Random Deletion Layer - Data Augmentation

I’ve created this issue to specifically discuss the Random Deletion Layer while we figure out how to incorporate WordNet for Synonym Replacement

I’ve adapted the design mentioned by @mattdangerw here for the same

class RandomDeletion(keras.layers.Layer):
    """Augments input by randomly deleting words

    Args:
        probability: probability of a word being chosen for deletion
        max_replacements: The maximum number of words to replace
        stop_word_only: Only deletes stopwords

    Examples:

    Basic usage.
    >>> augmenter = keras_nlp.layers.RandomDeletion(
    ...     probability = 0.3,
    ... )
    >>> augmenter(["dog dog dog dog dog"])
    <tf.Tensor: shape=(), dtype=string, numpy=b'dog dog dog dog'>
    """
    pass

[mattdangerw]

@aflah02 that makes sense to me. In that case, let’s call this RandomWordDeletion, it’s more specific to what we are doing here, and leave us room to grow our offering.

[mattdangerw]

@aflah02 yeah, definitely we need to support the bached 2D case at a minimum. Potentially this could be done with RaggedTensor and no map function? Something like…

inputs = tf.constant(["this is a test", "this is another test"])
ragged_words = tf.strings.split(inputs)
mask = tf.random.uniform(ragged_words.flat_values.shape) > 0.25
mask = ragged_words.with_flat_values(mask)
deleted = tf.ragged.boolean_mask(ragged_words, mask)
deleted = tf.strings.reduce_join(deleted, axis=-1, separator=" ")

Would that work?