Funnel transformer (#6908)
* Initial model * Fix upsampling * Add special cls token id and test * Formatting * Test and fist FunnelTokenizerFast * Common tests * Fix the check_repo script and document Funnel * Doc fixes * Add all models * Write doc * Fix test * Initial model * Fix upsampling * Add special cls token id and test * Formatting * Test and fist FunnelTokenizerFast * Common tests * Fix the check_repo script and document Funnel * Doc fixes * Add all models * Write doc * Fix test * Fix copyright * Forgot some layers can be repeated * Apply suggestions from code review Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Update src/transformers/modeling_funnel.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Address review comments * Update src/transformers/modeling_funnel.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Address review comments * Update src/transformers/modeling_funnel.py Co-authored-by: Sam Shleifer <sshleifer@gmail.com> * Slow integration test * Make small integration test * Formatting * Add checkpoint and separate classification head * Formatting * Expand list, fix link and add in pretrained models * Styling * Add the model in all summaries * Typo fixes Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> Co-authored-by: Sam Shleifer <sshleifer@gmail.com>
This commit is contained in:
Sylvain Gugger
@@ -131,7 +131,10 @@ conversion utilities for the following models:
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25. `LXMERT <https://github.com/airsplay/lxmert>`_ (from UNC Chapel Hill) released with the paper `LXMERT: Learning
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Cross-Modality Encoder Representations from Transformers for Open-Domain Question
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Answering <https://arxiv.org/abs/1908.07490>`_ by Hao Tan and Mohit Bansal.
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26. `Other community models <https://huggingface.co/models>`_, contributed by the `community
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26. `Funnel Transformer <https://github.com/laiguokun/Funnel-Transformer>`_ (from CMU/Google Brain) released with the paper
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`Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing
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<https://arxiv.org/abs/2006.03236>`_ by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
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27. `Other community models <https://huggingface.co/models>`_, contributed by the `community
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<https://huggingface.co/users>`_.
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.. toctree::
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@@ -216,6 +219,7 @@ conversion utilities for the following models:
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model_doc/dpr
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model_doc/pegasus
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model_doc/mbart
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model_doc/funnel
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model_doc/lxmert
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internal/modeling_utils
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internal/tokenization_utils
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126
docs/source/model_doc/funnel.rst
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126
docs/source/model_doc/funnel.rst
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@@ -0,0 +1,126 @@
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Funnel Transformer
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------------------
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Overview
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~~~~~~~~~~~~~~~~~~~~~
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The Funnel Transformer model was proposed in the paper
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`Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing
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<https://arxiv.org/abs/2006.03236>`__.
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It is a bidirectional transformer model, like BERT, but with a pooling operation after each block of layers, a bit
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like in traditional convolutional neural networks (CNN) in computer vision.
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The abstract from the paper is the following:
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*With the success of language pretraining, it is highly desirable to develop more efficient architectures of good
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scalability that can exploit the abundant unlabeled data at a lower cost. To improve the efficiency, we examine the
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much-overlooked redundancy in maintaining a full-length token-level presentation, especially for tasks that only
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require a single-vector presentation of the sequence. With this intuition, we propose Funnel-Transformer which
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gradually compresses the sequence of hidden states to a shorter one and hence reduces the computation cost. More
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importantly, by re-investing the saved FLOPs from length reduction in constructing a deeper or wider model, we further
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improve the model capacity. In addition, to perform token-level predictions as required by common pretraining
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objectives, Funnel-Transformer is able to recover a deep representation for each token from the reduced hidden sequence
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via a decoder. Empirically, with comparable or fewer FLOPs, Funnel-Transformer outperforms the standard Transformer on
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a wide variety of sequence-level prediction tasks, including text classification, language understanding, and reading
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comprehension.*
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Tips:
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- Since Funnel Transformer uses pooling, the sequence length of the hidden states changes after each block of layers.
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The base model therefore has a final sequence length that is a quarter of the original one. This model can be used
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directly for tasks that just require a sentence summary (like sequence classification or multiple choice). For other
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tasks, the full model is used; this full model has a decoder that upsamples the final hidden states to the same
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sequence length as the input.
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- The Funnel Transformer checkpoints are all available with a full version and a base version. The first ones should
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be used for :class:`~transformers.FunnelModel`, :class:`~transformers.FunnelForPreTraining`,
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:class:`~transformers.FunnelForMaskedLM`, :class:`~transformers.FunnelForTokenClassification` and
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class:`~transformers.FunnelForQuestionAnswering`. The second ones should be used for
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:class:`~transformers.FunnelBaseModel`, :class:`~transformers.FunnelForSequenceClassification` and
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:class:`~transformers.FunnelForMultipleChoice`.
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The original code can be found `here <https://github.com/laiguokun/Funnel-Transformer>`_.
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FunnelConfig
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~~~~~~~~~~~~
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.. autoclass:: transformers.FunnelConfig
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:members:
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FunnelTokenizer
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~~~~~~~~~~~~~~~
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.. autoclass:: transformers.FunnelTokenizer
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:members: build_inputs_with_special_tokens, get_special_tokens_mask,
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create_token_type_ids_from_sequences, save_vocabulary
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FunnelTokenizerFast
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~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.FunnelTokenizerFast
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:members:
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Funnel specific outputs
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~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.modeling_funnel.FunnelForPreTrainingOutput
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:members:
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FunnelBaseModel
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~~~~~~~~~~~~~~~
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.. autoclass:: transformers.FunnelBaseModel
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:members:
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FunnelModel
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~~~~~~~~~~~
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.. autoclass:: transformers.FunnelModel
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:members:
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FunnelModelForPreTraining
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~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.FunnelForPreTraining
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:members:
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FunnelForMaskedLM
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~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.FunnelForMaskedLM
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:members:
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FunnelForSequenceClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.FunnelForSequenceClassification
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:members:
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FunnelForMultipleChoice
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~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.FunnelForMultipleChoice
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:members:
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FunnelForTokenClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.FunnelForTokenClassification
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:members:
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FunnelForQuestionAnswering
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~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.FunnelForQuestionAnswering
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:members:
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@@ -416,6 +416,38 @@ traditional GAN setting) then the ELECTRA model is trained for a few steps.
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The library provides a version of the model for masked language modeling, token classification and sentence
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classification.
