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440 lines
93 KiB
ReStructuredText
440 lines
93 KiB
ReStructuredText
Pretrained models
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=======================================================================================================================
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Here is the full list of the currently provided pretrained models together with a short presentation of each model.
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For a list that includes community-uploaded models, refer to `https://huggingface.co/models
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<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, 336M 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, 109M 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, 335M 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, 168M parameters. |
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| | | | Trained on lower-cased text in the top 102 languages with the largest Wikipedias |
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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, 179M parameters. |
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| | | | Trained on cased text in the top 104 languages with the largest Wikipedias |
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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, 103M 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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| | | (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, 336M parameters. |
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| | | | Trained on lower-cased English text using Whole-Word-Masking |
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| | | (see `details <https://github.com/google-research/bert/#bert>`__). |
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| | ``bert-large-cased-whole-word-masking`` | | 24-layer, 1024-hidden, 16-heads, 335M parameters. |
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| | | | Trained on cased English text using Whole-Word-Masking |
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| | | (see `details <https://github.com/google-research/bert/#bert>`__). |
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| | ``bert-large-uncased-whole-word-masking-finetuned-squad`` | | 24-layer, 1024-hidden, 16-heads, 336M parameters. |
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| | | | The ``bert-large-uncased-whole-word-masking`` model fine-tuned on SQuAD |
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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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| | ``bert-large-cased-whole-word-masking-finetuned-squad`` | | 24-layer, 1024-hidden, 16-heads, 335M parameters |
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| | | | The ``bert-large-cased-whole-word-masking`` model fine-tuned on SQuAD |
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| | | (see `details of fine-tuning in the example section <https://huggingface.co/transformers/examples.html>`__) |
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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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| | | (see `details of fine-tuning in the example section <https://huggingface.co/transformers/examples.html>`__) |
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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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| | | (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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| | | (see `details on dbmdz repository <https://github.com/dbmdz/german-bert>`__). |
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| | ``cl-tohoku/bert-base-japanese`` | | 12-layer, 768-hidden, 12-heads, 111M 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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| | | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__). |
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| | ``cl-tohoku/bert-base-japanese-whole-word-masking`` | | 12-layer, 768-hidden, 12-heads, 111M 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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| | | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__). |
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| | ``cl-tohoku/bert-base-japanese-char`` | | 12-layer, 768-hidden, 12-heads, 90M parameters. |
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| | | | Trained on Japanese text. Text is tokenized into characters. |
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| | | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__). |
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| | ``cl-tohoku/bert-base-japanese-char-whole-word-masking`` | | 12-layer, 768-hidden, 12-heads, 90M parameters. |
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| | | | Trained on Japanese text using Whole-Word-Masking. Text is tokenized into characters. |
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| | | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__). |
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| | ``TurkuNLP/bert-base-finnish-cased-v1`` | | 12-layer, 768-hidden, 12-heads, 125M parameters. |
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| | | | Trained on cased Finnish text. |
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| | | (see `details on turkunlp.org <http://turkunlp.org/FinBERT/>`__). |
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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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| | | (see `details on turkunlp.org <http://turkunlp.org/FinBERT/>`__). |
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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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| | | (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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| | ``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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| | ``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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| | ``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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| | ``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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| | ``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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| | ``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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| | ``xlm-mlm-enro-1024`` | | 6-layer, 1024-hidden, 8-heads |
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| | | | XLM English-Romanian Multi-language model |
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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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| | ``xlm-mlm-tlm-xnli15-1024`` | | 12-layer, 1024-hidden, 8-heads |
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| | | | XLM Model pre-trained with MLM + TLM on the `15 XNLI languages <https://github.com/facebookresearch/XNLI>`__. |
