Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

IndexError: Target 2 is out of bounds. #375

Open
Jai-Agarwal-04 opened this issue Dec 22, 2023 · 3 comments
Open

IndexError: Target 2 is out of bounds. #375

Jai-Agarwal-04 opened this issue Dec 22, 2023 · 3 comments

Comments

@Jai-Agarwal-04
Copy link

Epoch:0 | Loss:0: 0%| | 0/4 [00:03<?, ?it/s]

IndexError Traceback (most recent call last)
Cell In[21], line 7
5 config.num_epoch = 1
6 config.model = APC.APCModelList.FAST_LSA_T_V2
----> 7 trainer = APC.APCTrainer(
8 config=config,
9 dataset=my_dataset,
10 #from_checkpoint="english",
11 # if you want to resume training from our pretrained checkpoints, you can pass the checkpoint name here
12 auto_device=DeviceTypeOption.AUTO,
13 path_to_save=None, # set a path to save checkpoints, if it is None, save checkpoints at 'checkpoints' folder
14 checkpoint_save_mode=ModelSaveOption.SAVE_MODEL_STATE_DICT,
15 load_aug=False,
16 # there are some augmentation dataset for integrated datasets, you use them by setting load_aug=True to improve performance
17 )

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.

This is the error I am getting while using custom dataset

this is my training snippet

from pyabsa import AspectPolarityClassification as APC
from pyabsa import ModelSaveOption, DeviceTypeOption
import random
config = APC.APCConfigManager.get_apc_config_english()
config.model = APC.APCModelList.FAST_LSA_T_V2

config.model = APC.APCModelList.FAST_LSA_S_V2

config.model = APC.APCModelList.BERT_SPC_V2

config.pretrained_bert = "microsoft/deberta-v3-large"
#config.evaluate_begin = 2
config.max_seq_len = 80
config.num_epoch = 1
config.log_step = 5
config.dropout = 0
config.cache_dataset = False
#config.l2reg = 1e-8
config.lsa = True
config.seed = [random.randint(0, 10000) for _ in range(3)]
trainer = APC.APCTrainer(
config=config,
dataset=my_dataset,
#from_checkpoint="/kaggle/working/fast_lcf_bert_English_acc_84.65_f1_82.39",
# if you want to resume training from our pretrained checkpoints, you can pass the checkpoint name here
auto_device=DeviceTypeOption.AUTO,
path_to_save=None, # set a path to save checkpoints, if it is None, save checkpoints at 'checkpoints' folder
checkpoint_save_mode=ModelSaveOption.SAVE_MODEL_STATE_DICT,
load_aug=False,
# there are some augmentation dataset for integrated datasets, you use them by setting load_aug=True to improve performance
)
@yangheng95
Copy link
Owner

What are the labels in your dataset? Please paste the full console output here

@Jai-Agarwal-04
Copy link
Author

Jai-Agarwal-04 commented Dec 26, 2023 •
edited
Loading

pro.apc.valid.txt

custom.apc.test.txt
pro.apc.train.txt

@Jai-Agarwal-04
Copy link
Author

Jai-Agarwal-04 commented Dec 26, 2023 •
edited
Loading

[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,
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, 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, 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,
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, 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, 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,
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]), '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,
-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-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,
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]), '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,
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, 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, 0.0000, 0.0000,
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.,
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.]), 'left_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,
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]), '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,
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]), '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,
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, 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, 0.0000, 0.0000,
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,
1407, 105479, 12525, 2, 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]), '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,
1.0000, 0.9524, 0.9048, 0.8571, 0.8095, 0.7619, 0.7143, 0.6667, 0.6190,
0.5714, 0.5238, 0.4762, 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, 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, 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,
1.0000, 0.9524, 0.9048, 0.8571, 0.8095, 0.7619, 0.7143, 0.6667, 0.6190,
0.5714, 0.5238, 0.4762, 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, 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, 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, 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]), '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, 749, 260, 983, 260, 69963, 261, 1034, 265, 262,
6173, 276, 268, 273, 10567, 81976, 1165, 20465, 537, 3066,
2, 749, 260, 983, 260, 69963, 261, 2, 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]), 'aspect_bert_indices': tensor(0), 'text_raw_bert_indices': tensor(0), 'polarity': tensor(2), 'cluster_ids': tensor([-100, 2, 2, 2, 2, 2, 2, -100, -100, -100, -100, -100,
-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-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, 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]), '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,
0.5714, 0.5238, 0.4762, 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, 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, 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.,
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.]), 'left_text_indices': tensor([ 1, 749, 260, 983, 260, 69963, 261, 1034, 265, 262,
6173, 276, 268, 273, 10567, 81976, 1165, 20465, 537, 3066,
2, 749, 260, 983, 260, 69963, 261, 2, 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]), 'left_dist': tensor(0), 'right_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,
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]), 'right_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,
0.5714, 0.5238, 0.4762, 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, 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, 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, 749, 260, 983, 260, 69963, 261, 1034, 265, 262,
6173, 276, 268, 273, 10567, 81976, 1165, 20465, 537, 3066,
2, 749, 260, 983, 260, 69963, 261, 2, 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]), 'right_dist': tensor(0)}]

