Text Generation Finetuning

HappyTextGeneration contains two methods for training

• train(): fine-tune the model to understand a body of text better
• eval(): determine how well the model performs

train()

inputs:

1. input_filepath (string): a path file to a text file that contains nothing but text to train the model.
2. args (GENTrainArgs): a dataclass with the same fields types as shown in Table 1.1.

Table 1.1

Information about the learning parameters can be found here

Information about saving/loading preprocessed data can be found here

preprocessing_processes: Number of processes used for preprocessing. We recommend 1-4.

Example 1.3:

from happytransformer import HappyGeneration, GENTrainArgs
# --------------------------------------#

happy_gen = HappyGeneration()
args = GENTrainArgs(num_train_epochs=1) 
happy_gen.train("../../data/gen/train-eval.txt", args=args)

eval()

Input:

1. input_filepath (string): a path file to a text file with just text to evaluate
2. args (WPEvalArgs): a dataclass with the same fields shown in Table 1.2

Table 1.2

See the explanations under Table 1.1 for more information

Output: An object with the field “loss”

Example 1.4

from happytransformer import HappyGeneration, GENEvalArgs
# --------------------------------------#
happy_gen = HappyGeneration()  
args = GENEvalArgs(preprocessing_processes=2)
result = happy_gen.eval("../../data/gen/train-eval.txt", args=args)
print(type(result))  # <class 'happytransformer.happy_trainer.EvalResult'>
print(result)  # EvalResult(loss=3.3437771797180176)
print(result.loss)  # 3.3437771797180176