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.gitattributes

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

README.md

@@ -0,0 +1,378 @@
+---
+language:
+- cs
+- dsb
+- en
+- hsb
+- pl
+- zlw
+
+tags:
+- translation
+
+license: cc-by-4.0
+model-index:
+- name: opus-mt-tc-big-zlw-en
+  results:
+  - task:
+      name: Translation ces-eng
+      type: translation
+      args: ces-eng
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: ces eng devtest
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 41.2
+  - task:
+      name: Translation pol-eng
+      type: translation
+      args: pol-eng
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: pol eng devtest
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 29.6
+  - task:
+      name: Translation slk-eng
+      type: translation
+      args: slk-eng
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: slk eng devtest
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 40.0
+  - task:
+      name: Translation ces-eng
+      type: translation
+      args: ces-eng
+    dataset:
+      name: multi30k_test_2016_flickr
+      type: multi30k-2016_flickr
+      args: ces-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 37.6
+  - task:
+      name: Translation ces-eng
+      type: translation
+      args: ces-eng
+    dataset:
+      name: multi30k_test_2018_flickr
+      type: multi30k-2018_flickr
+      args: ces-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 37.4
+  - task:
+      name: Translation ces-eng
+      type: translation
+      args: ces-eng
+    dataset:
+      name: news-test2008
+      type: news-test2008
+      args: ces-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 26.3
+  - task:
+      name: Translation pol-eng
+      type: translation
+      args: pol-eng
+    dataset:
+      name: newsdev2020
+      type: newsdev2020
+      args: pol-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 32.7
+  - task:
+      name: Translation ces-eng
+      type: translation
+      args: ces-eng
+    dataset:
+      name: tatoeba-test-v2021-08-07
+      type: tatoeba_mt
+      args: ces-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 57.4
+  - task:
+      name: Translation pol-eng
+      type: translation
+      args: pol-eng
+    dataset:
+      name: tatoeba-test-v2021-08-07
+      type: tatoeba_mt
+      args: pol-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 55.7
+  - task:
+      name: Translation ces-eng
+      type: translation
+      args: ces-eng
+    dataset:
+      name: newstest2009
+      type: wmt-2009-news
+      args: ces-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 29.5
+  - task:
+      name: Translation ces-eng
+      type: translation
+      args: ces-eng
+    dataset:
+      name: newstest2010
+      type: wmt-2010-news
+      args: ces-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 30.7
+  - task:
+      name: Translation ces-eng
+      type: translation
+      args: ces-eng
+    dataset:
+      name: newstest2011
+      type: wmt-2011-news
+      args: ces-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 30.9
+  - task:
+      name: Translation ces-eng
+      type: translation
+      args: ces-eng
+    dataset:
+      name: newstest2012
+      type: wmt-2012-news
+      args: ces-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 29.4
+  - task:
+      name: Translation ces-eng
+      type: translation
+      args: ces-eng
+    dataset:
+      name: newstest2013
+      type: wmt-2013-news
+      args: ces-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 32.8
+  - task:
+      name: Translation ces-eng
+      type: translation
+      args: ces-eng
+    dataset:
+      name: newstest2014
+      type: wmt-2014-news
+      args: ces-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 38.7
+  - task:
+      name: Translation ces-eng
+      type: translation
+      args: ces-eng
+    dataset:
+      name: newstest2015
+      type: wmt-2015-news
+      args: ces-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 33.4
+  - task:
+      name: Translation ces-eng
+      type: translation
+      args: ces-eng
+    dataset:
+      name: newstest2016
+      type: wmt-2016-news
+      args: ces-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 37.1
+  - task:
+      name: Translation ces-eng
+      type: translation
+      args: ces-eng
+    dataset:
+      name: newstest2017
+      type: wmt-2017-news
+      args: ces-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 32.5
+  - task:
+      name: Translation ces-eng
+      type: translation
+      args: ces-eng
+    dataset:
+      name: newstest2018
+      type: wmt-2018-news
+      args: ces-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 33.1
+  - task:
+      name: Translation pol-eng
+      type: translation
+      args: pol-eng
+    dataset:
+      name: newstest2020
+      type: wmt-2020-news
+      args: pol-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 32.6
+---
+# opus-mt-tc-big-zlw-en
+
+Neural machine translation model for translating from West Slavic languages (zlw) 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): ces dsb hsb pol
+* 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/zlw-eng/opusTCv20210807+bt_transformer-big_2022-03-17.zip)
+* more information released models: [OPUS-MT zlw-eng README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/zlw-eng/README.md)
+
+## Usage
+
+A short example code:
+
+```python
+from transformers import MarianMTModel, MarianTokenizer
+
+src_text = [
+    "Aoi'ego hobby to tańczenie.",
+    "Myślisz, że Tom planuje to zrobić?"
+]
+
+model_name = "pytorch-models/opus-mt-tc-big-zlw-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:
+#     Aoi's hobby is dancing.
+#     You think Tom's planning on doing that?
+```
+
+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-en")
+print(pipe("Aoi'ego hobby to tańczenie."))
+
+# expected output: Aoi's hobby is dancing.
+```
+
+## Benchmarks
+
+* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/zlw-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/zlw-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 |
+|----------|---------|-------|-------|-------|--------|
+| ces-eng | tatoeba-test-v2021-08-07 | 0.71861 | 57.4 | 13824 | 105010 |
+| pol-eng | tatoeba-test-v2021-08-07 | 0.70544 | 55.7 | 10099 | 75766 |
+| ces-eng | flores101-devtest | 0.66444 | 41.2 | 1012 | 24721 |
+| pol-eng | flores101-devtest | 0.58301 | 29.6 | 1012 | 24721 |
+| slk-eng | flores101-devtest | 0.66103 | 40.0 | 1012 | 24721 |
+| ces-eng | multi30k_test_2016_flickr | 0.61482 | 37.6 | 1000 | 12955 |
+| ces-eng | multi30k_test_2018_flickr | 0.61405 | 37.4 | 1071 | 14689 |
+| pol-eng | newsdev2020 | 0.60478 | 32.7 | 2000 | 46654 |
+| ces-eng | newssyscomb2009 | 0.56495 | 30.2 | 502 | 11818 |
+| ces-eng | news-test2008 | 0.54300 | 26.3 | 2051 | 49380 |
+| ces-eng | newstest2009 | 0.56309 | 29.5 | 2525 | 65399 |
+| ces-eng | newstest2010 | 0.57778 | 30.7 | 2489 | 61711 |
+| ces-eng | newstest2011 | 0.57336 | 30.9 | 3003 | 74681 |
+| ces-eng | newstest2012 | 0.56761 | 29.4 | 3003 | 72812 |
+| ces-eng | newstest2013 | 0.58809 | 32.8 | 3000 | 64505 |
+| ces-eng | newstest2014 | 0.64401 | 38.7 | 3003 | 68065 |
+| ces-eng | newstest2015 | 0.58607 | 33.4 | 2656 | 53569 |
+| ces-eng | newstest2016 | 0.61780 | 37.1 | 2999 | 64670 |
+| ces-eng | newstest2017 | 0.58259 | 32.5 | 3005 | 61721 |
+| ces-eng | newstest2018 | 0.58677 | 33.1 | 2983 | 63495 |
+| pol-eng | newstest2020 | 0.60047 | 32.6 | 1001 | 21755 |
+
+## 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 20:19:48 EEST 2022
+* port machine: LM0-400-22516.local

