readme: ---
  language: en
  tags:
  - text-generation
  
  license: mit
  ---
  
  
  # GPT-2
  
  Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
  
  Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
  [this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
  and first released at [this page](https://openai.com/blog/better-language-models/).
  
  Disclaimer: The team releasing GPT-2 also wrote a
  [model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. Content from this model card
  has been written by the Hugging Face team to complete the information they provided and give specific examples of bias.
  
  ## Model description
  
  GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This
  means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots
  of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
  it was trained to guess the next word in sentences.
  
  More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
  shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
  predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
  
  This way, the model learns an inner representation of the English language that can then be used to extract features
  useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a
  prompt.
  
  ## Intended uses & limitations
  
  You can use the raw model for text generation or fine-tune it to a downstream task. See the
  [model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you.
  
  ### How to use
  
  You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
  set a seed for reproducibility:
  
  ```python
  >>> from transformers import pipeline, set_seed
  >>> generator = pipeline('text-generation', model='gpt2')
  >>> set_seed(42)
  >>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)
  [{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."},
   {'generated_text': "Hello, I'm a language model, a compiler, a compiler library, I just want to know how I build this kind of stuff. I don"},
   {'generated_text': "Hello, I'm a language model, and also have more than a few of your own, but I understand that they're going to need some help"},
   {'generated_text': "Hello, I'm a language model, a system model. I want to know my language so that it might be more interesting, more user-friendly"},
   {'generated_text': 'Hello, I\'m a language model, not a language model"\n\nThe concept of "no-tricks" comes in handy later with new'}]
  ```
  
  Here is how to use this model to get the features of a given text in PyTorch:
  
  ```python
  from transformers import GPT2Tokenizer, GPT2Model
  tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
  model = GPT2Model.from_pretrained('gpt2')
  text = "Replace me by any text you'd like."
  encoded_input = tokenizer(text, return_tensors='pt')
  output = model(**encoded_input)
  ```
  
  and in TensorFlow:
  
  ```python
  from transformers import GPT2Tokenizer, TFGPT2Model
  tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
  model = TFGPT2Model.from_pretrained('gpt2')
  text = "Replace me by any text you'd like."
  encoded_input = tokenizer(text, return_tensors='tf')
  output = model(encoded_input)
  ```
  
  ### Limitations and bias
  
  The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of
  unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their
  [model card](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases):
  
  > Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases
  > that require the generated text to be true.
  >
  > Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do
  > not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a
  > study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,
  > and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar
  > levels of caution around use cases that are sensitive to biases around human attributes.
  
  Here's an example of how the model can have biased predictions:
  
  ```python
  >>> from transformers import pipeline, set_seed
  >>> generator = pipel