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# CTRL |
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<div class="flex flex-wrap space-x-1"> |
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<a href="https://huggingface.co/models?filter=ctrl"> |
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<img alt="Models" src="https://img.shields.io/badge/All_model_pages-ctrl-blueviolet"> |
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</a> |
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<a href="https://huggingface.co/spaces/docs-demos/tiny-ctrl"> |
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<img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue"> |
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</a> |
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</div> |
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## Overview |
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CTRL model was proposed in [CTRL: A Conditional Transformer Language Model for Controllable Generation](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_key_values` as input, which is the previously computed key/value attention pairs. |
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TensorFlow models accepts `past` as input. Using the `past_key_values` value prevents the model from re-computing |
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pre-computed values in the context of text generation. See the [`forward`](model_doc/ctrl#transformers.CTRLModel.forward) |
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method for more information on the usage of this argument. |
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This model was contributed by [keskarnitishr](https://huggingface.co/keskarnitishr). The original code can be found |
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[here](https://github.com/salesforce/ctrl). |
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## Documentation resources |
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- [Text classification task guide](../tasks/sequence_classification) |
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- [Causal language modeling task guide](../tasks/language_modeling) |
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## CTRLConfig |
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[[autodoc]] CTRLConfig |
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## CTRLTokenizer |
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[[autodoc]] CTRLTokenizer |
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- save_vocabulary |
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## CTRLModel |
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[[autodoc]] CTRLModel |
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- forward |
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## CTRLLMHeadModel |
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[[autodoc]] CTRLLMHeadModel |
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- forward |
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## CTRLForSequenceClassification |
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[[autodoc]] CTRLForSequenceClassification |
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- forward |
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## TFCTRLModel |
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[[autodoc]] TFCTRLModel |
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- call |
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## TFCTRLLMHeadModel |
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[[autodoc]] TFCTRLLMHeadModel |
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- call |
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## TFCTRLForSequenceClassification |
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[[autodoc]] TFCTRLForSequenceClassification |
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- call |
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