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9e147d31f6
* Deprecate prepare_seq2seq_batch * Fix last tests * Apply suggestions from code review Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Suraj Patil <surajp815@gmail.com> * More review comments Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Suraj Patil <surajp815@gmail.com>
74 lines
3.6 KiB
ReStructuredText
74 lines
3.6 KiB
ReStructuredText
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Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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FSMT
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-----------------------------------------------------------------------------------------------------------------------
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**DISCLAIMER:** If you see something strange, file a `Github Issue
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<https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title>`__ and assign
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@stas00.
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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FSMT (FairSeq MachineTranslation) models were introduced in `Facebook FAIR's WMT19 News Translation Task Submission
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<https://arxiv.org/abs/1907.06616>`__ by Nathan Ng, Kyra Yee, Alexei Baevski, Myle Ott, Michael Auli, Sergey Edunov.
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The abstract of the paper is the following:
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*This paper describes Facebook FAIR's submission to the WMT19 shared news translation task. We participate in two
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language pairs and four language directions, English <-> German and English <-> Russian. Following our submission from
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last year, our baseline systems are large BPE-based transformer models trained with the Fairseq sequence modeling
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toolkit which rely on sampled back-translations. This year we experiment with different bitext data filtering schemes,
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as well as with adding filtered back-translated data. We also ensemble and fine-tune our models on domain-specific
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data, then decode using noisy channel model reranking. Our submissions are ranked first in all four directions of the
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human evaluation campaign. On En->De, our system significantly outperforms other systems as well as human translations.
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This system improves upon our WMT'18 submission by 4.5 BLEU points.*
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The original code can be found here <https://github.com/pytorch/fairseq/tree/master/examples/wmt19>__.
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Implementation Notes
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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- FSMT uses source and target vocabulary pairs that aren't combined into one. It doesn't share embeddings tokens
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either. Its tokenizer is very similar to :class:`~transformers.XLMTokenizer` and the main model is derived from
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:class:`~transformers.BartModel`.
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FSMTConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.FSMTConfig
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:members:
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FSMTTokenizer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.FSMTTokenizer
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:members: build_inputs_with_special_tokens, get_special_tokens_mask,
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create_token_type_ids_from_sequences, save_vocabulary
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FSMTModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.FSMTModel
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:members: forward
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FSMTForConditionalGeneration
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.FSMTForConditionalGeneration
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:members: forward
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