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README.md

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+---
+language:
+- ces+slk
+- cs
+- uk
+
+tags:
+- translation
+
+license: cc-by-4.0
+model-index:
+- name: opus-mt-tc-base-uk-ces_slk
+  results:
+  - task:
+      name: Translation ukr-ces
+      type: translation
+      args: ukr-ces
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: ukr ces devtest
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 23.0
+  - task:
+      name: Translation ukr-slk
+      type: translation
+      args: ukr-slk
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: ukr slk devtest
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 22.1
+  - task:
+      name: Translation ukr-ces
+      type: translation
+      args: ukr-ces
+    dataset:
+      name: tatoeba-test-v2021-08-07
+      type: tatoeba_mt
+      args: ukr-ces
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 54.2
+---
+# opus-mt-tc-base-uk-ces_slk
+
+Neural machine translation model for translating from Ukrainian (uk) to unknown (ces+slk).
+
+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: align_2022-03-17
+* source language(s): ukr
+* target language(s): ces
+* valid target language labels: >>ces<< >>slk<<
+* model: transformer-align (base)
+* data: opusTCv20210807+pft ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
+* tokenization: SentencePiece (spm32k,spm32k)
+* original model: [opusTCv20210807+pft_transformer-align_2022-03-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-ces+slk/opusTCv20210807+pft_transformer-align_2022-03-17.zip)
+* more information released models: [OPUS-MT ukr-ces+slk README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-ces+slk/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. `>>ces<<`
+
+## Usage
+
+A short example code:
+
+```python
+from transformers import MarianMTModel, MarianTokenizer
+
+src_text = [
+    ">>ces<< А чого так?",
+    ">>ces<< Я загубив свої окуляри."
+]
+
+model_name = "pytorch-models/opus-mt-tc-base-uk-ces_slk"
+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:
+#     Proč to tak je?
+#     Ztratil jsem brýle.
+```
+
+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-base-uk-ces_slk")
+print(pipe(">>ces<< А чого так?"))
+
+# expected output: Proč to tak je?
+```
+
+## Benchmarks
+
+* test set translations: [opusTCv20210807+pft_transformer-align_2022-03-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-ces+slk/opusTCv20210807+pft_transformer-align_2022-03-17.test.txt)
+* test set scores: [opusTCv20210807+pft_transformer-align_2022-03-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-ces+slk/opusTCv20210807+pft_transformer-align_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 |
+|----------|---------|-------|-------|-------|--------|
+| ukr-ces | tatoeba-test-v2021-08-07 | 0.70661 | 54.2 | 1787 | 8550 |
+| ukr-ces | flores101-devtest | 0.51283 | 23.0 | 1012 | 22101 |
+| ukr-slk | flores101-devtest | 0.51043 | 22.1 | 1012 | 22543 |
+
+## 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: Wed Mar 23 20:10:11 EET 2022
+* port machine: LM0-400-22516.local

benchmark_results.txt

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+ukr-ces	flores101-devtest	0.51283	23.0	1012	22101
+ukr-slk	flores101-devtest	0.51043	22.1	1012	22543
+ukr-ces	flores101-dev	0.50743	22.3	997	21183
+ukr-slk	flores101-dev	0.50624	22.0	997	21796
+ukr-ces	tatoeba-test-v2020-07-28	0.70661	54.2	1787	8550
+ukr-ces	tatoeba-test-v2021-03-30	0.70661	54.2	1787	8550
+ukr-ces	tatoeba-test-v2021-08-07	0.70661	54.2	1787	8550

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config.json

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+  "init_std": 0.02,
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+  "max_length": 512,
+  "max_position_embeddings": 512,
+  "model_type": "marian",
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+  "num_beams": 4,
+  "num_hidden_layers": 6,
+  "pad_token_id": 60825,
+  "scale_embedding": true,
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+  "static_position_embeddings": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.18.0.dev0",
+  "use_cache": true,
+  "vocab_size": 60826
+}

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special_tokens_map.json

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tokenizer_config.json

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