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* first copy & past commit from Bert and morgans LSH code * add easy way to compare to trax original code * translate most of function * make trax lsh self attention deterministic with numpy seed + copy paste code * add same config * add same config * make layer init work * implemented hash_vectors function for lsh attention * continue reformer translation * hf LSHSelfAttentionLayer gives same output as trax layer * refactor code * refactor code * refactor code * refactor * refactor + add reformer config * delete bogus file * split reformer attention layer into two layers * save intermediate step * save intermediate step * make test work * add complete reformer block layer * finish reformer layer * implement causal and self mask * clean reformer test and refactor code * fix merge conflicts * fix merge conflicts * update init * fix device for GPU * fix chunk length init for tests * include morgans optimization * improve memory a bit * improve comment * factorize num_buckets * better testing parameters * make whole model work * make lm model work * add t5 copy paste tokenizer * add chunking feed forward * clean config * add improved assert statements * make tokenizer work * improve test * correct typo * extend config * add complexer test * add new axial position embeddings * add local block attention layer * clean tests * refactor * better testing * save intermediate progress * clean test file * make shorter input length work for model * allow variable input length * refactor * make forward pass for pretrained model work * add generation possibility * finish dropout and init * make style * refactor * add first version of RevNet Layers * make forward pass work and add convert file * make uploaded model forward pass work * make uploaded model forward pass work * refactor code * add namedtuples and cache buckets * correct head masks * refactor * made reformer more flexible * make style * remove set max length * add attention masks * fix up tests * fix lsh attention mask * make random seed optional for the moment * improve memory in reformer * add tests * make style * make sure masks work correctly * detach gradients * save intermediate * correct backprob through gather * make style * change back num hashes * rename to labels * fix rotation shape * fix detach * update * fix trainer * fix backward dropout * make reformer more flexible * fix conflict * fix * fix * add tests for fixed seed in reformer layer * fix trainer typo * fix typo in activations * add fp16 tests * add fp16 training * support fp16 * correct gradient bug in reformer * add fast gelu * re-add dropout for embedding dropout * better naming * better naming * renaming * finalize test branch * finalize tests * add more tests * finish tests * fix * fix type trainer * fix fp16 tests * fix tests * fix tests * fix tests * fix issue with dropout * fix dropout seeds * correct random seed on gpu * finalize random seed for dropout * finalize random seed for dropout * remove duplicate line * correct half precision bug * make style * refactor * refactor * docstring * remove sinusoidal position encodings for reformer * move chunking to modeling_utils * make style * clean config * make style * fix tests * fix auto tests * pretrained models * fix docstring * update conversion file * Update pretrained_models.rst * fix rst * fix rst * update copyright * fix test path * fix test path * fix small issue in test * include reformer in generation tests * add docs for axial position encoding * finish docs * Update convert_reformer_trax_checkpoint_to_pytorch.py * remove isort * include sams comments * remove wrong comment in utils * correct typos * fix typo * Update reformer.rst * applied morgans optimization * make style * make gpu compatible * remove bogus file * big test refactor * add example for chunking * fix typo * add to README
302 lines
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302 lines
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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 <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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| | | (see `details <https://github.com/google-research/bert/blob/master/multilingual.md>`__). |
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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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| | | (see `details <https://github.com/google-research/bert/blob/master/multilingual.md>`__). |
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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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| | | (see `details on deepset.ai website <https://deepset.ai/german-bert>`__). |
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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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| | | (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, 340M 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, 340M 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, 340M 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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| | ``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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| | ``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. |
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| | | | `MeCab <https://taku910.github.io/mecab/>`__ is required for tokenization. |
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| | | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__). |
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| | ``bert-base-japanese-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 with MeCab and WordPiece. |
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| | | | `MeCab <https://taku910.github.io/mecab/>`__ is required for tokenization. |
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| | | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__). |
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| | ``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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| | | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__). |
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| | ``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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| | | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__). |
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| | ``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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| | | (see `details on turkunlp.org <http://turkunlp.org/FinBERT/>`__). |
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| | ``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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| | ``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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| | ``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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| | ``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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| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
|
| | ``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-small-cased`` | | 6-layer, 512-hidden, 8-heads, 54M parameters |
|
|
| | | | FlauBERT small architecture |
|
|
| | | (see `details <https://github.com/getalp/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-base-cased`` | | 12-layer, 768-hidden, 12-heads, 138M parameters |
|
|
| | | | FlauBERT base architecture with cased vocabulary |
|
|
| | | (see `details <https://github.com/getalp/Flaubert>`__) |
|
|
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
|
| | ``flaubert-large-cased`` | | 24-layer, 1024-hidden, 16-heads, 373M parameters |
|
|
| | | | FlauBERT large architecture |
|
|
| | | (see `details <https://github.com/getalp/Flaubert>`__) |
|
|
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
|
| Bart | ``bart-large`` | | 12-layer, 1024-hidden, 16-heads, 406M parameters |
|
|
| | | (see `details <https://github.com/pytorch/fairseq/tree/master/examples/bart>`_) |
|
|
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
|
| | ``bart-large-mnli`` | | Adds a 2 layer classification head with 1 million parameters |
|
|
| | | | bart-large base architecture with a classification head, finetuned on MNLI |
|
|
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
|
| | ``bart-large-cnn`` | | 12-layer, 1024-hidden, 16-heads, 406M parameters (same as base) |
|
|
| | | | bart-large base architecture finetuned on cnn summarization task |
|
|
| +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
|
| | ``mbart-large-en-ro`` | | 12-layer, 1024-hidden, 16-heads, 880M parameters |
|
|
| | | | bart-large architecture pretrained on cc25 multilingual data , finetuned on WMT english romanian translation. |
|
|
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|
|
| 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-crime-and-punishment`` | | 6-layer, 256-hidden, 2-heads, 3M parameters |
|
|
| | | | Trained on English text: Crime and Punishment novel by Fyodor Dostoyevsky |
|
|
+-------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
|