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81 lines
3.7 KiB
ReStructuredText
81 lines
3.7 KiB
ReStructuredText
CTRL
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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CTRL model was proposed in `CTRL: A Conditional Transformer Language Model for Controllable Generation
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<https://arxiv.org/abs/1909.05858>`_ by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and
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Richard Socher. It's a causal (unidirectional) transformer pre-trained using language modeling on a very large corpus
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of ~140 GB of text data with the first token reserved as a control code (such as Links, Books, Wikipedia etc.).
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The abstract from the paper is the following:
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*Large-scale language models show promising text generation capabilities, but users cannot easily control particular
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aspects of the generated text. We release CTRL, a 1.63 billion-parameter conditional transformer language model,
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trained to condition on control codes that govern style, content, and task-specific behavior. Control codes were
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derived from structure that naturally co-occurs with raw text, preserving the advantages of unsupervised learning while
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providing more explicit control over text generation. These codes also allow CTRL to predict which parts of the
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training data are most likely given a sequence. This provides a potential method for analyzing large amounts of data
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via model-based source attribution.*
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Tips:
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- CTRL makes use of control codes to generate text: it requires generations to be started by certain words, sentences
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or links to generate coherent text. Refer to the `original implementation <https://github.com/salesforce/ctrl>`__ for
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more information.
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- CTRL is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
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the left.
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- CTRL was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
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token in a sequence. Leveraging this feature allows CTRL to generate syntactically coherent text as it can be
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observed in the `run_generation.py` example script.
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- The PyTorch models can take the `past` as input, which is the previously computed key/value attention pairs. Using
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this `past` value prevents the model from re-computing pre-computed values in the context of text generation. See
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`reusing the past in generative models <../quickstart.html#using-the-past>`__ for more information on the usage of
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this argument.
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The original code can be found `here <https://github.com/salesforce/ctrl>`__.
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CTRLConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.CTRLConfig
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:members:
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CTRLTokenizer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.CTRLTokenizer
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:members: save_vocabulary
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CTRLModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.CTRLModel
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:members: forward
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CTRLLMHeadModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.CTRLLMHeadModel
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:members: forward
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TFCTRLModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFCTRLModel
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:members: call
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TFCTRLLMHeadModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFCTRLLMHeadModel
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:members: call
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