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Funnel Transformer
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----------------------------------------------
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.. raw:: html
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<a href="https://huggingface.co/models?filter=funnel">
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<img alt="Models" src="https://img.shields.io/badge/All_model_pages-funnel-blueviolet">
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</a>
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<a href="model_doc/funnel.html">
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<img alt="Doc" src="https://img.shields.io/badge/Model_documentation-funnel-blueviolet">
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</a>
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`Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing
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<https://arxiv.org/abs/2006.03236>`_, Zihang Dai et al.
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Funnel Transformer is a transformer model using pooling, a bit like a ResNet model: layers are grouped in blocks, and
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at the beginning of each block (except the first one), the hidden states are pooled among the sequence dimension. This
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way, their length is divided by 2, which speeds up the computation of the next hidden states. All pretrained models
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have three blocks, which means the final hidden state has a sequence length that is one fourth of the original sequence
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length.
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For tasks such as classification, this is not a problem, but for tasks like masked language modeling or token
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classification, we need a hidden state with the same sequence length as the original input. In those cases, the final
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hidden states are upsampled to the input sequence length and go through two additional layers. That's why there are two
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versions of each checkpoint. The version suffixed with "-base" contains only the three blocks, while the version
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without that suffix contains the three blocks and the upsampling head with its additional layers.
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The pretrained models available use the same pretraining objective as ELECTRA.
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The library provides a version of the model for masked language modeling, token classification, sentence
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classification, multiple choice classification and question answering.
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.. _longformer:
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Longformer
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@@ -5,366 +5,406 @@ Here is the full list of the currently provided pretrained models together with
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For a list that includes community-uploaded models, refer to `https://huggingface.co/models <https://huggingface.co/models>`__.
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+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| Architecture | Shortcut name | Details of the model |
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+===================+============================================================+=======================================================================================================================================+
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| BERT | ``bert-base-uncased`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
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| | | | Trained on lower-cased English text. |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-large-uncased`` | | 24-layer, 1024-hidden, 16-heads, 340M parameters. |
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| | | | Trained on lower-cased English text. |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-base-cased`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
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| | | | Trained on cased English text. |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-large-cased`` | | 24-layer, 1024-hidden, 16-heads, 340M parameters. |
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| | | | Trained on cased English text. |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-base-multilingual-uncased`` | | (Original, not recommended) 12-layer, 768-hidden, 12-heads, 110M parameters. |
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| | | | Trained on lower-cased text in the top 102 languages with the largest Wikipedias |
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| | | |
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| | | (see `details <https://github.com/google-research/bert/blob/master/multilingual.md>`__). |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-base-multilingual-cased`` | | (New, **recommended**) 12-layer, 768-hidden, 12-heads, 110M parameters. |
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| | | | Trained on cased text in the top 104 languages with the largest Wikipedias |
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| | | |
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| | | (see `details <https://github.com/google-research/bert/blob/master/multilingual.md>`__). |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-base-chinese`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
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| | | | Trained on cased Chinese Simplified and Traditional text. |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-base-german-cased`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
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| | | | Trained on cased German text by Deepset.ai |
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| | | |
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| | | (see `details on deepset.ai website <https://deepset.ai/german-bert>`__). |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-large-uncased-whole-word-masking`` | | 24-layer, 1024-hidden, 16-heads, 340M parameters. |
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| | | | Trained on lower-cased English text using Whole-Word-Masking |
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| | | |
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| | | (see `details <https://github.com/google-research/bert/#bert>`__). |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-large-cased-whole-word-masking`` | | 24-layer, 1024-hidden, 16-heads, 340M parameters. |
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| | | | Trained on cased English text using Whole-Word-Masking |
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| | | |
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| | | (see `details <https://github.com/google-research/bert/#bert>`__). |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-large-uncased-whole-word-masking-finetuned-squad`` | | 24-layer, 1024-hidden, 16-heads, 340M parameters. |
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| | | | The ``bert-large-uncased-whole-word-masking`` model fine-tuned on SQuAD |
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| | | |
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| | | (see details of fine-tuning in the `example section <https://github.com/huggingface/transformers/tree/master/examples>`__). |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-large-cased-whole-word-masking-finetuned-squad`` | | 24-layer, 1024-hidden, 16-heads, 340M parameters |
