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41c559415a
* Add GPT2ForTokenClassification * Fix dropout exception for GPT2 NER * Remove sequence label in test * Change TokenClassifierOutput to TokenClassifierOutputWithPast * Fix for black formatter * Remove dummy * Update docs for GPT2ForTokenClassification * Fix check_inits ci fail * Update dummy_pt_objects after make fix-copies * Remove TokenClassifierOutputWithPast * Fix tuple input issue Co-authored-by: danielsejong55@gmail.com <danielsejong55@gmail.com>
163 lines
6.6 KiB
ReStructuredText
163 lines
6.6 KiB
ReStructuredText
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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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OpenAI GPT2
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-----------------------------------------------------------------------------------------------------------------------
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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OpenAI GPT-2 model was proposed in `Language Models are Unsupervised Multitask Learners
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<https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf>`_ by Alec
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Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever. It's a causal (unidirectional)
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transformer pretrained using language modeling on a very large 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] of 8 million
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web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous words within some
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text. The diversity of the dataset causes this simple goal to contain naturally occurring demonstrations of many tasks
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across diverse domains. GPT-2 is a direct scale-up of GPT, with more than 10X the parameters and trained on more than
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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 the right rather than
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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 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` 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. See
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`reusing the past in generative models <../quickstart.html#using-the-past>`__ for more information on the usage of
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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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This model was contributed by `thomwolf <https://huggingface.co/thomwolf>`__. The original code can be found `here
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<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.models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput
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:members:
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.. autoclass:: transformers.models.gpt2.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: forward, parallelize, deparallelize
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GPT2LMHeadModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.GPT2LMHeadModel
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:members: forward, parallelize, deparallelize
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GPT2DoubleHeadsModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.GPT2DoubleHeadsModel
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:members: forward
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GPT2ForSequenceClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.GPT2ForSequenceClassification
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:members: forward
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GPT2ForTokenClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.GPT2ForTokenClassification
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:members: forward
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TFGPT2Model
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFGPT2Model
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:members: call
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TFGPT2LMHeadModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFGPT2LMHeadModel
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:members: call
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TFGPT2DoubleHeadsModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFGPT2DoubleHeadsModel
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:members: call
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TFGPT2ForSequenceClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFGPT2ForSequenceClassification
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:members: call
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TFSequenceClassifierOutputWithPast
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_tf_outputs.TFSequenceClassifierOutputWithPast
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:members:
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FlaxGPT2Model
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.FlaxGPT2Model
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:members: __call__
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FlaxGPT2LMHeadModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.FlaxGPT2LMHeadModel
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:members: __call__
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