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docs/source/en/model_doc/ctrl.mdx

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+<!--Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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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
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# CTRL
+
+## Overview
+
+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
+Richard Socher. It's a causal (unidirectional) transformer pre-trained using language modeling on a very large corpus
+of ~140 GB of text data with the first token reserved as a control code (such as Links, Books, Wikipedia etc.).
+
+The abstract from the paper is the following:
+
+*Large-scale language models show promising text generation capabilities, but users cannot easily control particular
+aspects of the generated text. We release CTRL, a 1.63 billion-parameter conditional transformer language model,
+trained to condition on control codes that govern style, content, and task-specific behavior. Control codes were
+derived from structure that naturally co-occurs with raw text, preserving the advantages of unsupervised learning while
+providing more explicit control over text generation. These codes also allow CTRL to predict which parts of the
+training data are most likely given a sequence. This provides a potential method for analyzing large amounts of data
+via model-based source attribution.*
+
+Tips:
+
+- CTRL makes use of control codes to generate text: it requires generations to be started by certain words, sentences
+  or links to generate coherent text. Refer to the [original implementation](https://github.com/salesforce/ctrl) for
+  more information.
+- CTRL is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
+  the left.
+- CTRL was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
+  token in a sequence. Leveraging this feature allows CTRL to generate syntactically coherent text as it can be
+  observed in the *run_generation.py* example script.
+- The PyTorch models can take the `past_key_values` as input, which is the previously computed key/value attention pairs.
+  TensorFlow models accepts `past` as input. Using the `past_key_values` value prevents the model from re-computing
+  pre-computed values in the context of text generation. See the [`forward`](model_doc/ctrl#transformers.CTRLModel.forward)
+  method for more information on the usage of this argument.
+
+This model was contributed by [keskarnitishr](https://huggingface.co/keskarnitishr). The original code can be found
+[here](https://github.com/salesforce/ctrl).
+
+
+## CTRLConfig
+
+[[autodoc]] CTRLConfig
+
+## CTRLTokenizer
+
+[[autodoc]] CTRLTokenizer
+    - save_vocabulary
+
+## CTRLModel
+
+[[autodoc]] CTRLModel
+    - forward
+
+## CTRLLMHeadModel
+
+[[autodoc]] CTRLLMHeadModel
+    - forward
+
+## CTRLForSequenceClassification
+
+[[autodoc]] CTRLForSequenceClassification
+    - forward
+
+## TFCTRLModel
+
+[[autodoc]] TFCTRLModel
+    - call
+
+## TFCTRLLMHeadModel
+
+[[autodoc]] TFCTRLLMHeadModel
+    - call
+
+## TFCTRLForSequenceClassification
+
+[[autodoc]] TFCTRLForSequenceClassification
+    - call