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| | | | The ``bert-large-cased-whole-word-masking`` model fine-tuned on SQuAD |
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| | | |
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| | | (see `details of fine-tuning in the example section <https://huggingface.co/transformers/examples.html>`__) |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-base-cased-finetuned-mrpc`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
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| | | | The ``bert-base-cased`` model fine-tuned on MRPC |
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| | | |
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| | | (see `details of fine-tuning in the example section <https://huggingface.co/transformers/examples.html>`__) |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-base-german-dbmdz-cased`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
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| | | | Trained on cased German text by DBMDZ |
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| | | |
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| | | (see `details on dbmdz repository <https://github.com/dbmdz/german-bert>`__). |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``bert-base-german-dbmdz-uncased`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
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| | | | Trained on uncased German text by DBMDZ |
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| | | |
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| | | (see `details on dbmdz repository <https://github.com/dbmdz/german-bert>`__). |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``cl-tohoku/bert-base-japanese`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
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| | | | Trained on Japanese text. Text is tokenized with MeCab and WordPiece and this requires some extra dependencies, |
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| | | | `fugashi <https://github.com/polm/fugashi>`__ which is a wrapper around `MeCab <https://taku910.github.io/mecab/>`__. |
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| | | | Use ``pip install transformers["ja"]`` (or ``pip install -e .["ja"]`` if you install from source) to install them. |
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| | | |
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| | | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__). |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``cl-tohoku/bert-base-japanese-whole-word-masking`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
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| | | | Trained on Japanese text. Text is tokenized with MeCab and WordPiece and this requires some extra dependencies, |
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| | | | `fugashi <https://github.com/polm/fugashi>`__ which is a wrapper around `MeCab <https://taku910.github.io/mecab/>`__. |
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| | | | Use ``pip install transformers["ja"]`` (or ``pip install -e .["ja"]`` if you install from source) to install them. |
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| | | |
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| | | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__). |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``cl-tohoku/bert-base-japanese-char`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
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| | | | Trained on Japanese text. Text is tokenized into characters. |
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| | | |
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| | | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__). |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``cl-tohoku/bert-base-japanese-char-whole-word-masking`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
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| | | | Trained on Japanese text using Whole-Word-Masking. Text is tokenized into characters. |
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| | | |
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| | | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__). |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``TurkuNLP/bert-base-finnish-cased-v1`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
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| | | | Trained on cased Finnish text. |
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| | | |
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| | | (see `details on turkunlp.org <http://turkunlp.org/FinBERT/>`__). |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``TurkuNLP/bert-base-finnish-uncased-v1`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
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| | | | Trained on uncased Finnish text. |
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| | | |
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| | | (see `details on turkunlp.org <http://turkunlp.org/FinBERT/>`__). |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``wietsedv/bert-base-dutch-cased`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
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| | | | Trained on cased Dutch text. |
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| | | |
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| | | (see `details on wietsedv repository <https://github.com/wietsedv/bertje/>`__). |
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+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| GPT | ``openai-gpt`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
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| | | | OpenAI GPT English model |
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+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| GPT-2 | ``gpt2`` | | 12-layer, 768-hidden, 12-heads, 117M parameters. |
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| | | | OpenAI GPT-2 English model |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``gpt2-medium`` | | 24-layer, 1024-hidden, 16-heads, 345M parameters. |
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| | | | OpenAI's Medium-sized GPT-2 English model |
|
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``gpt2-large`` | | 36-layer, 1280-hidden, 20-heads, 774M parameters. |
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| | | | OpenAI's Large-sized GPT-2 English model |
|
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``gpt2-xl`` | | 48-layer, 1600-hidden, 25-heads, 1558M parameters. |
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| | | | OpenAI's XL-sized GPT-2 English model |
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+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| Transformer-XL | ``transfo-xl-wt103`` | | 18-layer, 1024-hidden, 16-heads, 257M parameters. |
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| | | | English model trained on wikitext-103 |
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+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| XLNet | ``xlnet-base-cased`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
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| | | | XLNet English model |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``xlnet-large-cased`` | | 24-layer, 1024-hidden, 16-heads, 340M parameters. |
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| | | | XLNet Large English model |
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+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| XLM | ``xlm-mlm-en-2048`` | | 12-layer, 2048-hidden, 16-heads |
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| | | | XLM English model |
|
