interfaces

Supported Interfaces

The following is a list of currently supported interfaces along-with sample transformations. (Note that the same interfaces can be used for creating new filters by implementing ".filter()" instead of ".generate()")

Interface	Description	Tasks	Example Transformation	Default Evaluation Models & Datasets
SentenceOperation	Expects a text as input and return of a transformed piece of text.	Text Classification, T2T Generation	BackTranslation	("aychang/roberta-base-imdb", "imdb")
SentenceAndTargetOperation	Expects a source and a target text as inputs and return of their transformations.	Text Classification, T2T Generation	ChangeTwoWayNamedEntities	("sshleifer/distilbart-xsum-12-6", "xsum")
KeyValuePairsOperation	Expects key-value pairs as input and returns a piece of text as output.	AMR-to-Text, E2E Task	-----
RDFOperation	Expects an RDF triplet as input and returns a piece of RDF triplet as output.	RDF-to-Text Generation	-----
QuestionAnswerOperation	Expects a question answering example as input and returns its transformation.	QA, QG	RedundantContextForQa	("mrm8488/bert-tiny-5-finetuned-squadv2", "squad")
TaggingOperation	Expects a list of tokena and a list of tags as input and returns its transformation.	Tagging	LongerNamesNer	("dslim/bert-base-NER", "conll2003")

We also welcome pull-requests of newer interfaces. To add a new interface, follow the below steps:

1. Create a new python file - "YourInterface.py" in the interfaces folder
2. Inside this python file, define a class with the appropriate inputs for the generate and the filter functions.

   from interfaces.Operation import Operation
   
   class YourInterface(Operation):

   A good idea would be to look at existing interfaces like SentenceOperation and QuestionAnswerOperation
3. [Optional] Now, you can create a transformation and a filter corresponding to YourInterface with the usual steps mentioned on the main page
4. [Optional] To gauge the effectiveness of your transformation, you can call a HuggingFace model and evaluate it over a HuggingFace dataset with the following command

   python evaluate.py -t NameOfTransformationClass

   That's it! You can now measure your transformation with this simple command!