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.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
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+*.spm filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language:
+- be
+- cs
+- dsb
+- hsb
+- pl
+- ru
+- uk
+- zle
+- zlw
+
+tags:
+- translation
+
+license: cc-by-4.0
+model-index:
+- name: opus-mt-tc-big-zlw-zle
+  results:
+  - task:
+      name: Translation ces-rus
+      type: translation
+      args: ces-rus
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: ces rus devtest
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 24.2
+  - task:
+      name: Translation ces-ukr
+      type: translation
+      args: ces-ukr
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: ces ukr devtest
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 22.9
+  - task:
+      name: Translation pol-rus
+      type: translation
+      args: pol-rus
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: pol rus devtest
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 20.1
+  - task:
+      name: Translation ces-rus
+      type: translation
+      args: ces-rus
+    dataset:
+      name: tatoeba-test-v2021-08-07
+      type: tatoeba_mt
+      args: ces-rus
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 56.4
+  - task:
+      name: Translation ces-ukr
+      type: translation
+      args: ces-ukr
+    dataset:
+      name: tatoeba-test-v2021-08-07
+      type: tatoeba_mt
+      args: ces-ukr
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 53.0
+  - task:
+      name: Translation pol-bel
+      type: translation
+      args: pol-bel
+    dataset:
+      name: tatoeba-test-v2021-08-07
+      type: tatoeba_mt
+      args: pol-bel
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 29.4
+  - task:
+      name: Translation pol-rus
+      type: translation
+      args: pol-rus
+    dataset:
+      name: tatoeba-test-v2021-08-07
+      type: tatoeba_mt
+      args: pol-rus
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 55.3
+  - task:
+      name: Translation pol-ukr
+      type: translation
+      args: pol-ukr
+    dataset:
+      name: tatoeba-test-v2021-08-07
+      type: tatoeba_mt
+      args: pol-ukr
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 48.6
+  - task:
+      name: Translation ces-rus
+      type: translation
+      args: ces-rus
+    dataset:
+      name: newstest2012
+      type: wmt-2012-news
+      args: ces-rus
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 21.0
+  - task:
+      name: Translation ces-rus
+      type: translation
+      args: ces-rus
+    dataset:
+      name: newstest2013
+      type: wmt-2013-news
+      args: ces-rus
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 27.2
+---
+# opus-mt-tc-big-zlw-zle
+
+Neural machine translation model for translating from West Slavic languages (zlw) to East Slavic languages (zle).
+
+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: 2022-03-19
+* source language(s): ces dsb hsb pol
+* target language(s): bel rus ukr
+* valid target language labels: >>bel<< >>rus<< >>ukr<<
+* model: transformer-big
+* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
+* tokenization: SentencePiece (spm32k,spm32k)
+* original model: [opusTCv20210807+bt_transformer-big_2022-03-19.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/zlw-zle/opusTCv20210807+bt_transformer-big_2022-03-19.zip)
+* more information released models: [OPUS-MT zlw-zle README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zlw-zle/README.md)
+* more information about the model: [MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)
+
+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. `>>bel<<`
+
+## Usage
+
+A short example code:
+
+```python
+from transformers import MarianMTModel, MarianTokenizer
+
+src_text = [
+    ">>rus<< Je vystudovaný právník.",
+    ">>rus<< Gdzie jest moja książka ?"
+]
+
+model_name = "pytorch-models/opus-mt-tc-big-zlw-zle"
+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:
+#     Он дипломированный юрист.
+#     Где моя книга?
+```
+
+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-zlw-zle")
+print(pipe(">>rus<< Je vystudovaný právník."))
+
+# expected output: Он дипломированный юрист.
+```
+
+## Benchmarks
+
+* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-19.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zlw-zle/opusTCv20210807+bt_transformer-big_2022-03-19.test.txt)
+* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-19.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zlw-zle/opusTCv20210807+bt_transformer-big_2022-03-19.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 |
+|----------|---------|-------|-------|-------|--------|
+| ces-rus | tatoeba-test-v2021-08-07 | 0.73154 | 56.4 | 2934 | 17790 |
+| ces-ukr | tatoeba-test-v2021-08-07 | 0.69934 | 53.0 | 1787 | 8891 |
+| pol-bel | tatoeba-test-v2021-08-07 | 0.51039 | 29.4 | 287 | 1730 |
+| pol-rus | tatoeba-test-v2021-08-07 | 0.73156 | 55.3 | 3543 | 22067 |
+| pol-ukr | tatoeba-test-v2021-08-07 | 0.68247 | 48.6 | 2519 | 13535 |
+| ces-rus | flores101-devtest | 0.52316 | 24.2 | 1012 | 23295 |
+| ces-ukr | flores101-devtest | 0.52261 | 22.9 | 1012 | 22810 |
+| pol-rus | flores101-devtest | 0.49414 | 20.1 | 1012 | 23295 |
+| pol-ukr | flores101-devtest | 0.48250 | 18.3 | 1012 | 22810 |
+| ces-rus | newstest2012 | 0.49469 | 21.0 | 3003 | 64790 |
+| ces-rus | newstest2013 | 0.54197 | 27.2 | 3000 | 58560 |
+
+## 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: 1bdabf7
+* port time: Thu Mar 24 04:13:23 EET 2022
+* port machine: LM0-400-22516.local

