blob: dc42b1ca0c9830553847330161cd24ac6e7396d4 (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
|
# Marian examples
Examples, tutorials and use cases for the Marian toolkit.
More information on https://marian-nmt.github.io
List of examples:
* `translating-amun` -- examples for translating with Amun
* `training-basics` -- the complete example for training a WMT16-scale model
* `training-basics-sentencepiece` -- as `training-basics`, but uses built-in SentencePiece for data processing, requires Marian v1.7+
* `transformer` -- scripts for training the transformer model
* `wmt2017-uedin` -- scripts for building a WMT2017-grade model for en-de based on Edinburgh's WMT2017 submission
* `wmt2017-transformer` -- scripts for building a better than WMT2017-grade model for en-de, beating WMT2017 submission by 1.2 BLEU
## Usage
First download common tools:
cd tools
make all
cd ..
Next, go to the chosen directory and run `run-me.sh`, e.g.:
cd training-basics
./run-me.sh
The README file in each directory provides more detailed description.
## Acknowledgements
The development of Marian received funding from the European Union's
_Horizon 2020 Research and Innovation Programme_ under grant agreements
688139 ([SUMMA](http://www.summa-project.eu); 2016-2019),
645487 ([Modern MT](http://www.modernmt.eu); 2015-2017),
644333 ([TraMOOC](http://tramooc.eu/); 2015-2017),
644402 ([HiML](http://www.himl.eu/); 2015-2017),
the Amazon Academic Research Awards program, and
the World Intellectual Property Organization.
This software contains source code provided by NVIDIA Corporation.
|
