
OpenAI GPT2

Overview
OpenAI GPT-2 model was proposed in Language Models are Unsupervised Multitask Learners by Alec
Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever from OpenAI. It's a causal (unidirectional)
transformer pretrained using language modeling on a very large corpus of ~40 GB of text data.
The abstract from the paper is the following:
GPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset[1] of 8 million
web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous words within some
text. The diversity of the dataset causes this simple goal to contain naturally occurring demonstrations of many tasks
across diverse domains. GPT-2 is a direct scale-up of GPT, with more than 10X the parameters and trained on more than
10X the amount of data.
Write With Transformer is a webapp created and hosted by
Hugging Face showcasing the generative capabilities of several models. GPT-2 is one of them and is available in five
different sizes: small, medium, large, xl and a distilled version of the small checkpoint: distilgpt-2.
This model was contributed by thomwolf. The original code can be found here.
Usage tips

GPT-2 is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
  the left.
GPT-2 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 GPT-2 to generate syntactically coherent text as it can be
  observed in the run_generation.py example script.
The model can take the past_key_values (for PyTorch) or past (for TF) as input, which is the previously computed
  key/value attention pairs. Using this (past_key_values or past) value prevents the model from re-computing
  pre-computed values in the context of text generation. For PyTorch, see past_key_values argument of the
  [GPT2Model.forward] method, or for TF the past argument of the
  [TFGPT2Model.call] method for more information on its usage.
Enabling the scale_attn_by_inverse_layer_idx and reorder_and_upcast_attn flags will apply the training stability
  improvements from Mistral (for PyTorch only).

Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with GPT2. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.

A blog on how to Finetune a non-English GPT-2 Model with Hugging Face.
A blog on How to generate text: using different decoding methods for language generation with Transformers with GPT-2.
A blog on Training CodeParrot 🦜 from Scratch, a large GPT-2 model.
A blog on Faster Text Generation with TensorFlow and XLA with GPT-2.
A blog on How to train a Language Model with Megatron-LM with a GPT-2 model.
A notebook on how to finetune GPT2 to generate lyrics in the style of your favorite artist. 🌎
A notebook on how to finetune GPT2 to generate tweets in the style of your favorite Twitter user. 🌎
Causal language modeling chapter of the 🤗 Hugging Face Course.
[GPT2LMHeadModel] is supported by this causal language modeling example script, text generation example script, and notebook.
[TFGPT2LMHeadModel] is supported by this causal language modeling example script and notebook.
[FlaxGPT2LMHeadModel] is supported by this causal language modeling example script and notebook.
Text classification task guide
Token classification task guide
Causal language modeling task guide

GPT2Config
[[autodoc]] GPT2Config
GPT2Tokenizer
[[autodoc]] GPT2Tokenizer
    - save_vocabulary
GPT2TokenizerFast
[[autodoc]] GPT2TokenizerFast
GPT2 specific outputs
[[autodoc]] models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput
[[autodoc]] models.gpt2.modeling_tf_gpt2.TFGPT2DoubleHeadsModelOutput

GPT2Model
[[autodoc]] GPT2Model
    - forward
GPT2LMHeadModel
[[autodoc]] GPT2LMHeadModel
    - forward
GPT2DoubleHeadsModel
[[autodoc]] GPT2DoubleHeadsModel
    - forward
GPT2ForQuestionAnswering
[[autodoc]] GPT2ForQuestionAnswering
    - forward
GPT2ForSequenceClassification
[[autodoc]] GPT2ForSequenceClassification
    - forward
GPT2ForTokenClassification
[[autodoc]] GPT2ForTokenClassification
    - forward

TFGPT2Model
[[autodoc]] TFGPT2Model
    - call
TFGPT2LMHeadModel
[[autodoc]] TFGPT2LMHeadModel
    - call
TFGPT2DoubleHeadsModel
[[autodoc]] TFGPT2DoubleHeadsModel
    - call
TFGPT2ForSequenceClassification
[[autodoc]] TFGPT2ForSequenceClassification
    - call
TFSequenceClassifierOutputWithPast
[[autodoc]] modeling_tf_outputs.TFSequenceClassifierOutputWithPast
TFGPT2Tokenizer
[[autodoc]] TFGPT2Tokenizer

FlaxGPT2Model
[[autodoc]] FlaxGPT2Model
    - call
FlaxGPT2LMHeadModel
[[autodoc]] FlaxGPT2LMHeadModel
    - call