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| | ``xlm-clm-enfr-1024`` | | 6-layer, 1024-hidden, 8-heads |
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| | | | XLM English-French model trained with CLM (Causal Language Modeling) on the concatenation of English and French wikipedia |
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| | ``xlm-clm-ende-1024`` | | 6-layer, 1024-hidden, 8-heads |
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| | | | XLM English-German model trained with CLM (Causal Language Modeling) on the concatenation of English and German wikipedia |
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| | ``xlm-mlm-17-1280`` | | 16-layer, 1280-hidden, 16-heads |
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| | | | XLM model trained with MLM (Masked Language Modeling) on 17 languages. |
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| | ``xlm-mlm-100-1280`` | | 16-layer, 1280-hidden, 16-heads |
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| | | | XLM model trained with MLM (Masked Language Modeling) on 100 languages. |
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+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| RoBERTa | ``roberta-base`` | | 12-layer, 768-hidden, 12-heads, 125M parameters |
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| | | | RoBERTa using the BERT-base architecture |
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| | | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__) |
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| | ``roberta-large`` | | 24-layer, 1024-hidden, 16-heads, 355M parameters |
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| | | | RoBERTa using the BERT-large architecture |
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| | | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__) |
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| | ``roberta-large-mnli`` | | 24-layer, 1024-hidden, 16-heads, 355M parameters |
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| | | | ``roberta-large`` fine-tuned on `MNLI <http://www.nyu.edu/projects/bowman/multinli/>`__. |
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| | | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/roberta>`__) |
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| | ``distilroberta-base`` | | 6-layer, 768-hidden, 12-heads, 82M parameters |
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| | | | The DistilRoBERTa model distilled from the RoBERTa model `roberta-base` checkpoint. |
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| | | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__) |
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| | ``roberta-base-openai-detector`` | | 12-layer, 768-hidden, 12-heads, 125M parameters |
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| | | | ``roberta-base`` fine-tuned by OpenAI on the outputs of the 1.5B-parameter GPT-2 model. |
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| | | (see `details <https://github.com/openai/gpt-2-output-dataset/tree/master/detector>`__) |
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| | ``roberta-large-openai-detector`` | | 24-layer, 1024-hidden, 16-heads, 355M parameters |
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| | | | ``roberta-large`` fine-tuned by OpenAI on the outputs of the 1.5B-parameter GPT-2 model. |
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| | | (see `details <https://github.com/openai/gpt-2-output-dataset/tree/master/detector>`__) |
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+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| DistilBERT | ``distilbert-base-uncased`` | | 6-layer, 768-hidden, 12-heads, 66M parameters |
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| | | | The DistilBERT model distilled from the BERT model `bert-base-uncased` checkpoint |
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| | | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__) |
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| | ``distilbert-base-uncased-distilled-squad`` | | 6-layer, 768-hidden, 12-heads, 66M parameters |
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| | | | The DistilBERT model distilled from the BERT model `bert-base-uncased` checkpoint, with an additional linear layer. |
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| | | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__) |
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| | ``distilbert-base-cased`` | | 6-layer, 768-hidden, 12-heads, 65M parameters |
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| | | | The DistilBERT model distilled from the BERT model `bert-base-cased` checkpoint |
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| | | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__) |
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| | ``distilbert-base-cased-distilled-squad`` | | 6-layer, 768-hidden, 12-heads, 65M parameters |
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| | | | The DistilBERT model distilled from the BERT model `bert-base-cased` checkpoint, with an additional question answering layer. |
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| | | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__) |
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| | ``distilgpt2`` | | 6-layer, 768-hidden, 12-heads, 82M parameters |
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| | | | The DistilGPT2 model distilled from the GPT2 model `gpt2` checkpoint. |
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| | | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__) |
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| | ``distilbert-base-german-cased`` | | 6-layer, 768-hidden, 12-heads, 66M parameters |
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| | | | The German DistilBERT model distilled from the German DBMDZ BERT model `bert-base-german-dbmdz-cased` checkpoint. |
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| | | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__) |
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| | ``distilbert-base-multilingual-cased`` | | 6-layer, 768-hidden, 12-heads, 134M parameters |
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| | | | The multilingual DistilBERT model distilled from the Multilingual BERT model `bert-base-multilingual-cased` checkpoint. |
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| | | (see `details <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__) |
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+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| CTRL | ``ctrl`` | | 48-layer, 1280-hidden, 16-heads, 1.6B parameters |
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| | | | Salesforce's Large-sized CTRL English model |
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+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| CamemBERT | ``camembert-base`` | | 12-layer, 768-hidden, 12-heads, 110M parameters |
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| | | | CamemBERT using the BERT-base architecture |
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| | | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/camembert>`__) |
