
3027 if size_average is not None or reduce is not None:
3028 reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 3029 return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index, label_smoothing)
IndexError: Target 2 is out of bounds.
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Related Issues (20)
- some bug in TUTORIALS of Aspect Term Extraction and Sentiment Classification HOT 1
- Attention mask not specified HOT 1
- 请问提取情感三元组是利用哪个算法实现的
- 请问情感三元组的提取是利用哪个算法实现的 HOT 1
- Max length in ASTE HOT 1
- Loading state_dict fails (model trained on linux and copied to windows) HOT 1
- RuntimeError: Error(s) in loading state_dict for APCEnsembler: HOT 1
- Aspect sentiment quadruple extraction
- 模型无法远程下载，如何从本地加载模型？ HOT 1
- 请问哪里可以设置integrated dataset path
- Fail to download checkpoints info from huggingface space, try to download from local HOT 1
- import AspectSentimentTripletExtraction in colab raise error HOT 4
- Fine-tuned ASTE model returns no triplets HOT 1
- The example code: Aspect_Term_Extraction.ipynb in the subfolder of aspect_term_extraction in examples-v2 is not working HOT 4
- PyABSA for German language? HOT 1
- No module named 'pyabsa.functional' HOT 4
- AspectExtractor returns aspects only if pred_sentiments=True in `extract_aspect` method but I don't want sentiments HOT 3
- how to train my own model? HOT 1
- Different performance between model saved as fine-tuned PLM and state_dict HOT 3
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