File size: 18,933 Bytes
55e1a3a |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 |
---
library_name: transformers
language:
- de
- en
- es
- fr
- lt
- lv
- prg
- pt
- sgs
tags:
- translation
- opus-mt-tc-bible
license: apache-2.0
model-index:
- name: opus-mt-tc-bible-big-deu_eng_fra_por_spa-bat
results:
- task:
name: Translation deu-lit
type: translation
args: deu-lit
dataset:
name: flores200-devtest
type: flores200-devtest
args: deu-lit
metrics:
- name: BLEU
type: bleu
value: 22.6
- name: chr-F
type: chrf
value: 0.54957
- task:
name: Translation eng-lit
type: translation
args: eng-lit
dataset:
name: flores200-devtest
type: flores200-devtest
args: eng-lit
metrics:
- name: BLEU
type: bleu
value: 27.7
- name: chr-F
type: chrf
value: 0.59338
- task:
name: Translation fra-lit
type: translation
args: fra-lit
dataset:
name: flores200-devtest
type: flores200-devtest
args: fra-lit
metrics:
- name: BLEU
type: bleu
value: 22.3
- name: chr-F
type: chrf
value: 0.54683
- task:
name: Translation por-lit
type: translation
args: por-lit
dataset:
name: flores200-devtest
type: flores200-devtest
args: por-lit
metrics:
- name: BLEU
type: bleu
value: 22.6
- name: chr-F
type: chrf
value: 0.55033
- task:
name: Translation spa-lit
type: translation
args: spa-lit
dataset:
name: flores200-devtest
type: flores200-devtest
args: spa-lit
metrics:
- name: BLEU
type: bleu
value: 16.9
- name: chr-F
type: chrf
value: 0.50725
- task:
name: Translation deu-lav
type: translation
args: deu-lav
dataset:
name: flores101-devtest
type: flores_101
args: deu lav devtest
metrics:
- name: BLEU
type: bleu
value: 24.4
- name: chr-F
type: chrf
value: 0.54724
- task:
name: Translation eng-lav
type: translation
args: eng-lav
dataset:
name: flores101-devtest
type: flores_101
args: eng lav devtest
metrics:
- name: BLEU
type: bleu
value: 31.0
- name: chr-F
type: chrf
value: 0.59955
- task:
name: Translation eng-lit
type: translation
args: eng-lit
dataset:
name: flores101-devtest
type: flores_101
args: eng lit devtest
metrics:
- name: BLEU
type: bleu
value: 27.2
- name: chr-F
type: chrf
value: 0.58961
- task:
name: Translation fra-lav
type: translation
args: fra-lav
dataset:
name: flores101-devtest
type: flores_101
args: fra lav devtest
metrics:
- name: BLEU
type: bleu
value: 24.2
- name: chr-F
type: chrf
value: 0.54276
- task:
name: Translation fra-lit
type: translation
args: fra-lit
dataset:
name: flores101-devtest
type: flores_101
args: fra lit devtest
metrics:
- name: BLEU
type: bleu
value: 22.4
- name: chr-F
type: chrf
value: 0.54665
- task:
name: Translation spa-lav
type: translation
args: spa-lav
dataset:
name: flores101-devtest
type: flores_101
args: spa lav devtest
metrics:
- name: BLEU
type: bleu
value: 17.8
- name: chr-F
type: chrf
value: 0.50131
- task:
name: Translation deu-lav
type: translation
args: deu-lav
dataset:
name: ntrex128
type: ntrex128
args: deu-lav
metrics:
- name: BLEU
type: bleu
value: 16.8
- name: chr-F
type: chrf
value: 0.47980
- task:
name: Translation deu-lit
type: translation
args: deu-lit
dataset:
name: ntrex128
type: ntrex128
args: deu-lit
metrics:
- name: BLEU
type: bleu
value: 17.6
- name: chr-F
type: chrf
value: 0.50645
- task:
name: Translation eng-lav
type: translation
args: eng-lav
dataset:
name: ntrex128
type: ntrex128
args: eng-lav
metrics:
- name: BLEU
type: bleu
value: 20.6
- name: chr-F
type: chrf
value: 0.51026
- task:
name: Translation eng-lit
type: translation
args: eng-lit
dataset:
name: ntrex128
type: ntrex128
args: eng-lit
metrics:
- name: BLEU
type: bleu
value: 21.5
- name: chr-F
type: chrf
value: 0.54187
- task:
name: Translation fra-lav
type: translation
args: fra-lav
dataset:
name: ntrex128
type: ntrex128
args: fra-lav
metrics:
- name: BLEU
type: bleu
value: 15.5
- name: chr-F
type: chrf
value: 0.45346
- task:
name: Translation fra-lit
type: translation
args: fra-lit
dataset:
name: ntrex128
type: ntrex128
args: fra-lit
metrics:
- name: BLEU
type: bleu
value: 16.2
- name: chr-F
type: chrf
value: 0.48870
- task:
name: Translation por-lav
type: translation
args: por-lav
dataset:
name: ntrex128
type: ntrex128
args: por-lav
metrics:
- name: BLEU
type: bleu
value: 17.3
- name: chr-F
type: chrf
value: 0.47809
- task:
name: Translation por-lit
type: translation
args: por-lit
dataset:
name: ntrex128
type: ntrex128
args: por-lit
metrics:
- name: BLEU
type: bleu
value: 17.5
- name: chr-F
type: chrf
value: 0.50653
