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.gitattributes

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

README.md

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+---
+language:
+- be
+- en
+- ru
+- uk
+- zle
+
+tags:
+- translation
+
+license: cc-by-4.0
+model-index:
+- name: opus-mt-tc-big-zle-en
+  results:
+  - task:
+      name: Translation rus-eng
+      type: translation
+      args: rus-eng
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: rus eng devtest
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 35.2
+  - task:
+      name: Translation ukr-eng
+      type: translation
+      args: ukr-eng
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: ukr eng devtest
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 39.2
+  - task:
+      name: Translation bel-eng
+      type: translation
+      args: bel-eng
+    dataset:
+      name: tatoeba-test-v2021-08-07
+      type: tatoeba_mt
+      args: bel-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 48.1
+  - task:
+      name: Translation rus-eng
+      type: translation
+      args: rus-eng
+    dataset:
+      name: tatoeba-test-v2021-08-07
+      type: tatoeba_mt
+      args: rus-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 57.4
+  - task:
+      name: Translation ukr-eng
+      type: translation
+      args: ukr-eng
+    dataset:
+      name: tatoeba-test-v2021-08-07
+      type: tatoeba_mt
+      args: ukr-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 56.9
+  - task:
+      name: Translation rus-eng
+      type: translation
+      args: rus-eng
+    dataset:
+      name: tico19-test
+      type: tico19-test
+      args: rus-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 33.3
+  - task:
+      name: Translation rus-eng
+      type: translation
+      args: rus-eng
+    dataset:
+      name: newstest2012
+      type: wmt-2012-news
+      args: rus-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 39.2
+  - task:
+      name: Translation rus-eng
+      type: translation
+      args: rus-eng
+    dataset:
+      name: newstest2013
+      type: wmt-2013-news
+      args: rus-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 31.3
+  - task:
+      name: Translation rus-eng
+      type: translation
+      args: rus-eng
+    dataset:
+      name: newstest2014
+      type: wmt-2014-news
+      args: rus-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 40.5
+  - task:
+      name: Translation rus-eng
+      type: translation
+      args: rus-eng
+    dataset:
+      name: newstest2015
+      type: wmt-2015-news
+      args: rus-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 36.1
+  - task:
+      name: Translation rus-eng
+      type: translation
+      args: rus-eng
+    dataset:
+      name: newstest2016
+      type: wmt-2016-news
+      args: rus-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 35.7
+  - task:
+      name: Translation rus-eng
+      type: translation
+      args: rus-eng
+    dataset:
+      name: newstest2017
+      type: wmt-2017-news
+      args: rus-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 40.8
+  - task:
+      name: Translation rus-eng
+      type: translation
+      args: rus-eng
+    dataset:
+      name: newstest2018
+      type: wmt-2018-news
+      args: rus-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 35.2
+  - task:
+      name: Translation rus-eng
+      type: translation
+      args: rus-eng
+    dataset:
+      name: newstest2019
+      type: wmt-2019-news
+      args: rus-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 41.6
+  - task:
+      name: Translation rus-eng
+      type: translation
+      args: rus-eng
+    dataset:
+      name: newstest2020
+      type: wmt-2020-news
+      args: rus-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 36.9
+---
+# opus-mt-tc-big-zle-en
+
+Neural machine translation model for translating from East Slavic languages (zle) 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: big_2022-03-17
+* source language(s): bel rus ukr
+* target language(s): eng
+* valid target language labels: >>eng<<
+* model: transformer-big (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/zle-eng/opusTCv20210807+bt_transformer-big_2022-03-17.zip)
+* more information released models: [OPUS-MT zle-eng README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zle-eng/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. `>>eng<<`
+
+## Usage
+
+A short example code:
+
+```python
+from transformers import MarianMTModel, MarianTokenizer
+
+src_text = [
+    "Скільки мені слід купити пива?",
+    "Я клієнтка."
+]
+
+model_name = "pytorch-models/opus-mt-tc-big-zle-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:
+#     How much beer should I buy?
+#     I'm a client.
+```
+
+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-zle-en")
+print(pipe("Скільки мені слід купити пива?"))
+
+# expected output: How much beer should I buy?
+```
+
+## Benchmarks
+
+* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zle-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/zle-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 |
+|----------|---------|-------|-------|-------|--------|
+| bel-eng | tatoeba-test-v2021-08-07 | 0.65221 | 48.1 | 2500 | 18571 |
+| rus-eng | tatoeba-test-v2021-08-07 | 0.71452 | 57.4 | 19425 | 147872 |
+| ukr-eng | tatoeba-test-v2021-08-07 | 0.71162 | 56.9 | 13127 | 88607 |
+| bel-eng | flores101-devtest | 0.51689 | 18.1 | 1012 | 24721 |
+| rus-eng | flores101-devtest | 0.62581 | 35.2 | 1012 | 24721 |
+| ukr-eng | flores101-devtest | 0.65001 | 39.2 | 1012 | 24721 |
+| rus-eng | newstest2012 | 0.63724 | 39.2 | 3003 | 72812 |
+| rus-eng | newstest2013 | 0.57641 | 31.3 | 3000 | 64505 |
+| rus-eng | newstest2014 | 0.65667 | 40.5 | 3003 | 69190 |
+| rus-eng | newstest2015 | 0.61747 | 36.1 | 2818 | 64428 |
+| rus-eng | newstest2016 | 0.61414 | 35.7 | 2998 | 69278 |
+| rus-eng | newstest2017 | 0.65365 | 40.8 | 3001 | 69025 |
+| rus-eng | newstest2018 | 0.61386 | 35.2 | 3000 | 71291 |
+| rus-eng | newstest2019 | 0.65476 | 41.6 | 2000 | 42642 |
+| rus-eng | newstest2020 | 0.64878 | 36.9 | 991 | 20217 |
+| rus-eng | newstestB2020 | 0.65685 | 39.3 | 991 | 20423 |
+| rus-eng | tico19-test | 0.63280 | 33.3 | 2100 | 56323 |
+
+## 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: f084bad
+* port time: Mon Mar 21 23:10:40 EET 2022
+* port machine: LM0-400-22516.local