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``xlm-mlm-ende-1024`` | | 6-layer, 1024-hidden, 8-heads |
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| | | | XLM English-German model trained on the concatenation of English and German wikipedia |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``xlm-mlm-enfr-1024`` | | 6-layer, 1024-hidden, 8-heads |
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| | | | XLM English-French model trained on the concatenation of English and French wikipedia |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``xlm-mlm-enro-1024`` | | 6-layer, 1024-hidden, 8-heads |
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| | | | XLM English-Romanian Multi-language model |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``xlm-mlm-xnli15-1024`` | | 12-layer, 1024-hidden, 8-heads |
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| | | | XLM Model pre-trained with MLM on the `15 XNLI languages <https://github.com/facebookresearch/XNLI>`__. |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``xlm-mlm-tlm-xnli15-1024`` | | 12-layer, 1024-hidden, 8-heads |
|
||||
| | | | XLM Model pre-trained with MLM + TLM on the `15 XNLI languages <https://github.com/facebookresearch/XNLI>`__. |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``xlm-clm-enfr-1024`` | | 6-layer, 1024-hidden, 8-heads |
|
||||
| | | | XLM English-French model trained with CLM (Causal Language Modeling) on the concatenation of English and French wikipedia |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``xlm-clm-ende-1024`` | | 6-layer, 1024-hidden, 8-heads |
|
||||
| | | | XLM English-German model trained with CLM (Causal Language Modeling) on the concatenation of English and German wikipedia |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``xlm-mlm-17-1280`` | | 16-layer, 1280-hidden, 16-heads |
|
||||
| | | | XLM model trained with MLM (Masked Language Modeling) on 17 languages. |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``xlm-mlm-100-1280`` | | 16-layer, 1280-hidden, 16-heads |
|
||||
| | | | XLM model trained with MLM (Masked Language Modeling) on 100 languages. |
|
||||
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| RoBERTa | ``roberta-base`` | | 12-layer, 768-hidden, 12-heads, 125M parameters |
|
||||
| | | | RoBERTa using the BERT-base architecture |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``roberta-large`` | | 24-layer, 1024-hidden, 16-heads, 355M parameters |
|
||||
| | | | RoBERTa using the BERT-large architecture |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``roberta-large-mnli`` | | 24-layer, 1024-hidden, 16-heads, 355M parameters |
|
||||
| | | | ``roberta-large`` fine-tuned on `MNLI <http://www.nyu.edu/projects/bowman/multinli/>`__. |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``distilroberta-base`` | | 6-layer, 768-hidden, 12-heads, 82M parameters |
|
||||
| | | | The DistilRoBERTa model distilled from the RoBERTa model `roberta-base` checkpoint. |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``roberta-base-openai-detector`` | | 12-layer, 768-hidden, 12-heads, 125M parameters |
|
||||
| | | | ``roberta-base`` fine-tuned by OpenAI on the outputs of the 1.5B-parameter GPT-2 model. |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/openai/gpt-2-output-dataset/tree/master/detector>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``roberta-large-openai-detector`` | | 24-layer, 1024-hidden, 16-heads, 355M parameters |
|
||||
| | | | ``roberta-large`` fine-tuned by OpenAI on the outputs of the 1.5B-parameter GPT-2 model. |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/openai/gpt-2-output-dataset/tree/master/detector>`__) |
|
||||
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| DistilBERT | ``distilbert-base-uncased`` | | 6-layer, 768-hidden, 12-heads, 66M parameters |
|
||||
| | | | The DistilBERT model distilled from the BERT model `bert-base-uncased` checkpoint |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``distilbert-base-uncased-distilled-squad`` | | 6-layer, 768-hidden, 12-heads, 66M parameters |
|
||||
| | | | The DistilBERT model distilled from the BERT model `bert-base-uncased` checkpoint, with an additional linear layer. |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``distilbert-base-cased`` | | 6-layer, 768-hidden, 12-heads, 65M parameters |
|
||||
| | | | The DistilBERT model distilled from the BERT model `bert-base-cased` checkpoint |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``distilbert-base-cased-distilled-squad`` | | 6-layer, 768-hidden, 12-heads, 65M parameters |
|
||||
| | | | The DistilBERT model distilled from the BERT model `bert-base-cased` checkpoint, with an additional question answering layer. |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``distilgpt2`` | | 6-layer, 768-hidden, 12-heads, 82M parameters |
|
||||
| | | | The DistilGPT2 model distilled from the GPT2 model `gpt2` checkpoint. |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``distilbert-base-german-cased`` | | 6-layer, 768-hidden, 12-heads, 66M parameters |
|
||||
| | | | The German DistilBERT model distilled from the German DBMDZ BERT model `bert-base-german-dbmdz-cased` checkpoint. |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``distilbert-base-multilingual-cased`` | | 6-layer, 768-hidden, 12-heads, 134M parameters |
|
||||
| | | | The multilingual DistilBERT model distilled from the Multilingual BERT model `bert-base-multilingual-cased` checkpoint. |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__) |
|
||||
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| CTRL | ``ctrl`` | | 48-layer, 1280-hidden, 16-heads, 1.6B parameters |
|
||||
| | | | Salesforce's Large-sized CTRL English model |
|
||||
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| CamemBERT | ``camembert-base`` | | 12-layer, 768-hidden, 12-heads, 110M parameters |
|
||||
| | | | CamemBERT using the BERT-base architecture |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/camembert>`__) |
|
||||
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| ALBERT | ``albert-base-v1`` | | 12 repeating layers, 128 embedding, 768-hidden, 12-heads, 11M parameters |
|
||||
| | | | ALBERT base model |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``albert-large-v1`` | | 24 repeating layers, 128 embedding, 1024-hidden, 16-heads, 17M parameters |
|
||||
| | | | ALBERT large model |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``albert-xlarge-v1`` | | 24 repeating layers, 128 embedding, 2048-hidden, 16-heads, 58M parameters |
|
||||
| | | | ALBERT xlarge model |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``albert-xxlarge-v1`` | | 12 repeating layer, 128 embedding, 4096-hidden, 64-heads, 223M parameters |
|
||||
| | | | ALBERT xxlarge model |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``albert-base-v2`` | | 12 repeating layers, 128 embedding, 768-hidden, 12-heads, 11M parameters |
|
||||
| | | | ALBERT base model with no dropout, additional training data and longer training |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``albert-large-v2`` | | 24 repeating layers, 128 embedding, 1024-hidden, 16-heads, 17M parameters |
|
||||
| | | | ALBERT large model with no dropout, additional training data and longer training |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``albert-xlarge-v2`` | | 24 repeating layers, 128 embedding, 2048-hidden, 16-heads, 58M parameters |
|
||||
| | | | ALBERT xlarge model with no dropout, additional training data and longer training |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``albert-xxlarge-v2`` | | 12 repeating layer, 128 embedding, 4096-hidden, 64-heads, 223M parameters |
|
||||
| | | | ALBERT xxlarge model with no dropout, additional training data and longer training |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
|
||||
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| T5 | ``t5-small`` | | ~60M parameters with 6-layers, 512-hidden-state, 2048 feed-forward hidden-state, 8-heads, |
|