- task:
name: Translation spa-lav
type: translation
args: spa-lav
dataset:
name: ntrex128
type: ntrex128
args: spa-lav
metrics:
- name: BLEU
type: bleu
value: 17.1
- name: chr-F
type: chrf
value: 0.47690
- task:
name: Translation spa-lit
type: translation
args: spa-lit
dataset:
name: ntrex128
type: ntrex128
args: spa-lit
metrics:
- name: BLEU
type: bleu
value: 17.1
- name: chr-F
type: chrf
value: 0.50412
- task:
name: Translation deu-lit
type: translation
args: deu-lit
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: deu-lit
metrics:
- name: BLEU
type: bleu
value: 39.8
- name: chr-F
type: chrf
value: 0.65379
- task:
name: Translation eng-lav
type: translation
args: eng-lav
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-lav
metrics:
- name: BLEU
type: bleu
value: 46.4
- name: chr-F
type: chrf
value: 0.68823
- task:
name: Translation eng-lit
type: translation
args: eng-lit
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-lit
metrics:
- name: BLEU
type: bleu
value: 39.8
- name: chr-F
type: chrf
value: 0.67792
- task:
name: Translation multi-multi
type: translation
args: multi-multi
dataset:
name: tatoeba-test-v2020-07-28-v2023-09-26
type: tatoeba_mt
args: multi-multi
metrics:
- name: BLEU
type: bleu
value: 43.3
- name: chr-F
type: chrf
value: 0.68018
- task:
name: Translation spa-lit
type: translation
args: spa-lit
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: spa-lit
metrics:
- name: BLEU
type: bleu
value: 43.3
- name: chr-F
type: chrf
value: 0.68133
- task:
name: Translation eng-lav
type: translation
args: eng-lav
dataset:
name: newstest2017
type: wmt-2017-news
args: eng-lav
metrics:
- name: BLEU
type: bleu
value: 21.5
- name: chr-F
type: chrf
value: 0.53192
- task:
name: Translation eng-lit
type: translation
args: eng-lit
dataset:
name: newstest2019
type: wmt-2019-news
args: eng-lit
metrics:
- name: BLEU
type: bleu
value: 18.3
- name: chr-F
type: chrf
value: 0.51714
---
# opus-mt-tc-bible-big-deu_eng_fra_por_spa-bat
## Table of Contents
- [Model Details](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-information)
- [Acknowledgements](#acknowledgements)
## Model Details
Neural machine translation model for translating from unknown (deu+eng+fra+por+spa) to Baltic languages (bat).
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).
**Model Description:**
- **Developed by:** Language Technology Research Group at the University of Helsinki
- **Model Type:** Translation (transformer-big)
- **Release**: 2024-05-30
- **License:** Apache-2.0
- **Language(s):**
- Source Language(s): deu eng fra por spa
- Target Language(s): lav lit prg sgs
- Valid Target Language Labels: >>lav<< >>lit<< >>ndf<< >>olt<< >>prg<< >>prg_Latn<< >>sgs<< >>svx<< >>sxl<< >>xcu<< >>xgl<< >>xsv<< >>xzm<<
- **Original Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-30.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/deu+eng+fra+por+spa-bat/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-30.zip)
- **Resources for more information:**
- [OPUS-MT dashboard](https://opus.nlpl.eu/dashboard/index.php?pkg=opusmt&test=all&scoreslang=all&chart=standard&model=Tatoeba-MT-models/deu%2Beng%2Bfra%2Bpor%2Bspa-bat/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-05-30)
- [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
- [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian)
- [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/)
- [HPLT bilingual data v1 (as part of the Tatoeba Translation Challenge dataset)](https://hplt-project.org/datasets/v1)
- [A massively parallel Bible corpus](https://aclanthology.org/L14-1215/)
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of `>>id<<` (id = valid target language ID), e.g. `>>lav<<`
## Uses
This model can be used for translation and text-to-text generation.
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
## How to Get Started With the Model
A short example code:
```python
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>lav<< Replace this with text in an accepted source language.",
">>sgs<< This is the second sentence."
]
model_name = "pytorch-models/opus-mt-tc-bible-big-deu_eng_fra_por_spa-bat"
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) )
```
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-bible-big-deu_eng_fra_por_spa-bat")
print(pipe(">>lav<< Replace this with text in an accepted source language."))