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+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| ALBERT | ``albert-base-v1`` | | 12 repeating layers, 128 embedding, 768-hidden, 12-heads, 11M parameters |
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| | | | ALBERT base model |
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| | | (see `details <https://github.com/google-research/ALBERT>`__) |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``albert-large-v1`` | | 24 repeating layers, 128 embedding, 1024-hidden, 16-heads, 17M parameters |
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| | | | ALBERT large model |
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| | | (see `details <https://github.com/google-research/ALBERT>`__) |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``albert-xlarge-v1`` | | 24 repeating layers, 128 embedding, 2048-hidden, 16-heads, 58M parameters |
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| | | | ALBERT xlarge model |
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| | | (see `details <https://github.com/google-research/ALBERT>`__) |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``albert-xxlarge-v1`` | | 12 repeating layer, 128 embedding, 4096-hidden, 64-heads, 223M parameters |
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| | | | ALBERT xxlarge model |
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| | | (see `details <https://github.com/google-research/ALBERT>`__) |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``albert-base-v2`` | | 12 repeating layers, 128 embedding, 768-hidden, 12-heads, 11M parameters |
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| | | | ALBERT base model with no dropout, additional training data and longer training |
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| | | (see `details <https://github.com/google-research/ALBERT>`__) |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``albert-large-v2`` | | 24 repeating layers, 128 embedding, 1024-hidden, 16-heads, 17M parameters |
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| | | | ALBERT large model with no dropout, additional training data and longer training |
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| | | (see `details <https://github.com/google-research/ALBERT>`__) |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``albert-xlarge-v2`` | | 24 repeating layers, 128 embedding, 2048-hidden, 16-heads, 58M parameters |
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| | | | ALBERT xlarge model with no dropout, additional training data and longer training |
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| | | (see `details <https://github.com/google-research/ALBERT>`__) |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``albert-xxlarge-v2`` | | 12 repeating layer, 128 embedding, 4096-hidden, 64-heads, 223M parameters |
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| | | | ALBERT xxlarge model with no dropout, additional training data and longer training |
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| | | (see `details <https://github.com/google-research/ALBERT>`__) |
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+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| T5 | ``t5-small`` | | ~60M parameters with 6-layers, 512-hidden-state, 2048 feed-forward hidden-state, 8-heads, |
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| | | | Trained on English text: the Colossal Clean Crawled Corpus (C4) |
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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| | ``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) |
|
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
|
| | ``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) |
|
|
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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|
| XLM-RoBERTa | ``xlm-roberta-base`` | | ~270M 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 |
|
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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
|
| | ``xlm-roberta-large`` | | ~550M parameters with 24-layers, 1024-hidden-state, 4096 feed-forward hidden-state, 16-heads, |
|
|
| | | | Trained on 2.5 TB of newly created clean CommonCrawl data in 100 languages |
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|
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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|
| 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 |
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|
| | | |
|
|
| | | (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`` | | 24-layer, 1024-hidden, 16-heads, 406M parameters (same as large) |
|
|
| | | | bart-large base architecture finetuned on cnn summarization task |
|
|
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
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|
| 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>`__) |
|
|
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
|
| LayoutLM | ``microsoft/layoutlm-base-uncased`` | | 12 layers, 768-hidden, 12-heads, 113M parameters |
|
|
| | | |
|
|
| | | (see `details <https://github.com/microsoft/unilm/tree/master/layoutlm>`__) |
|
|
+ +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
|
| | ``microsoft/layoutlm-large-uncased`` | | 24 layers, 1024-hidden, 16-heads, 343M parameters |
|
|
| | | |
|
|
| | | (see `details <https://github.com/microsoft/unilm/tree/master/layoutlm>`__) |
|
|
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
|
| DeBERTa | ``microsoft/deberta-base`` | | 12-layer, 768-hidden, 12-heads, ~125M parameters |
|
|
| | | | DeBERTa using the BERT-base architecture |
|
|
| | | |
|
|
| | | (see `details <https://github.com/microsoft/DeBERTa>`__) |
|
|
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
|
| | ``microsoft/deberta-large`` | | 24-layer, 1024-hidden, 16-heads, ~390M parameters |
|
|
| | | | DeBERTa using the BERT-large architecture |
|
|
| | | |
|
|
| | | (see `details <https://github.com/microsoft/DeBERTa>`__) |
|
|
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
|
| SqueezeBERT | ``squeezebert/squeezebert-uncased`` | | 12-layer, 768-hidden, 12-heads, 51M parameters, 4.3x faster than bert-base-uncased on a smartphone. |
|
|
| | | | SqueezeBERT architecture pretrained from scratch on masked language model (MLM) and sentence order prediction (SOP) tasks. |
|
|
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
|
| | ``squeezebert/squeezebert-mnli`` | | 12-layer, 768-hidden, 12-heads, 51M parameters, 4.3x faster than bert-base-uncased on a smartphone. |
|
|
| | | | This is the squeezebert-uncased model finetuned on MNLI sentence pair classification task with distillation from electra-base. |
|
|
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
|
| | ``squeezebert/squeezebert-mnli-headless`` | | 12-layer, 768-hidden, 12-heads, 51M parameters, 4.3x faster than bert-base-uncased on a smartphone. |
|
|
| | | | This is the squeezebert-uncased model finetuned on MNLI sentence pair classification task with distillation from electra-base. |
|
|
| | | | The final classification layer is removed, so when you finetune, the final layer will be reinitialized. |
|
|
+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|