||||
| | | | Trained on English text: the Colossal Clean Crawled Corpus (C4) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``t5-base`` | | ~220M parameters with 12-layers, 768-hidden-state, 3072 feed-forward hidden-state, 12-heads, |
|
||||
| | | | Trained on English text: the Colossal Clean Crawled Corpus (C4) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``t5-large`` | | ~770M parameters with 24-layers, 1024-hidden-state, 4096 feed-forward hidden-state, 16-heads, |
|
||||
| | | | Trained on English text: the Colossal Clean Crawled Corpus (C4) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``t5-3B`` | | ~2.8B parameters with 24-layers, 1024-hidden-state, 16384 feed-forward hidden-state, 32-heads, |
|
||||
| | | | Trained on English text: the Colossal Clean Crawled Corpus (C4) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``t5-11B`` | | ~11B parameters with 24-layers, 1024-hidden-state, 65536 feed-forward hidden-state, 128-heads, |
|
||||
| | | | Trained on English text: the Colossal Clean Crawled Corpus (C4) |
|
||||
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| XLM-RoBERTa | ``xlm-roberta-base`` | | ~125M parameters with 12-layers, 768-hidden-state, 3072 feed-forward hidden-state, 8-heads, |
|
||||
| | | | Trained on on 2.5 TB of newly created clean CommonCrawl data in 100 languages |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``xlm-roberta-large`` | | ~355M parameters with 24-layers, 1027-hidden-state, 4096 feed-forward hidden-state, 16-heads, |
|
||||
| | | | Trained on 2.5 TB of newly created clean CommonCrawl data in 100 languages |
|
||||
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| FlauBERT | ``flaubert/flaubert_small_cased`` | | 6-layer, 512-hidden, 8-heads, 54M parameters |
|
||||
| | | | FlauBERT small architecture |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/getalp/Flaubert>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``flaubert/flaubert_base_uncased`` | | 12-layer, 768-hidden, 12-heads, 137M parameters |
|
||||
| | | | FlauBERT base architecture with uncased vocabulary |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/getalp/Flaubert>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``flaubert/flaubert_base_cased`` | | 12-layer, 768-hidden, 12-heads, 138M parameters |
|
||||
| | | | FlauBERT base architecture with cased vocabulary |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/getalp/Flaubert>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``flaubert/flaubert_large_cased`` | | 24-layer, 1024-hidden, 16-heads, 373M parameters |
|
||||
| | | | FlauBERT large architecture |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/getalp/Flaubert>`__) |
|
||||
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| Bart | ``facebook/bart-large`` | | 24-layer, 1024-hidden, 16-heads, 406M parameters |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/bart>`_) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``facebook/bart-base`` | | 12-layer, 768-hidden, 16-heads, 139M parameters |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``facebook/bart-large-mnli`` | | Adds a 2 layer classification head with 1 million parameters |
|
||||
| | | | bart-large base architecture with a classification head, finetuned on MNLI |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``facebook/bart-large-cnn`` | | 12-layer, 1024-hidden, 16-heads, 406M parameters (same as base) |
|
||||
| | | | bart-large base architecture finetuned on cnn summarization task |
|
||||
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| DialoGPT | ``DialoGPT-small`` | | 12-layer, 768-hidden, 12-heads, 124M parameters |
|
||||
| | | | Trained on English text: 147M conversation-like exchanges extracted from Reddit. |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``DialoGPT-medium`` | | 24-layer, 1024-hidden, 16-heads, 355M parameters |
|
||||
| | | | Trained on English text: 147M conversation-like exchanges extracted from Reddit. |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``DialoGPT-large`` | | 36-layer, 1280-hidden, 20-heads, 774M parameters |
|
||||
| | | | Trained on English text: 147M conversation-like exchanges extracted from Reddit. |
|
||||
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| Reformer | ``reformer-enwik8`` | | 12-layer, 1024-hidden, 8-heads, 149M parameters |
|
||||
| | | | Trained on English Wikipedia data - enwik8. |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``reformer-crime-and-punishment`` | | 6-layer, 256-hidden, 2-heads, 3M parameters |
|
||||
| | | | Trained on English text: Crime and Punishment novel by Fyodor Dostoyevsky. |
|
||||
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| MarianMT | ``Helsinki-NLP/opus-mt-{src}-{tgt}`` | | 12-layer, 512-hidden, 8-heads, ~74M parameter Machine translation models. Parameter counts vary depending on vocab size. |
|
||||
| | | | (see `model list <https://huggingface.co/Helsinki-NLP>`_) |
|
||||
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| Pegasus | ``google/pegasus-{dataset}`` | | 16-layer, 1024-hidden, 16-heads, ~568M parameter, 2.2 GB for summary. `model list <https://huggingface.co/models?search=pegasus>`__ |
|
||||
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| Longformer | ``allenai/longformer-base-4096`` | | 12-layer, 768-hidden, 12-heads, ~149M parameters |
|
||||
| | | | Starting from RoBERTa-base checkpoint, trained on documents of max length 4,096 |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``allenai/longformer-large-4096`` | | 24-layer, 1024-hidden, 16-heads, ~435M parameters |
|
||||
| | | | Starting from RoBERTa-large checkpoint, trained on documents of max length 4,096 |
|
||||
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| MBart | ``facebook/mbart-large-cc25`` | | 24-layer, 1024-hidden, 16-heads, 610M parameters |
|
||||
| | | | mBART (bart-large architecture) model trained on 25 languages' monolingual corpus |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``facebook/mbart-large-en-ro`` | | 24-layer, 1024-hidden, 16-heads, 610M parameters |
|
||||
| | | | mbart-large-cc25 model finetuned on WMT english romanian translation. |
|
||||
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| Lxmert | ``lxmert-base-uncased`` | | 9-language layers, 9-relationship layers, and 12-cross-modality layers |
|
||||
| | | | 768-hidden, 12-heads (for each layer) ~ 228M parameters |
|
||||
| | | | Starting from lxmert-base checkpoint, trained on over 9 million image-text couplets from COCO, VisualGenome, GQA, VQA |
|
||||
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| Architecture | Shortcut name | Details of the model |
|
||||
+====================+============================================================+=======================================================================================================================================+
|
||||
| BERT | ``bert-base-uncased`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
|
||||
| | | | Trained on lower-cased English text. |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``bert-large-uncased`` | | 24-layer, 1024-hidden, 16-heads, 340M parameters. |
|
||||
| | | | Trained on lower-cased English text. |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``bert-base-cased`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
|
||||
| | | | Trained on cased English text. |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``bert-large-cased`` | | 24-layer, 1024-hidden, 16-heads, 340M parameters. |
|
||||
| | | | Trained on cased English text. |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``bert-base-multilingual-uncased`` | | (Original, not recommended) 12-layer, 768-hidden, 12-heads, 110M parameters. |
|
||||
| | | | Trained on lower-cased text in the top 102 languages with the largest Wikipedias |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/google-research/bert/blob/master/multilingual.md>`__). |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``bert-base-multilingual-cased`` | | (New, **recommended**) 12-layer, 768-hidden, 12-heads, 110M parameters. |
