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90 lines
4.0 KiB
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90 lines
4.0 KiB
Markdown
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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*This model was published in HF papers on 2019-09-11 and contributed to Hugging Face Transformers on 2020-11-16.*
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<div style="float: right;">
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<div class="flex flex-wrap space-x-1">
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<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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</div>
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# CTRL
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## Overview
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CTRL model was proposed in [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://huggingface.co/papers/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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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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## Usage 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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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 [`~CTRLModel#forward`]
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method for more information on the usage of this argument.
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## 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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