Text-to-text Finetuning

HappyTextToText contains two methods for training

  • train(): fine-tune the model to convert a standalone text to another standalone piece of text
  • eval(): determines how well the model performs



  1. input_filepath (string): a path file to a csv file as shown in table 7.1
  2. args (TTTrainArgs): a dataclass with the same fields types as shown in Table 7.2.
  3. eval_filepath (string): By default, an evaluating dataset will be generated from the supplied training data. But, you may provide a filepath to a CSV file as described for input_filepath to use standalone evaluating data.

Table 7.1

Contains two columns with the following header values: input and target

input target
grammar: I has poor grammars I have poor grammar
grammar: I wants too plays I want to play

Table 7.2

Parameter Default
learning_rate 5e-5
num_train_epochs 1
batch_size 1
preprocessing_processes 1
save_path ””
load_path ””
max_input_length None
max_output_length None
fp16 False
eval_ratio 0.1
save_steps 0.0
eval_steps 0.1
logging_steps 0.1
output_dir “happy_transformer”

Information about the learning parameters can be found here

preprocessing_processes: Number of processes used for preprocessing. We recommend 1-4. max_input_length: The maximum number of tokens for the input. The rest get truncated. By default the maximum number of tokens the model can handle is used. max_output_length: Ditto, except for the output.

Example 7.3:

from happytransformer import HappyTextToText, TTTrainArgs
# --------------------------------------#
happy_tt = HappyTextToText()
args = TTTrainArgs(num_train_epochs=1) 
happy_tt.train("../../data/tt/train-eval-grammar.csv", args=args)



  1. input_filepath (string): a path file to a csv file with the same format as described for the training data in table 7.1
  2. args (TTEvalArgs): a dataclass with the same fields shown in Table 7.3

Table 7.3

Parameter Default
preprocessing_processes 1
max_input_length 1024
max_output_length 1024

See Table 7.1 for more information

Output: An object with a single field called “loss”

Example 1.4

from happytransformer import HappyTextToText, TTEvalArgs
# --------------------------------------#
happy_tt = HappyTextToText()
args = TTEvalArgs(preprocessing_processes=1)
result = happy_tt.eval("../../data/tt/train-eval-grammar.csv", args=args)
print(type(result))  # <class 'happytransformer.happy_trainer.EvalResult'>
print(result)  # EvalResult(loss=3.2277376651763916)
print(result.loss)  # 3.2277376651763916


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