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,
0.6333, 0.6000, 0.5667, 0.5333, 0.5000, 0.4667, 0.4333, 0.4000, 0.3667,
0.3333, 0.3000, 0.2667, 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, 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, 0.9667,
0.9333, 0.9000, 0.8667, 0.8333, 0.8000, 0.7667, 0.7333, 0.7000, 0.6667,
0.6333, 0.6000, 0.5667, 0.5333, 0.5000, 0.4667, 0.4333, 0.4000, 0.3667,
0.3333, 0.3000, 0.2667, 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, 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, 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, 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, 15186, 19688, 35157, 261, 267, 271, 21638, 265, 7923,
2898, 22861, 261, 732, 272, 262, 1164, 3740, 75444, 292,
2988, 30083, 1246, 2526, 265, 1260, 17906, 277, 1420, 2,
19688, 35157, 261, 2, 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]), 'aspect_bert_indices': tensor(0), 'text_raw_bert_indices': tensor(0), 'polarity': tensor(2), 'cluster_ids': tensor([-100, -100, 2, 2, 2, -100, -100, -100, -100, -100, -100, -100,
-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-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, 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, 0]), 'left_lcf_cdw_vec': 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,
0.6333, 0.6000, 0.5667, 0.5333, 0.5000, 0.4667, 0.4333, 0.4000, 0.3667,
0.3333, 0.3000, 0.2667, 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, 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]), '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.,
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.]), 'left_text_indices': tensor([ 1, 15186, 19688, 35157, 261, 267, 271, 21638, 265, 7923,
2898, 22861, 261, 732, 272, 262, 1164, 3740, 75444, 292,
2988, 30083, 1246, 2526, 265, 1260, 17906, 277, 1420, 2,
19688, 35157, 261, 2, 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]), 'left_dist': tensor(0), 'right_lcf_cdm_vec': tensor([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, 0]), 'right_lcf_cdw_vec': 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,
0.6333, 0.6000, 0.5667, 0.5333, 0.5000, 0.4667, 0.4333, 0.4000, 0.3667,
0.3333, 0.3000, 0.2667, 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, 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]), '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, 15186, 19688, 35157, 261, 267, 271, 21638, 265, 7923,
2898, 22861, 261, 732, 272, 262, 1164, 3740, 75444, 292,
2988, 30083, 1246, 2526, 265, 1260, 17906, 277, 1420, 2,
19688, 35157, 261, 2, 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]), 'right_dist': tensor(0)}, {'ex_id': tensor(1), 'text_raw': 'InspectorRita Yadav,in-charge of cyber crimestation, told that the police arrested Vishal from Bardhaman district of West Bengal on Monday', 'text_spc': '[CLS] InspectorRita Yadav,in-charge of cyber crimestation, told that the police arrested Vishal from Bardhaman district of West Bengal on Monday [SEP] Vishal [SEP]', 'aspect': 'Vishal', 'aspect_position': tensor(0), 'lca_ids': tensor([0.5000, 0.5333, 0.5667, 0.6000, 0.6333, 0.6667, 0.7000, 0.7333, 0.7667,
0.8000, 0.8333, 0.8667, 0.9000, 0.9333, 0.9667, 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, 0.0000, 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, 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([0.5000, 0.5333, 0.5667, 0.6000, 0.6333, 0.6667, 0.7000, 0.7333, 0.7667,
0.8000, 0.8333, 0.8667, 0.9000, 0.9333, 0.9667, 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, 0.0000, 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, 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([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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]), '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, 15186, 106763, 35157, 261, 547, 271, 21638, 265,
7923, 2898, 22861, 261, 732, 272, 262, 1164, 3740,
75444, 292, 2988, 30083, 1246, 2526, 265, 1260, 17906,
277, 1420, 2, 75444, 2, 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]), 'aspect_bert_indices': tensor(0), 'text_raw_bert_indices': tensor(0), 'polarity': tensor(0), 'cluster_ids': tensor([-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-100, -100, -100, -100, -100, -100, 0, -100, -100, -100, -100, -100,
-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-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([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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]), 'left_lcf_cdw_vec': tensor([0.5000, 0.5333, 0.5667, 0.6000, 0.6333, 0.6667, 0.7000, 0.7333, 0.7667,
0.8000, 0.8333, 0.8667, 0.9000, 0.9333, 0.9667, 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, 0.0000, 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, 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]), '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.,
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.]), 'left_text_indices': tensor([ 1, 15186, 106763, 35157, 261, 547, 271, 21638, 265,
7923, 2898, 22861, 261, 732, 272, 262, 1164, 3740,
75444, 292, 2988, 30083, 1246, 2526, 265, 1260, 17906,
277, 1420, 2, 75444, 2, 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]), 'left_dist': tensor(0), 'right_lcf_cdm_vec': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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]), 'right_lcf_cdw_vec': tensor([0.5000, 0.5333, 0.5667, 0.6000, 0.6333, 0.6667, 0.7000, 0.7333, 0.7667,
0.8000, 0.8333, 0.8667, 0.9000, 0.9333, 0.9667, 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, 0.0000, 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, 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]), '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, 15186, 106763, 35157, 261, 547, 271, 21638, 265,
7923, 2898, 22861, 261, 732, 272, 262, 1164, 3740,
75444, 292, 2988, 30083, 1246, 2526, 265, 1260, 17906,
277, 1420, 2, 75444, 2, 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]), 'right_dist': tensor(0)}]

