getting error while making model about simpletransformers HOT 7 CLOSED

Well, the error is telling you what the issue is. Simple Transformers will try to use CUDA by default. If it's not available, you have to specify use_cuda=False when creating the model.

model = ClassificationModel('bert', 'bert-base-cased', use_cuda=False)

from simpletransformers.

Thanks for your reply. Now I got the error while training the model:
ValueError: too many dimensions 'str'

from simpletransformers.

Please give more details about the issue.

Describe the bug
A clear and concise description of what the bug is. Please specify the class causing the issue.

from simpletransformers.

ValueError Traceback (most recent call last)
in
8 model = MultiLabelClassificationModel('roberta', 'roberta-base', num_labels=6, args={'train_batch_size':2, 'gradient_accumulation_steps':16, 'learning_rate': 3e-5, 'num_train_epochs': 3, 'max_seq_length': 512}, use_cuda=False)
9
---> 10 model.train_model(train_df)

~\Anaconda3\envs\simpletransformers\lib\site-packages\simpletransformers\classification\multi_label_classification_model.py in train_model(self, train_df, multi_label, output_dir, show_running_loss, args)
97
98 def train_model(self, train_df, multi_label=True, output_dir=None, show_running_loss=True, args=None):
---> 99 return super().train_model(train_df, multi_label=multi_label, output_dir=output_dir, show_running_loss=show_running_loss, args=args)
100
101 def eval_model(self, eval_df, multi_label=True, output_dir=None, verbose=False, **kwargs):

~\Anaconda3\envs\simpletransformers\lib\site-packages\simpletransformers\classification\classification_model.py in train_model(self, train_df, multi_label, output_dir, show_running_loss, args, eval_df)
157
158
--> 159 train_dataset = self.load_and_cache_examples(train_examples)
160 global_step, tr_loss = self.train(train_dataset, output_dir, show_running_loss=show_running_loss, eval_df=eval_df)
161

~\Anaconda3\envs\simpletransformers\lib\site-packages\simpletransformers\classification\multi_label_classification_model.py in load_and_cache_examples(self, examples, evaluate, no_cache, multi_label)
106
107 def load_and_cache_examples(self, examples, evaluate=False, no_cache=False, multi_label=True):
--> 108 return super().load_and_cache_examples(examples, evaluate=evaluate, no_cache=no_cache, multi_label=multi_label)
109
110 def compute_metrics(self, preds, labels, eval_examples, multi_label=True, **kwargs):

~\Anaconda3\envs\simpletransformers\lib\site-packages\simpletransformers\classification\classification_model.py in load_and_cache_examples(self, examples, evaluate, no_cache, multi_label)
440
441 if output_mode == "classification":
--> 442 all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.long)
443 elif output_mode == "regression":
444 all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.float)

ValueError: too many dimensions 'str'

from simpletransformers.

Hi
I am getting the error when I am trying to execute the code:

model.train_model(train_df)

from simpletransformers.

What is the data in train_df? Is it the minimal example code?

from simpletransformers.

This should be fixed now. There was a typo in the readme.

#42

from simpletransformers.