|
||||
| | | | Trained on cased text in the top 104 languages with the largest Wikipedias |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/google-research/bert/blob/master/multilingual.md>`__). |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``bert-base-chinese`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
|
||||
| | | | Trained on cased Chinese Simplified and Traditional text. |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``bert-base-german-cased`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
|
||||
| | | | Trained on cased German text by Deepset.ai |
|
||||
| | | |
|
||||
| | | (see `details on deepset.ai website <https://deepset.ai/german-bert>`__). |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``bert-large-uncased-whole-word-masking`` | | 24-layer, 1024-hidden, 16-heads, 340M parameters. |
|
||||
| | | | Trained on lower-cased English text using Whole-Word-Masking |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/google-research/bert/#bert>`__). |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``bert-large-cased-whole-word-masking`` | | 24-layer, 1024-hidden, 16-heads, 340M parameters. |
|
||||
| | | | Trained on cased English text using Whole-Word-Masking |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/google-research/bert/#bert>`__). |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``bert-large-uncased-whole-word-masking-finetuned-squad`` | | 24-layer, 1024-hidden, 16-heads, 340M parameters. |
|
||||
| | | | The ``bert-large-uncased-whole-word-masking`` model fine-tuned on SQuAD |
|
||||
| | | |
|
||||
| | | (see details of fine-tuning in the `example section <https://github.com/huggingface/transformers/tree/master/examples>`__). |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``bert-large-cased-whole-word-masking-finetuned-squad`` | | 24-layer, 1024-hidden, 16-heads, 340M parameters |
|
||||
| | | | The ``bert-large-cased-whole-word-masking`` model fine-tuned on SQuAD |
|
||||
| | | |
|
||||
| | | (see `details of fine-tuning in the example section <https://huggingface.co/transformers/examples.html>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``bert-base-cased-finetuned-mrpc`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
|
||||
| | | | The ``bert-base-cased`` model fine-tuned on MRPC |
|
||||
| | | |
|
||||
| | | (see `details of fine-tuning in the example section <https://huggingface.co/transformers/examples.html>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``bert-base-german-dbmdz-cased`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
|
||||
| | | | Trained on cased German text by DBMDZ |
|
||||
| | | |
|
||||
| | | (see `details on dbmdz repository <https://github.com/dbmdz/german-bert>`__). |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``bert-base-german-dbmdz-uncased`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
|
||||
| | | | Trained on uncased German text by DBMDZ |
|
||||
| | | |
|
||||
| | | (see `details on dbmdz repository <https://github.com/dbmdz/german-bert>`__). |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``cl-tohoku/bert-base-japanese`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
|
||||
| | | | Trained on Japanese text. Text is tokenized with MeCab and WordPiece and this requires some extra dependencies, |
|
||||
| | | | `fugashi <https://github.com/polm/fugashi>`__ which is a wrapper around `MeCab <https://taku910.github.io/mecab/>`__. |
|
||||
| | | | Use ``pip install transformers["ja"]`` (or ``pip install -e .["ja"]`` if you install from source) to install them. |
|
||||
| | | |
|
||||
| | | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__). |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``cl-tohoku/bert-base-japanese-whole-word-masking`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
|
||||
| | | | Trained on Japanese text. Text is tokenized with MeCab and WordPiece and this requires some extra dependencies, |
|
||||
| | | | `fugashi <https://github.com/polm/fugashi>`__ which is a wrapper around `MeCab <https://taku910.github.io/mecab/>`__. |
|
||||
| | | | Use ``pip install transformers["ja"]`` (or ``pip install -e .["ja"]`` if you install from source) to install them. |
|
||||
| | | |
|
||||
| | | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__). |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``cl-tohoku/bert-base-japanese-char`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
|
||||
| | | | Trained on Japanese text. Text is tokenized into characters. |
|
||||
| | | |
|
||||
| | | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__). |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``cl-tohoku/bert-base-japanese-char-whole-word-masking`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
|
||||
| | | | Trained on Japanese text using Whole-Word-Masking. Text is tokenized into characters. |
|
||||
| | | |
|
||||
| | | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__). |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``TurkuNLP/bert-base-finnish-cased-v1`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
|
||||
| | | | Trained on cased Finnish text. |
|
||||
| | | |
|
||||
| | | (see `details on turkunlp.org <http://turkunlp.org/FinBERT/>`__). |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``TurkuNLP/bert-base-finnish-uncased-v1`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
|
||||
| | | | Trained on uncased Finnish text. |
|
||||
| | | |
|
||||
| | | (see `details on turkunlp.org <http://turkunlp.org/FinBERT/>`__). |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``wietsedv/bert-base-dutch-cased`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
|
||||
| | | | Trained on cased Dutch text. |
|
||||
| | | |
|
||||
| | | (see `details on wietsedv repository <https://github.com/wietsedv/bertje/>`__). |
|
||||
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| GPT | ``openai-gpt`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
|
||||
| | | | OpenAI GPT English model |
|
||||
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| GPT-2 | ``gpt2`` | | 12-layer, 768-hidden, 12-heads, 117M parameters. |
|
||||
| | | | OpenAI GPT-2 English model |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``gpt2-medium`` | | 24-layer, 1024-hidden, 16-heads, 345M parameters. |
|
||||
| | | | OpenAI's Medium-sized GPT-2 English model |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``gpt2-large`` | | 36-layer, 1280-hidden, 20-heads, 774M parameters. |
|
||||
| | | | OpenAI's Large-sized GPT-2 English model |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``gpt2-xl`` | | 48-layer, 1600-hidden, 25-heads, 1558M parameters. |
|
||||
| | | | OpenAI's XL-sized GPT-2 English model |
|
||||
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| Transformer-XL | ``transfo-xl-wt103`` | | 18-layer, 1024-hidden, 16-heads, 257M parameters. |
|
||||
| | | | English model trained on wikitext-103 |
|
||||
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| XLNet | ``xlnet-base-cased`` | | 12-layer, 768-hidden, 12-heads, 110M parameters. |
|
||||
| | | | XLNet English model |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``xlnet-large-cased`` | | 24-layer, 1024-hidden, 16-heads, 340M parameters. |
|
||||
| | | | XLNet Large English model |
|
||||
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| XLM | ``xlm-mlm-en-2048`` | | 12-layer, 2048-hidden, 16-heads |
|
||||
| | | | XLM English model |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``xlm-mlm-ende-1024`` | | 6-layer, 1024-hidden, 8-heads |
|
||||
| | | | XLM English-German model trained on the concatenation of English and German wikipedia |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``xlm-mlm-enfr-1024`` | | 6-layer, 1024-hidden, 8-heads |
|
||||
| | | | XLM English-French model trained on the concatenation of English and French wikipedia |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``xlm-mlm-enro-1024`` | | 6-layer, 1024-hidden, 8-heads |