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:
[{'ex_id': tensor(0), 'text_raw': 'he sized cattle then go missing and reach slaughterhouses inHyderabad, Mr Kharge said', 'text_spc': '[CLS] he sized cattle then go missing and reach slaughterhouses inHyderabad, Mr Kharge said [SEP] Kharge [SEP]', 'aspect': 'Kharge', 'aspect_position': tensor(0), 'lca_ids': tensor([0.3333, 0.3810, 0.4286, 0.4762, 0.5238, 0.5714, 0.6190, 0.6667, 0.7143,
0.7619, 0.8095, 0.8571, 0.9048, 0.9524, 1.0000, 1.0000, 1.0000, 1.0000,
1.0000, 1.0000, 1.0000, 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, 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,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]), 'lcf_vec': tensor(0), 'lcf_cdw_vec': tensor([0.3333, 0.3810, 0.4286, 0.4762, 0.5238, 0.5714, 0.6190, 0.6667, 0.7143,
0.7619, 0.8095, 0.8571, 0.9048, 0.9524, 1.0000, 1.0000, 1.0000, 1.0000,
1.0000, 1.0000, 1.0000, 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, 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,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]), 'lcf_cdm_vec': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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]), '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, 313, 6408, 9044, 393, 424, 2367, 263, 1431, 69672,
268, 267, 45840, 7059, 29472, 261, 945, 48373, 6024, 357,
2, 48373, 6024, 2, 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]), 'aspect_bert_indices': tensor(0), 'text_raw_bert_indices': tensor(0), 'polarity': tensor(1), 'cluster_ids': tensor([-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-100, -100, -100, -100, -100, 1, 1, -100, -100, -100, -100, -100,
-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-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([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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]), 'left_lcf_cdw_vec': tensor([0.3333, 0.3810, 0.4286, 0.4762, 0.5238, 0.5714, 0.6190, 0.6667, 0.7143,
0.7619, 0.8095, 0.8571, 0.9048, 0.9524, 1.0000, 1.0000, 1.0000, 1.0000,
1.0000, 1.0000, 1.0000, 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, 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,
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.,
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.]), 'left_text_indices': tensor([ 1, 313, 6408, 9044, 393, 424, 2367, 263, 1431, 69672,
268, 267, 45840, 7059, 29472, 261, 945, 48373, 6024, 357,
2, 48373, 6024, 2, 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]), 'left_dist': tensor(0), 'right_lcf_cdm_vec': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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]), 'right_lcf_cdw_vec': tensor([0.3333, 0.3810, 0.4286, 0.4762, 0.5238, 0.5714, 0.6190, 0.6667, 0.7143,
0.7619, 0.8095, 0.8571, 0.9048, 0.9524, 1.0000, 1.0000, 1.0000, 1.0000,
1.0000, 1.0000, 1.0000, 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, 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,
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, 313, 6408, 9044, 393, 424, 2367, 263, 1431, 69672,
268, 267, 45840, 7059, 29472, 261, 945, 48373, 6024, 357,