benchmark_results.txt

@@ -0,0 +1,26 @@
+bel-eng	flores101-dev	0.52003	18.0	997	23555
+rus-eng	flores101-dev	0.62931	35.2	997	23555
+bel-eng	flores101-devtest	0.51689	18.1	1012	24721
+rus-eng	flores101-devtest	0.62581	35.2	1012	24721
+ukr-eng	flores101-devtest	0.65001	39.2	1012	24721
+ukr-eng	flores101-dev	0.65088	39.3	997	23555
+rus-eng	newstest2012	0.63724	39.2	3003	72812
+rus-eng	newstest2013	0.57641	31.3	3000	64505
+rus-eng	newstest2014	0.65667	40.5	3003	69190
+rus-eng	newstest2015	0.61747	36.1	2818	64428
+rus-eng	newstest2016	0.61414	35.7	2998	69278
+rus-eng	newstest2017	0.65365	40.8	3001	69025
+rus-eng	newstest2018	0.61386	35.2	3000	71291
+rus-eng	newstest2019	0.65476	41.6	2000	42642
+rus-eng	newstest2020	0.64878	36.9	991	20217
+rus-eng	newstestB2020	0.65685	39.3	991	20423
+bel-eng	tatoeba-test-v2020-07-28	0.65221	48.1	2500	18571
+rus-eng	tatoeba-test-v2020-07-28	0.72653	59.4	10000	72902
+ukr-eng	tatoeba-test-v2020-07-28	0.70935	56.8	10000	66118
+bel-eng	tatoeba-test-v2021-03-30	0.65221	48.1	2500	18571
+rus-eng	tatoeba-test-v2021-03-30	0.72153	58.5	15118	111813
+ukr-eng	tatoeba-test-v2021-03-30	0.71069	56.9	11969	80246
+bel-eng	tatoeba-test-v2021-08-07	0.65221	48.1	2500	18571
+rus-eng	tatoeba-test-v2021-08-07	0.71452	57.4	19425	147872
+ukr-eng	tatoeba-test-v2021-08-07	0.71162	56.9	13127	88607
+rus-eng	tico19-test	0.63280	33.3	2100	56323

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+  "static_position_embeddings": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.18.0.dev0",
+  "use_cache": true,
+  "vocab_size": 61017
+}

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