File size: 7,166 Bytes
5010e32
 
 
 
 
 
584fb87
5010e32
 
 
 
 
 
 
 
 
 
 
 
 
584fb87
 
 
5010e32
 
 
 
 
 
 
 
 
584fb87
 
 
5010e32
 
 
 
 
 
 
 
 
584fb87
 
 
5010e32
 
 
 
 
 
 
 
 
584fb87
 
 
5010e32
 
 
 
 
 
 
 
 
584fb87
 
 
5010e32
 
 
 
 
 
 
 
 
584fb87
 
 
5010e32
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
---
language:
- en
- tr
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-tr-en
  results:
  - task:
      name: Translation tur-eng
      type: translation
      args: tur-eng
    dataset:
      name: flores101-devtest
      type: flores_101
      args: tur eng devtest
    metrics:
    - name: BLEU
      type: bleu
      value: 37.6
  - task:
      name: Translation tur-eng
      type: translation
      args: tur-eng
    dataset:
      name: newsdev2016
      type: newsdev2016
      args: tur-eng
    metrics:
    - name: BLEU
      type: bleu
      value: 32.1
  - task:
      name: Translation tur-eng
      type: translation
      args: tur-eng
    dataset:
      name: tatoeba-test-v2021-08-07
      type: tatoeba_mt
      args: tur-eng
    metrics:
    - name: BLEU
      type: bleu
      value: 57.6
  - task:
      name: Translation tur-eng
      type: translation
      args: tur-eng
    dataset:
      name: newstest2016
      type: wmt-2016-news
      args: tur-eng
    metrics:
    - name: BLEU
      type: bleu
      value: 29.3
  - task:
      name: Translation tur-eng
      type: translation
      args: tur-eng
    dataset:
      name: newstest2017
      type: wmt-2017-news
      args: tur-eng
    metrics:
    - name: BLEU
      type: bleu
      value: 29.7
  - task:
      name: Translation tur-eng
      type: translation
      args: tur-eng
    dataset:
      name: newstest2018
      type: wmt-2018-news
      args: tur-eng
    metrics:
    - name: BLEU
      type: bleu
      value: 30.7
---
# opus-mt-tc-big-tr-en

Neural machine translation model for translating from Turkish (tr) to English (en).

This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of [Marian NMT](https://marian-nmt.github.io/), an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from [OPUS](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train).

* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)

```
@inproceedings{tiedemann-thottingal-2020-opus,
    title = "{OPUS}-{MT} {--} Building open translation services for the World",
    author = {Tiedemann, J{\"o}rg  and Thottingal, Santhosh},
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://aclanthology.org/2020.eamt-1.61",
    pages = "479--480",
}

@inproceedings{tiedemann-2020-tatoeba,
    title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
    author = {Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.wmt-1.139",
    pages = "1174--1182",
}
```

## Model info

* Release: 2022-03-17
* source language(s): tur
* target language(s): eng
* model: transformer-big
* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
* tokenization: SentencePiece (spm32k,spm32k)
* original model: [opusTCv20210807+bt_transformer-big_2022-03-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/tur-eng/opusTCv20210807+bt_transformer-big_2022-03-17.zip)
* more information released models: [OPUS-MT tur-eng README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tur-eng/README.md)

## Usage

A short example code:

```python
from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "Allahsızlığı Yayma Kürsüsü başkanıydı.",
    "Tom'a ne olduğunu öğrenin."
]

model_name = "pytorch-models/opus-mt-tc-big-tr-en"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))

for t in translated:
    print( tokenizer.decode(t, skip_special_tokens=True) )

# expected output:
#     He was the president of the Curse of Spreading Godlessness.
#     Find out what happened to Tom.
```

You can also use OPUS-MT models with the transformers pipelines, for example:

```python
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-tr-en")
print(pipe("Allahsızlığı Yayma Kürsüsü başkanıydı."))

# expected output: He was the president of the Curse of Spreading Godlessness.
```

## Benchmarks

* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/tur-eng/opusTCv20210807+bt_transformer-big_2022-03-17.test.txt)
* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/tur-eng/opusTCv20210807+bt_transformer-big_2022-03-17.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip)

| langpair | testset | chr-F | BLEU  | #sent | #words |
|----------|---------|-------|-------|-------|--------|
| tur-eng | tatoeba-test-v2021-08-07 | 0.71895 | 57.6 | 13907 | 109231 |
| tur-eng | flores101-devtest | 0.64152 | 37.6 | 1012 | 24721 |
| tur-eng | newsdev2016 | 0.58658 | 32.1 | 1001 | 21988 |
| tur-eng | newstest2016 | 0.56960 | 29.3 | 3000 | 66175 |
| tur-eng | newstest2017 | 0.57455 | 29.7 | 3007 | 67703 |
| tur-eng | newstest2018 | 0.58488 | 30.7 | 3000 | 68725 |

## Acknowledgements

The work is supported by the [European Language Grid](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland.

## Model conversion info

* transformers version: 4.16.2
* OPUS-MT git hash: 3405783
* port time: Wed Apr 13 20:02:48 EEST 2022
* port machine: LM0-400-22516.local