"""Train loop for models. Iterates over epochs and batches and gives inputs to model. After training, call evaluation.py for evaluation of finetuned model.
Params:
model: model out of models.py
name: str
train_dataset: Dataset
test_dataset: Dataset
seed: int
batch_size:
test_batch_size:
num_epochs: int
imdb: bool
mixup: bool
lambda_value: float
mixepoch:int
tmix: bool
mixlayer: int in {0, 11}
learning_rate: float
mlp_leaning_rate:float
model: model out of models.py ->WordClassificationModel, BertForWordClassification or RobertaForWordClassification
name: str -> specifies architecture of model (either bert-base-uncased or roberta-base)
train_dataset: Dataset -> Train dataset as Torch.Dataset Object (created in preprocess.py)
test_dataset: Dataset ->Test dataset as Torch.Dataset Object (created in preprocess.py)
seed: int -> Random seed
batch_size: ->batch size for training
test_batch_size: -> batch size for testing
num_epochs: int -> number of epochs
imdb: bool ->whether or not imdb dataset is used
mixup: bool ->whether or not to use mixup in training
lambda_value: float ->if mixup or tmix selected, what lambda value to use
mixepoch:int -> specifies in what epoch to use mixup
tmix: bool ->whether or not tmix is used in training (used to differentiate between mixing in training and not mixing in evaluation)
mixlayer: int in {0, 11} ->what layer to mix in tmix
learning_rate: float ->learning rate for Bert/Roberta Model, or WordClassificationModel including linear classifier
mlp_leaning_rate:float ->separate learning rate for multi layer perceptron
Returns: Evaluation Results for train and test dataset in Accuracy, F1, Precision and Recall"""