benchmark_results.txt

@@ -0,0 +1,28 @@
+ces-eng	flores101-dev	0.65704	39.7	997	23555
+pol-eng	flores101-dev	0.58424	29.8	997	23555
+slk-eng	flores101-dev	0.66379	40.1	997	23555
+ces-eng	flores101-devtest	0.66444	41.2	1012	24721
+pol-eng	flores101-devtest	0.58301	29.6	1012	24721
+slk-eng	flores101-devtest	0.66103	40.0	1012	24721
+ces-eng	multi30k_test_2016_flickr	0.61482	37.6	1000	12955
+ces-eng	multi30k_test_2018_flickr	0.61405	37.4	1071	14689
+pol-eng	newsdev2020	0.60478	32.7	2000	46654
+ces-eng	newssyscomb2009	0.56495	30.2	502	11818
+ces-eng	news-test2008	0.54300	26.3	2051	49380
+ces-eng	newstest2009	0.56309	29.5	2525	65399
+ces-eng	newstest2010	0.57778	30.7	2489	61711
+ces-eng	newstest2011	0.57336	30.9	3003	74681
+ces-eng	newstest2012	0.56761	29.4	3003	72812
+ces-eng	newstest2013	0.58809	32.8	3000	64505
+ces-eng	newstest2014	0.64401	38.7	3003	68065
+ces-eng	newstest2015	0.58607	33.4	2656	53569
+ces-eng	newstest2016	0.61780	37.1	2999	64670
+ces-eng	newstest2017	0.58259	32.5	3005	61721
+ces-eng	newstest2018	0.58677	33.1	2983	63495
+pol-eng	newstest2020	0.60047	32.6	1001	21755
+ces-eng	tatoeba-test-v2020-07-28	0.72722	58.5	10000	75376
+pol-eng	tatoeba-test-v2020-07-28	0.70515	55.7	10000	75002
+ces-eng	tatoeba-test-v2021-03-30	0.72179	57.7	12076	91333
+pol-eng	tatoeba-test-v2021-03-30	0.70528	55.7	10056	75479
+ces-eng	tatoeba-test-v2021-08-07	0.71861	57.4	13824	105010
+pol-eng	tatoeba-test-v2021-08-07	0.70544	55.7	10099	75766

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+  "static_position_embeddings": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.18.0.dev0",
+  "use_cache": true,
+  "vocab_size": 58102
+}

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