65 lines
2.7 KiB
Markdown
65 lines
2.7 KiB
Markdown
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
|
|
the License. You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
|
|
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
|
|
specific language governing permissions and limitations under the License.
|
|
|
|
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
|
|
rendered properly in your Markdown viewer.
|
|
|
|
-->
|
|
|
|
# FSMT
|
|
|
|
## Overview
|
|
|
|
FSMT (FairSeq MachineTranslation) models were introduced in [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616) by Nathan Ng, Kyra Yee, Alexei Baevski, Myle Ott, Michael Auli, Sergey Edunov.
|
|
|
|
The abstract of the paper is the following:
|
|
|
|
*This paper describes Facebook FAIR's submission to the WMT19 shared news translation task. We participate in two
|
|
language pairs and four language directions, English <-> German and English <-> Russian. Following our submission from
|
|
last year, our baseline systems are large BPE-based transformer models trained with the Fairseq sequence modeling
|
|
toolkit which rely on sampled back-translations. This year we experiment with different bitext data filtering schemes,
|
|
as well as with adding filtered back-translated data. We also ensemble and fine-tune our models on domain-specific
|
|
data, then decode using noisy channel model reranking. Our submissions are ranked first in all four directions of the
|
|
human evaluation campaign. On En->De, our system significantly outperforms other systems as well as human translations.
|
|
This system improves upon our WMT'18 submission by 4.5 BLEU points.*
|
|
|
|
This model was contributed by [stas](https://huggingface.co/stas). The original code can be found
|
|
[here](https://github.com/pytorch/fairseq/tree/master/examples/wmt19).
|
|
|
|
## Implementation Notes
|
|
|
|
- FSMT uses source and target vocabulary pairs that aren't combined into one. It doesn't share embeddings tokens
|
|
either. Its tokenizer is very similar to [`XLMTokenizer`] and the main model is derived from
|
|
[`BartModel`].
|
|
|
|
|
|
## FSMTConfig
|
|
|
|
[[autodoc]] FSMTConfig
|
|
|
|
## FSMTTokenizer
|
|
|
|
[[autodoc]] FSMTTokenizer
|
|
- build_inputs_with_special_tokens
|
|
- get_special_tokens_mask
|
|
- create_token_type_ids_from_sequences
|
|
- save_vocabulary
|
|
|
|
## FSMTModel
|
|
|
|
[[autodoc]] FSMTModel
|
|
- forward
|
|
|
|
## FSMTForConditionalGeneration
|
|
|
|
[[autodoc]] FSMTForConditionalGeneration
|
|
- forward
|