2, 48373, 6024, 2, 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]), '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,
0.8710, 0.8387, 0.8065, 0.7742, 0.7419, 0.7097, 0.6774, 0.6452, 0.6129,
0.5806, 0.5484, 0.5161, 0.4839, 0.4516, 0.4194, 0.3871, 0.3548, 0.3226,
0.2903, 0.2581, 0.2258, 0.1935, 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, 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, 0.9677, 0.9355, 0.9032,
0.8710, 0.8387, 0.8065, 0.7742, 0.7419, 0.7097, 0.6774, 0.6452, 0.6129,
0.5806, 0.5484, 0.5161, 0.4839, 0.4516, 0.4194, 0.3871, 0.3548, 0.3226,
0.2903, 0.2581, 0.2258, 0.1935, 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, 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, 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, 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, 61794, 66065, 303, 1223, 264, 262, 15280, 28328, 555,
4272, 285, 272, 262, 30221, 1253, 264, 417, 284, 15082,
263, 10265, 41770, 261, 970, 264, 32583, 10743, 268, 260,
2, 61794, 66065, 2, 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]), 'aspect_bert_indices': tensor(0), 'text_raw_bert_indices': tensor(0), 'polarity': tensor(0), 'cluster_ids': tensor([-100, 0, 0, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,
-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, 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, 0, 0, 0]), 'left_lcf_cdw_vec': tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 0.9677, 0.9355, 0.9032,
0.8710, 0.8387, 0.8065, 0.7742, 0.7419, 0.7097, 0.6774, 0.6452, 0.6129,
0.5806, 0.5484, 0.5161, 0.4839, 0.4516, 0.4194, 0.3871, 0.3548, 0.3226,
0.2903, 0.2581, 0.2258, 0.1935, 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, 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]), '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.,
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.]), 'left_text_indices': tensor([ 1, 61794, 66065, 303, 1223, 264, 262, 15280, 28328, 555,
4272, 285, 272, 262, 30221, 1253, 264, 417, 284, 15082,
263, 10265, 41770, 261, 970, 264, 32583, 10743, 268, 260,
2, 61794, 66065, 2, 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]), 'left_dist': tensor(0), 'right_lcf_cdm_vec': tensor([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, 0, 0, 0]), 'right_lcf_cdw_vec': tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 0.9677, 0.9355, 0.9032,
0.8710, 0.8387, 0.8065, 0.7742, 0.7419, 0.7097, 0.6774, 0.6452, 0.6129,
0.5806, 0.5484, 0.5161, 0.4839, 0.4516, 0.4194, 0.3871, 0.3548, 0.3226,
0.2903, 0.2581, 0.2258, 0.1935, 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, 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]), '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, 61794, 66065, 303, 1223, 264, 262, 15280, 28328, 555,
4272, 285, 272, 262, 30221, 1253, 264, 417, 284, 15082,
263, 10265, 41770, 261, 970, 264, 32583, 10743, 268, 260,
2, 61794, 66065, 2, 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]), 'right_dist': tensor(0)}]

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.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Assignees
No one assigned
Labels
None yet
Projects
None yet
Milestone
No milestone
Development

No branches or pull requests
2 participants
@yangheng95 @Jai-Agarwal-04