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42 lines
1.9 KiB
Python
42 lines
1.9 KiB
Python
""".. _models:
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Models
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=========
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TextAttack can attack any model that takes a list of strings as input and outputs a list of predictions. This is the idea behind *model wrappers*: to help your model conform to this API, we've provided the ``textattack.models.wrappers.ModelWrapper`` abstract class.
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We've also provided implementations of model wrappers for common patterns in some popular machine learning frameworks:
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Models User-specified
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--------------------------
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TextAttack allows users to provide their own models for testing. Models can be loaded in three ways:
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1. ``--model`` for pre-trained models and models trained with TextAttack
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2. ``--model-from-huggingface`` which will attempt to load any model from the ``HuggingFace model hub <https://huggingface.co/models>``
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3. ``--model-from-file`` which will dynamically load a Python file and look for the ``model`` variable
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Models Pre-trained
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--------------------------
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TextAttack also provides lots of pre-trained models for common tasks. Testing different attacks on the same model ensures attack comparisons are fair.
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Any of these models can be provided to ``textattack attack`` via ``--model``, for example, ``--model bert-base-uncased-mr``. For a full list of pre-trained models, see the `pre-trained models README <https://github.com/QData/TextAttack/tree/master/textattack/models>`_.
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Model Wrappers
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--------------------------
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TextAttack can attack any model that takes a list of strings as input and outputs a list of predictions. This is the idea behind *model wrappers*: to help your model conform to this API, we've provided the ``textattack.models.wrappers.ModelWrapper`` abstract class.
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We've also provided implementations of model wrappers for common patterns in some popular machine learning frameworks: including pytorch / sklearn / tensorflow.
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"""
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from . import helpers
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from . import tokenizers
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from . import wrappers
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