Added Transformer support

docs/reference/node_reference.md

@@ -157,6 +157,7 @@
 | [Ptn Conv 2d](ptn_conv2d.md) |     A convolutional model consisting of a single conv2d layer.   |
 | [Ptn Conv Model](ptn_conv_model.md) | A convolutional model consisting of multiple convolutional layers.   |
 | [Ptn Embedding](ptn_embedding.md) | Constructs an embedding layer. |
+| [Ptn Embedding Transformer Linear](ptn_embedding_transformer_linear.md) | A Transformer encoder model with a linear head. |
 | [Ptn GRU](ptn_gru.md) | A gated recurrent unit (GRU) model consisting of one or more of a recurrent layer.   |
 | [Ptn GRU Linear](ptn_gru_linear.md) | A recurrent neural network (GRU) model with a linear head. |
 | [Ptn Instance Norm 2d](ptn_instance_norm2d.md) | A normalization model to normalize elements over spatial axes within each channel for each sample. |
@@ -234,6 +235,7 @@
 | [Pt Predict Regression Model](pt_predict_regression_model.md) | Performs inference on input data. |
 | [Pt Train Classification Model](pt_train_classification_model.md) | Trains a classification model using a given dataset, optimizer, and number of epochs.   |
 | [Pt Train Classification Model Lr](pt_train_classification_model_lr.md) | Trains a classification model using a given dataset, optimizer, and number of epochs with learning rate decay.   |
+| [Pt Train Classification Transformer Model](pt_train_classification_transformer_model.md) | Trains a classification Transformer model using a given dataset, loss function, optimizer, and number of epochs with learning rate decay.   |
 | [Pt Train Model](pt_train_model.md) | Trains a model using a given dataset, loss function, optimizer, and number of epochs with learning rate decay.   |
 | [Pt Train RNN Model](pt_train_rnn_model.md) | Trains an RNN model using a given dataset, loss function, optimizer, and number of epochs with learning rate decay.   |
 | [Pt Train Regression Model](pt_train_regression_model.md) | Trains a regression model using a given dataset, optimizer, and number of epochs.   |

docs/reference/pt_train_classification_transformer_model.md

@@ -0,0 +1,44 @@
+# Pt Train Classification Transformer Model
+Trains a classification Transformer model using a given dataset, loss function, optimizer, and number of epochs with learning rate decay.  
+
+   Args:  
+        model (PTMODEL): The PyTorch model to be trained.  
+        train_loader (torch.utils.data.DataLoader): DataLoader containing the training dataset.  
+        optimizer (PTOPTIMIZER): Optimizer used for training.  
+        loss (PTLOSS): The loss class.  
+        epochs (int): Number of training epochs.  
+        use_gpu (bool): Whether to use GPU for training.  
+        early_stopping (bool): Whether to turn on early stopping  
+        early_stopping_rounds (int): Specifies the number of rounds to monitor the evaluation loss when early_stopping is enabled and validation data is specified. If the loss does not decrease compared to the current best loss within this period, training will be stopped.  
+        output_best_val_model (bool): Whether to outputs the model with the best val loss or the model from the last epoch.  
+        classification_metrics (bool): If True and if val_loader is set, print out classification metrics.  This is only valid for classification model training.  
+        val_loader (PTDATALOADER): DataLoader for validation dataset (optional).  
+        scheduler (PTLRSCHEDULER): Learning rate scheduler.
+
+## Input
+| Name | Data type |
+|---|---|
+| model | Ptmodel |
+| train_loader | Ptdataloader |
+| optimizer | Ptoptimizer |
+| loss_function | Ptloss |
+| epochs | Int |
+| use_gpu | Boolean |
+| early_stopping | Boolean |
+| early_stopping_rounds | Int |
+| output_best_val_model | Boolean |
+| classification_metrics | Boolean |
+| scheduler | Ptlrscheduler |
+| val_loader | Ptdataloader |
+
+## Output
+| Data type |
+|---|
+| Ptmodel |
+| Tensor |
+| Tensor |
+
+<HR>
+Category: PyTorch wrapper - Training
+
+ComfyUI Pt Wrapper Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.

docs/reference/ptn_embedding_transformer_linear.md

@@ -0,0 +1,46 @@
+# Ptn Embedding Transformer Linear
+A Transformer encoder model with a linear head.
+
+    Args:
+        num_layers (int): Number of encoder layers in Transformer.
+        vocabulary_size (int): The vocabulary size of the tokens.
+        hidden_size (int): Hidden dimension (d_model) for each token of the Transformer.
+        nhead (int): Number of attention heads.
+        dim_feedforward (int): Feedforward dimension.
+        dropout (float): Dropout rate for Transformer layers.
+        nonlinearity (str): Activation function ('relu' or 'gelu') used in Transformer layers.
+        layer_norm_eps (float): Epsilon value for layer normalization.
+        batch_first (bool): Input shape format: True for (B, Seq, Feature), False for (Seq, B, Feature).
+        norm_first (bool): Whether to apply normalization before attention/feedforward.
+        bias (bool): Use bias in the Transformer layers.
+        max_length (int): Maximum input sequence length (for positional embedding).
+        linear_output_size (int): Output size of the final linear layer.
+        linear_bias (bool): Use bias in the linear layer.
+
+## Input
+| Name | Data type |
+|---|---|
+| num_layers | Int |
+| vocabulary_size | Int |
+| hidden_size | Int |
+| nhead | Int |
+| dim_feedforward | Int |
+| dropout | Float |
+| nonlinearity |  |
+| layer_norm_eps | Float |
+| batch_first | Boolean |
+| norm_first | Boolean |
+| bias | Boolean |
+| max_length | Int |
+| linear_output_size | Int |
+| linear_bias | Boolean |
+
+## Output
+| Data type |
+|---|
+| Ptmodel |
+
+<HR>
+Category: PyTorch wrapper - Model
+
+ComfyUI Pt Wrapper Node Reference. © 2025 Hide Inada (HowToSD.com). All rights reserved.

docs/reference/ptv_hf_dataset_with_token_encode.md

@@ -7,6 +7,7 @@ A PyTorch Dataset class wrapper for a Hugging Face dataset that converts text to
         sample_field_name (str): Field name for text samples.
         label_field_name (str): Field name for labels.
         encode (PTCALLABLE): The reference to a token encoder function.
+        remove_html_tags (bool): Remove html tags in text if True.
 
 ## Input
 | Name | Data type |
@@ -16,6 +17,7 @@ A PyTorch Dataset class wrapper for a Hugging Face dataset that converts text to
 | sample_field_name | String |
 | label_field_name | String |
 | encode | Ptcallable |
+| remove_html_tags | Boolean |
 
 ## Output
 | Data type |