kiki88104

The data type is a pytorch **tensordataset** It includes in **input_ids_array** and **label_id_array**. There are all in **Huggingface AutoTokenizer** generated. Because the text needs to encode by using Huggingface language...

```python def generate_data_set(input_examples, label_masks, label_map, do_shuffle=False, balance_label_examples=False): ''' Generate a Dataloader given the input examples, eventually masked if they are to be considered NOT labeled. ''' examples = [] #...

```python generator_find_param = NeuralNetClassifier( module=Generator1, criterion=torch.nn.CrossEntropyLoss(ignore_index=-1), train_split=None, max_epochs=10, batch_size=32, module__noise_size=100, module__output_size=hidden_size, module__hidden_sizes=hidden_levels_g, module__dropout_rate=out_dropout_rate, ) kf = KFold(n_splits=5, shuffle=True, random_state=42) generator_param_grid = { 'module__noise_size': [100, 200, 300, 400, 500, 600, 700,...

Error Message File c:\Users\User\.virtualenvs\testNewTransformer-s5V1e_yt\lib\site-packages\sklearn\base.py:1151, in _fit_context..decorator..wrapper(estimator, *args, **kwargs) 1144 estimator._validate_params() 1146 with config_context( 1147 skip_parameter_validation=( 1148 prefer_skip_nested_validation or global_skip_validation 1149 ) 1150 ): -> 1151 return fit_method(estimator, *args, **kwargs) File...

x_train_dataset=generate_data_set(labeled_examples,x_train_label_masks, label_map, do_shuffle = False, balance_label_examples = apply_balance)

**OK,Thanks for your help.** I will take your advice. This is a good suggestion.