benchmark_results.txt

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+ces-bel	flores101-dev	0.26213	5.4	997	23996
+ces-rus	flores101-dev	0.52210	24.0	997	22657
+ces-ukr	flores101-dev	0.51171	21.6	997	21841
+pol-bel	flores101-dev	0.24387	4.8	997	23996
+pol-rus	flores101-dev	0.49391	20.2	997	22657
+pol-ukr	flores101-dev	0.47783	18.0	997	21841
+ces-bel	flores101-devtest	0.25834	5.2	1012	24829
+ces-rus	flores101-devtest	0.52316	24.2	1012	23295
+ces-ukr	flores101-devtest	0.52261	22.9	1012	22810
+pol-bel	flores101-devtest	0.24450	4.9	1012	24829
+pol-rus	flores101-devtest	0.49414	20.1	1012	23295
+pol-ukr	flores101-devtest	0.48250	18.3	1012	22810
+ces-rus	newstest2012	0.49469	21.0	3003	64790
+ces-rus	newstest2013	0.54197	27.2	3000	58560
+ces-rus	tatoeba-test-v2020-07-28	0.73377	56.5	2500	15084
+ces-ukr	tatoeba-test-v2020-07-28	0.69934	53.0	1787	8891
+pol-bel	tatoeba-test-v2020-07-28	0.51039	29.4	287	1730
+pol-rus	tatoeba-test-v2020-07-28	0.73156	55.3	3543	22067
+pol-ukr	tatoeba-test-v2020-07-28	0.68259	48.6	2500	13434
+ces-rus	tatoeba-test-v2021-03-30	0.73212	56.3	5060	30636
+ces-ukr	tatoeba-test-v2021-03-30	0.69934	53.0	1787	8891
+pol-bel	tatoeba-test-v2021-03-30	0.51120	29.5	289	1743
+pol-rus	tatoeba-test-v2021-03-30	0.73156	55.3	3543	22067
+pol-ukr	tatoeba-test-v2021-03-30	0.68225	48.6	4977	26782
+ces-rus	tatoeba-test-v2021-08-07	0.73154	56.4	2934	17790
+ces-ukr	tatoeba-test-v2021-08-07	0.69934	53.0	1787	8891
+pol-bel	tatoeba-test-v2021-08-07	0.51039	29.4	287	1730
+pol-rus	tatoeba-test-v2021-08-07	0.73156	55.3	3543	22067
+pol-ukr	tatoeba-test-v2021-08-07	0.68247	48.6	2519	13535

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+  "model_type": "marian",
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+  "static_position_embeddings": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.18.0.dev0",
+  "use_cache": true,
+  "vocab_size": 61592
+}

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