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111 lines
3.4 KiB
ReStructuredText
111 lines
3.4 KiB
ReStructuredText
OpenAI GPT2
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Overview
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~~~~~~~~~~~~~~~~~~~~~
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OpenAI GPT-2 model was proposed in
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`Language Models are Unsupervised Multitask Learners <https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf>`_
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by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
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It's a causal (unidirectional) transformer pre-trained using language modeling on a very large
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corpus of ~40 GB of text data.
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The abstract from the paper is the following:
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*GPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset[1]
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of 8 million web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous
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words within some text. The diversity of the dataset causes this simple goal to contain naturally occurring
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demonstrations of many tasks across diverse domains. GPT-2 is a direct scale-up of GPT, with more than 10X
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the parameters and trained on more than 10X the amount of data.*
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Tips:
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- GPT-2 is a model with absolute position embeddings so it's usually advised to pad the inputs on
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the right rather than the left.
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- GPT-2 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 GPT-2 to generate syntactically coherent text as
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it can be observed in the `run_generation.py` example script.
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- The PyTorch models can take the `past` as input, which is the previously computed key/value attention pairs. Using
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this `past` value prevents the model from re-computing pre-computed values in the context of text generation.
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See `reusing the past in generative models <../quickstart.html#using-the-past>`_ for more information on the usage
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of this argument.
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`Write With Transformer <https://transformer.huggingface.co/doc/gpt2-large>`__ is a webapp created and hosted by
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Hugging Face showcasing the generative capabilities of several models. GPT-2 is one of them and is available in five
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different sizes: small, medium, large, xl and a distilled version of the small checkpoint: distilgpt-2.
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The original code can be found `here <https://openai.com/blog/better-language-models/>`_.
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GPT2Config
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~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.GPT2Config
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:members:
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GPT2Tokenizer
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~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.GPT2Tokenizer
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:members: save_vocabulary
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GPT2TokenizerFast
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~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.GPT2TokenizerFast
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:members:
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GPT2 specific outputs
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~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_gpt2.GPT2DoubleHeadsModelOutput
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:members:
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.. autoclass:: transformers.modeling_tf_gpt2.TFGPT2DoubleHeadsModelOutput
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:members:
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GPT2Model
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~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.GPT2Model
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:members:
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GPT2LMHeadModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.GPT2LMHeadModel
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:members:
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GPT2DoubleHeadsModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.GPT2DoubleHeadsModel
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:members:
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TFGPT2Model
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~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFGPT2Model
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:members:
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TFGPT2LMHeadModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFGPT2LMHeadModel
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:members:
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TFGPT2DoubleHeadsModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFGPT2DoubleHeadsModel
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:members:
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