|
||||
| | | | XLM English-Romanian Multi-language model |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``xlm-mlm-xnli15-1024`` | | 12-layer, 1024-hidden, 8-heads |
|
||||
| | | | XLM Model pre-trained with MLM on the `15 XNLI languages <https://github.com/facebookresearch/XNLI>`__. |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``xlm-mlm-tlm-xnli15-1024`` | | 12-layer, 1024-hidden, 8-heads |
|
||||
| | | | XLM Model pre-trained with MLM + TLM on the `15 XNLI languages <https://github.com/facebookresearch/XNLI>`__. |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``xlm-clm-enfr-1024`` | | 6-layer, 1024-hidden, 8-heads |
|
||||
| | | | XLM English-French model trained with CLM (Causal Language Modeling) on the concatenation of English and French wikipedia |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``xlm-clm-ende-1024`` | | 6-layer, 1024-hidden, 8-heads |
|
||||
| | | | XLM English-German model trained with CLM (Causal Language Modeling) on the concatenation of English and German wikipedia |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``xlm-mlm-17-1280`` | | 16-layer, 1280-hidden, 16-heads |
|
||||
| | | | XLM model trained with MLM (Masked Language Modeling) on 17 languages. |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``xlm-mlm-100-1280`` | | 16-layer, 1280-hidden, 16-heads |
|
||||
| | | | XLM model trained with MLM (Masked Language Modeling) on 100 languages. |
|
||||
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| RoBERTa | ``roberta-base`` | | 12-layer, 768-hidden, 12-heads, 125M parameters |
|
||||
| | | | RoBERTa using the BERT-base architecture |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``roberta-large`` | | 24-layer, 1024-hidden, 16-heads, 355M parameters |
|
||||
| | | | RoBERTa using the BERT-large architecture |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``roberta-large-mnli`` | | 24-layer, 1024-hidden, 16-heads, 355M parameters |
|
||||
| | | | ``roberta-large`` fine-tuned on `MNLI <http://www.nyu.edu/projects/bowman/multinli/>`__. |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``distilroberta-base`` | | 6-layer, 768-hidden, 12-heads, 82M parameters |
|
||||
| | | | The DistilRoBERTa model distilled from the RoBERTa model `roberta-base` checkpoint. |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``roberta-base-openai-detector`` | | 12-layer, 768-hidden, 12-heads, 125M parameters |
|
||||
| | | | ``roberta-base`` fine-tuned by OpenAI on the outputs of the 1.5B-parameter GPT-2 model. |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/openai/gpt-2-output-dataset/tree/master/detector>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``roberta-large-openai-detector`` | | 24-layer, 1024-hidden, 16-heads, 355M parameters |
|
||||
| | | | ``roberta-large`` fine-tuned by OpenAI on the outputs of the 1.5B-parameter GPT-2 model. |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/openai/gpt-2-output-dataset/tree/master/detector>`__) |
|
||||
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| DistilBERT | ``distilbert-base-uncased`` | | 6-layer, 768-hidden, 12-heads, 66M parameters |
|
||||
| | | | The DistilBERT model distilled from the BERT model `bert-base-uncased` checkpoint |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``distilbert-base-uncased-distilled-squad`` | | 6-layer, 768-hidden, 12-heads, 66M parameters |
|
||||
| | | | The DistilBERT model distilled from the BERT model `bert-base-uncased` checkpoint, with an additional linear layer. |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``distilbert-base-cased`` | | 6-layer, 768-hidden, 12-heads, 65M parameters |
|
||||
| | | | The DistilBERT model distilled from the BERT model `bert-base-cased` checkpoint |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``distilbert-base-cased-distilled-squad`` | | 6-layer, 768-hidden, 12-heads, 65M parameters |
|
||||
| | | | The DistilBERT model distilled from the BERT model `bert-base-cased` checkpoint, with an additional question answering layer. |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``distilgpt2`` | | 6-layer, 768-hidden, 12-heads, 82M parameters |
|
||||
| | | | The DistilGPT2 model distilled from the GPT2 model `gpt2` checkpoint. |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``distilbert-base-german-cased`` | | 6-layer, 768-hidden, 12-heads, 66M parameters |
|
||||
| | | | The German DistilBERT model distilled from the German DBMDZ BERT model `bert-base-german-dbmdz-cased` checkpoint. |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``distilbert-base-multilingual-cased`` | | 6-layer, 768-hidden, 12-heads, 134M parameters |
|
||||
| | | | The multilingual DistilBERT model distilled from the Multilingual BERT model `bert-base-multilingual-cased` checkpoint. |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__) |
|
||||
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| CTRL | ``ctrl`` | | 48-layer, 1280-hidden, 16-heads, 1.6B parameters |
|
||||
| | | | Salesforce's Large-sized CTRL English model |
|
||||
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| CamemBERT | ``camembert-base`` | | 12-layer, 768-hidden, 12-heads, 110M parameters |
|
||||
| | | | CamemBERT using the BERT-base architecture |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/camembert>`__) |
|
||||
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| ALBERT | ``albert-base-v1`` | | 12 repeating layers, 128 embedding, 768-hidden, 12-heads, 11M parameters |
|
||||
| | | | ALBERT base model |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``albert-large-v1`` | | 24 repeating layers, 128 embedding, 1024-hidden, 16-heads, 17M parameters |
|
||||
| | | | ALBERT large model |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``albert-xlarge-v1`` | | 24 repeating layers, 128 embedding, 2048-hidden, 16-heads, 58M parameters |
|
||||
| | | | ALBERT xlarge model |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``albert-xxlarge-v1`` | | 12 repeating layer, 128 embedding, 4096-hidden, 64-heads, 223M parameters |
|
||||
| | | | ALBERT xxlarge model |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``albert-base-v2`` | | 12 repeating layers, 128 embedding, 768-hidden, 12-heads, 11M parameters |
|
||||
| | | | ALBERT base model with no dropout, additional training data and longer training |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``albert-large-v2`` | | 24 repeating layers, 128 embedding, 1024-hidden, 16-heads, 17M parameters |
|
||||
| | | | ALBERT large model with no dropout, additional training data and longer training |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``albert-xlarge-v2`` | | 24 repeating layers, 128 embedding, 2048-hidden, 16-heads, 58M parameters |
|
||||
| | | | ALBERT xlarge model with no dropout, additional training data and longer training |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``albert-xxlarge-v2`` | | 12 repeating layer, 128 embedding, 4096-hidden, 64-heads, 223M parameters |
|
||||
| | | | ALBERT xxlarge model with no dropout, additional training data and longer training |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/google-research/ALBERT>`__) |
|
||||
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| T5 | ``t5-small`` | | ~60M parameters with 6-layers, 512-hidden-state, 2048 feed-forward hidden-state, 8-heads, |
|
||||
| | | | Trained on English text: the Colossal Clean Crawled Corpus (C4) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``t5-base`` | | ~220M parameters with 12-layers, 768-hidden-state, 3072 feed-forward hidden-state, 12-heads, |
|
||||
| | | | Trained on English text: the Colossal Clean Crawled Corpus (C4) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``t5-large`` | | ~770M parameters with 24-layers, 1024-hidden-state, 4096 feed-forward hidden-state, 16-heads, |
|
||||
| | | | Trained on English text: the Colossal Clean Crawled Corpus (C4) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``t5-3B`` | | ~2.8B parameters with 24-layers, 1024-hidden-state, 16384 feed-forward hidden-state, 32-heads, |
