This test checks if the NLP model can handle input text with added context, such as a greeting or closing.
To test QA models, we are using QAEval from Langchain where we need to use the model itself or other ML model for evaluation, which can make mistakes.
add_context: min_pass_rate: 0.65 prob: 0.5 # Defaults to 1.0, which means all words will be transformed. parameters: ending_context: ['Bye', 'Reported'] starting_context: ['Hi', 'Good morning', 'Hello'] count: 1 # Defaults to 1
You can adjust the level of transformation in the sentence by using the “
prob” parameter, which controls the proportion of words to be changed during
- min_pass_rate (float): Minimum pass rate to pass the test.
- starting_context (<List[str]>): Phrases to be added at the start of inputs.
- ending_context (<List[str]>): Phrases to be added at the end of inputs.
- prob (float): Controls the proportion of words to be changed.
- count (int): Number of variations of sentence to be constructed.
|The quick brown fox jumps over the lazy dog.||The quick brown fox jumps over the lazy dog, bye.|
|I love playing football.||Hello, I love playing football.|