Custom Prefixes for data column and few shot column for prompt #45
Description
Currently generate_data_for_columnand fewshot_example_columns are used as the prefixs for the prompt.
E.g.:
from datasets import Dataset
from ai_dataset_generator.prompts import BasePrompt
fewshot_examples = Dataset.from_dict({
"text": ["This movie is great!", "This movie is bad!"],
"label": ["positive", "negative"]
})
prompt_template = BasePrompt(
task_description="Annotate movie reviews as either: {label_options}",
label_options=["positive", "negative"],
generate_data_for_column="label",
fewshot_example_columns="text",
)Has Output:
Annotate movie reviews as either: positive, negative
text: This movie is great!
label: positive
text: This movie is bad!
label: negative
text: {text}
label:
With text: and label: as the prefixes.
Proposal/Motivation
What if I use a custom fine-tuned model, that does not work well with textand label as prefixes in the prompt, but was trained with sentence and prediction.
For more flexibility, those prefixes should be optionally configurable. For example:
from datasets import Dataset
from ai_dataset_generator.prompts import BasePrompt
fewshot_examples = Dataset.from_dict({
"text": ["This movie is great!", "This movie is bad!"],
"label": ["positive", "negative"]
})
prompt_template = BasePrompt(
task_description="Annotate movie reviews as either: {label_options}",
label_options=["positive", "negative"],
generate_data_for_column=("label", "sentence"), # Second tuple item contains the new prefix string
fewshot_example_columns=("text", "prediction"), # Second tuple item contains the new prefix string
)Has Output:
Annotate movie reviews as either: positive, negative
sentence: This movie is great!
prediction: positive
sentence: This movie is bad!
prediction: negative
sentence: {text}
prediction:
The default behaviour could stay the same and the column name is used as the prefix. If it is a tuple (or other structure) then the second item is used.