```
## Training
- **Data**: opusTCv20230926max50+bt+jhubc ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
- **Pre-processing**: SentencePiece (spm32k,spm32k)
- **Model Type:** transformer-big
- **Original MarianNMT Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-30.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/deu+eng+fra+por+spa-bat/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-30.zip)
- **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
## Evaluation
* [Model scores at the OPUS-MT dashboard](https://opus.nlpl.eu/dashboard/index.php?pkg=opusmt&test=all&scoreslang=all&chart=standard&model=Tatoeba-MT-models/deu%2Beng%2Bfra%2Bpor%2Bspa-bat/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-05-30)
* test set translations: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/deu+eng+fra+por+spa-bat/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.test.txt)
* test set scores: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/deu+eng+fra+por+spa-bat/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.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 |
|----------|---------|-------|-------|-------|--------|
| deu-lit | tatoeba-test-v2021-08-07 | 0.65379 | 39.8 | 1115 | 7091 |
| eng-lav | tatoeba-test-v2021-08-07 | 0.68823 | 46.4 | 1631 | 9932 |
| eng-lit | tatoeba-test-v2021-08-07 | 0.67792 | 39.8 | 2528 | 14942 |
| spa-lit | tatoeba-test-v2021-08-07 | 0.68133 | 43.3 | 454 | 2352 |
| deu-lav | flores101-devtest | 0.54724 | 24.4 | 1012 | 22092 |
| eng-lav | flores101-devtest | 0.59955 | 31.0 | 1012 | 22092 |
| eng-lit | flores101-devtest | 0.58961 | 27.2 | 1012 | 20695 |
| fra-lav | flores101-devtest | 0.54276 | 24.2 | 1012 | 22092 |
| fra-lit | flores101-devtest | 0.54665 | 22.4 | 1012 | 20695 |
| spa-lav | flores101-devtest | 0.50131 | 17.8 | 1012 | 22092 |
| deu-lit | flores200-devtest | 0.54957 | 22.6 | 1012 | 20695 |
| eng-lit | flores200-devtest | 0.59338 | 27.7 | 1012 | 20695 |
| fra-lit | flores200-devtest | 0.54683 | 22.3 | 1012 | 20695 |
| por-lit | flores200-devtest | 0.55033 | 22.6 | 1012 | 20695 |
| spa-lit | flores200-devtest | 0.50725 | 16.9 | 1012 | 20695 |
| eng-lav | newstest2017 | 0.53192 | 21.5 | 2001 | 39392 |
| eng-lit | newstest2019 | 0.51714 | 18.3 | 998 | 19711 |
| deu-lav | ntrex128 | 0.47980 | 16.8 | 1997 | 44709 |
| deu-lit | ntrex128 | 0.50645 | 17.6 | 1997 | 41189 |
| eng-lav | ntrex128 | 0.51026 | 20.6 | 1997 | 44709 |
| eng-lit | ntrex128 | 0.54187 | 21.5 | 1997 | 41189 |
| fra-lav | ntrex128 | 0.45346 | 15.5 | 1997 | 44709 |
| fra-lit | ntrex128 | 0.48870 | 16.2 | 1997 | 41189 |
| por-lav | ntrex128 | 0.47809 | 17.3 | 1997 | 44709 |
| por-lit | ntrex128 | 0.50653 | 17.5 | 1997 | 41189 |
| spa-lav | ntrex128 | 0.47690 | 17.1 | 1997 | 44709 |
| spa-lit | ntrex128 | 0.50412 | 17.1 | 1997 | 41189 |
## Citation Information
* Publications: [Democratizing neural machine translation with OPUS-MT](https://doi.org/10.1007/s10579-023-09704-w) and [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.)
```bibtex
@article{tiedemann2023democratizing,
title={Democratizing neural machine translation with {OPUS-MT}},
author={Tiedemann, J{\"o}rg and Aulamo, Mikko and Bakshandaeva, Daria and Boggia, Michele and Gr{\"o}nroos, Stig-Arne and Nieminen, Tommi and Raganato, Alessandro and Scherrer, Yves and Vazquez, Raul and Virpioja, Sami},
journal={Language Resources and Evaluation},
number={58},
pages={713--755},
year={2023},
publisher={Springer Nature},
issn={1574-0218},
doi={10.1007/s10579-023-09704-w}
}
@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",
}
```
## Acknowledgements
The work is supported by the [HPLT project](https://hplt-project.org/), funded by the European Union’s Horizon Europe research and innovation programme under grant agreement No 101070350. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland, and the [EuroHPC supercomputer LUMI](https://www.lumi-supercomputer.eu/).
## Model conversion info
* transformers version: 4.45.1
* OPUS-MT git hash: 0882077
* port time: Tue Oct 8 00:43:04 EEST 2024
* port machine: LM0-400-22516.local
|