|
||||
| | | | Trained on English text: the Colossal Clean Crawled Corpus (C4) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``t5-11B`` | | ~11B parameters with 24-layers, 1024-hidden-state, 65536 feed-forward hidden-state, 128-heads, |
|
||||
| | | | Trained on English text: the Colossal Clean Crawled Corpus (C4) |
|
||||
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| XLM-RoBERTa | ``xlm-roberta-base`` | | ~125M parameters with 12-layers, 768-hidden-state, 3072 feed-forward hidden-state, 8-heads, |
|
||||
| | | | Trained on on 2.5 TB of newly created clean CommonCrawl data in 100 languages |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``xlm-roberta-large`` | | ~355M parameters with 24-layers, 1027-hidden-state, 4096 feed-forward hidden-state, 16-heads, |
|
||||
| | | | Trained on 2.5 TB of newly created clean CommonCrawl data in 100 languages |
|
||||
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| FlauBERT | ``flaubert/flaubert_small_cased`` | | 6-layer, 512-hidden, 8-heads, 54M parameters |
|
||||
| | | | FlauBERT small architecture |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/getalp/Flaubert>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``flaubert/flaubert_base_uncased`` | | 12-layer, 768-hidden, 12-heads, 137M parameters |
|
||||
| | | | FlauBERT base architecture with uncased vocabulary |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/getalp/Flaubert>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``flaubert/flaubert_base_cased`` | | 12-layer, 768-hidden, 12-heads, 138M parameters |
|
||||
| | | | FlauBERT base architecture with cased vocabulary |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/getalp/Flaubert>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``flaubert/flaubert_large_cased`` | | 24-layer, 1024-hidden, 16-heads, 373M parameters |
|
||||
| | | | FlauBERT large architecture |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/getalp/Flaubert>`__) |
|
||||
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| Bart | ``facebook/bart-large`` | | 24-layer, 1024-hidden, 16-heads, 406M parameters |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/bart>`_) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``facebook/bart-base`` | | 12-layer, 768-hidden, 16-heads, 139M parameters |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``facebook/bart-large-mnli`` | | Adds a 2 layer classification head with 1 million parameters |
|
||||
| | | | bart-large base architecture with a classification head, finetuned on MNLI |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``facebook/bart-large-cnn`` | | 12-layer, 1024-hidden, 16-heads, 406M parameters (same as base) |
|
||||
| | | | bart-large base architecture finetuned on cnn summarization task |
|
||||
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| DialoGPT | ``DialoGPT-small`` | | 12-layer, 768-hidden, 12-heads, 124M parameters |
|
||||
| | | | Trained on English text: 147M conversation-like exchanges extracted from Reddit. |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``DialoGPT-medium`` | | 24-layer, 1024-hidden, 16-heads, 355M parameters |
|
||||
| | | | Trained on English text: 147M conversation-like exchanges extracted from Reddit. |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``DialoGPT-large`` | | 36-layer, 1280-hidden, 20-heads, 774M parameters |
|
||||
| | | | Trained on English text: 147M conversation-like exchanges extracted from Reddit. |
|
||||
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| Reformer | ``reformer-enwik8`` | | 12-layer, 1024-hidden, 8-heads, 149M parameters |
|
||||
| | | | Trained on English Wikipedia data - enwik8. |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``reformer-crime-and-punishment`` | | 6-layer, 256-hidden, 2-heads, 3M parameters |
|
||||
| | | | Trained on English text: Crime and Punishment novel by Fyodor Dostoyevsky. |
|
||||
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| MarianMT | ``Helsinki-NLP/opus-mt-{src}-{tgt}`` | | 12-layer, 512-hidden, 8-heads, ~74M parameter Machine translation models. Parameter counts vary depending on vocab size. |
|
||||
| | | | (see `model list <https://huggingface.co/Helsinki-NLP>`_) |
|
||||
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| Pegasus | ``google/pegasus-{dataset}`` | | 16-layer, 1024-hidden, 16-heads, ~568M parameter, 2.2 GB for summary. `model list <https://huggingface.co/models?search=pegasus>`__ |
|
||||
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| Longformer | ``allenai/longformer-base-4096`` | | 12-layer, 768-hidden, 12-heads, ~149M parameters |
|
||||
| | | | Starting from RoBERTa-base checkpoint, trained on documents of max length 4,096 |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``allenai/longformer-large-4096`` | | 24-layer, 1024-hidden, 16-heads, ~435M parameters |
|
||||
| | | | Starting from RoBERTa-large checkpoint, trained on documents of max length 4,096 |
|
||||
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| MBart | ``facebook/mbart-large-cc25`` | | 24-layer, 1024-hidden, 16-heads, 610M parameters |
|
||||
| | | | mBART (bart-large architecture) model trained on 25 languages' monolingual corpus |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``facebook/mbart-large-en-ro`` | | 24-layer, 1024-hidden, 16-heads, 610M parameters |
|
||||
| | | | mbart-large-cc25 model finetuned on WMT english romanian translation. |
|
||||
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| Lxmert | ``lxmert-base-uncased`` | | 9-language layers, 9-relationship layers, and 12-cross-modality layers |
|
||||
| | | | 768-hidden, 12-heads (for each layer) ~ 228M parameters |
|
||||
| | | | Starting from lxmert-base checkpoint, trained on over 9 million image-text couplets from COCO, VisualGenome, GQA, VQA |
|
||||
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| Funnel Transformer | ``funnel-transformer/small`` | | 14 layers: 3 blocks of 4 layers then 2 layers decoder, 768-hidden, 12-heads, 130M parameters |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``funnel-transformer/small-base`` | | 12 layers: 3 blocks of 4 layers (no decoder), 768-hidden, 12-heads, 115M parameters |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``funnel-transformer/medium`` | | 14 layers: 3 blocks 6, 3x2, 3x2 layers then 2 layers decoder, 768-hidden, 12-heads, 130M parameters |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``funnel-transformer/medium-base`` | | 12 layers: 3 blocks 6, 3x2, 3x2 layers(no decoder), 768-hidden, 12-heads, 115M parameters |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``funnel-transformer/intermediate`` | | 20 layers: 3 blocks of 6 layers then 2 layers decoder, 768-hidden, 12-heads, 177M parameters |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``funnel-transformer/intermediate-base`` | | 18 layers: 3 blocks of 6 layers (no decoder), 768-hidden, 12-heads, 161M parameters |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``funnel-transformer/large`` | | 26 layers: 3 blocks of 8 layers then 2 layers decoder, 1024-hidden, 12-heads, 386M parameters |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``funnel-transformer/large-base`` | | 24 layers: 3 blocks of 8 layers (no decoder), 1024-hidden, 12-heads, 358M parameters |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``funnel-transformer/xlarge`` | | 32 layers: 3 blocks of 10 layers then 2 layers decoder, 1024-hidden, 12-heads, 468M parameters |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__) |
|
||||
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
| | ``funnel-transformer/xlarge-base`` | | 30 layers: 3 blocks of 10 layers (no decoder), 1024-hidden, 12-heads, 440M parameters |
|
||||
| | | |
|
||||
| | | (see `details <https://github.com/laiguokun/Funnel-Transformer>`__) |
|
||||
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
||||
|
||||
Reference in New Issue
Block a user