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README.md

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+---
+language:
+- en
+- es
+
+tags:
+- translation
+
+license: cc-by-4.0
+model-index:
+- name: opus-mt-tc-big-en-es
+  results:
+  - task:
+      name: Translation eng-spa
+      type: translation
+      args: eng-spa
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: eng spa devtest
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 28.5
+  - task:
+      name: Translation eng-spa
+      type: translation
+      args: eng-spa
+    dataset:
+      name: news-test2008
+      type: news-test2008
+      args: eng-spa
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 30.1
+  - task:
+      name: Translation eng-spa
+      type: translation
+      args: eng-spa
+    dataset:
+      name: tatoeba-test-v2021-08-07
+      type: tatoeba_mt
+      args: eng-spa
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 57.2
+  - task:
+      name: Translation eng-spa
+      type: translation
+      args: eng-spa
+    dataset:
+      name: tico19-test
+      type: tico19-test
+      args: eng-spa
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 53.0
+  - task:
+      name: Translation eng-spa
+      type: translation
+      args: eng-spa
+    dataset:
+      name: newstest2009
+      type: wmt-2009-news
+      args: eng-spa
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 30.2
+  - task:
+      name: Translation eng-spa
+      type: translation
+      args: eng-spa
+    dataset:
+      name: newstest2010
+      type: wmt-2010-news
+      args: eng-spa
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 37.6
+  - task:
+      name: Translation eng-spa
+      type: translation
+      args: eng-spa
+    dataset:
+      name: newstest2011
+      type: wmt-2011-news
+      args: eng-spa
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 38.9
+  - task:
+      name: Translation eng-spa
+      type: translation
+      args: eng-spa
+    dataset:
+      name: newstest2012
+      type: wmt-2012-news
+      args: eng-spa
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 39.5
+  - task:
+      name: Translation eng-spa
+      type: translation
+      args: eng-spa
+    dataset:
+      name: newstest2013
+      type: wmt-2013-news
+      args: eng-spa
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 35.9
+---
+# opus-mt-tc-big-en-es
+
+Neural machine translation model for translating from English (en) to Spanish (es).
+
+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: 2022-03-13
+* source language(s): eng
+* target language(s): spa
+* model: transformer-big
+* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
+* tokenization: SentencePiece (spm32k,spm32k)
+* original model: [opusTCv20210807+bt_transformer-big_2022-03-13.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-spa/opusTCv20210807+bt_transformer-big_2022-03-13.zip)
+* more information released models: [OPUS-MT eng-spa README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-spa/README.md)
+
+## Usage
+
+A short example code:
+
+```python
+from transformers import MarianMTModel, MarianTokenizer
+
+src_text = [
+    "A wasp stung him and he had an allergic reaction.",
+    "I love nature."
+]
+
+model_name = "pytorch-models/opus-mt-tc-big-en-es"
+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:
+#     Una avispa lo picó y tuvo una reacción alérgica.
+#     Me encanta la naturaleza.
+```
+
+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-en-es")
+print(pipe("A wasp stung him and he had an allergic reaction."))
+
+# expected output: Una avispa lo picó y tuvo una reacción alérgica.
+```
+
+## Benchmarks
+
+* test set translations: [opusTCv20210807+bt_transformer-big_2022-03-13.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-spa/opusTCv20210807+bt_transformer-big_2022-03-13.test.txt)
+* test set scores: [opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-spa/opusTCv20210807+bt_transformer-big_2022-03-13.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 |
+|----------|---------|-------|-------|-------|--------|
+| eng-spa | tatoeba-test-v2021-08-07 | 0.73863 | 57.2 | 16583 | 134710 |
+| eng-spa | flores101-devtest | 0.56440 | 28.5 | 1012 | 29199 |
+| eng-spa | newssyscomb2009 | 0.58415 | 31.5 | 502 | 12503 |
+| eng-spa | news-test2008 | 0.56707 | 30.1 | 2051 | 52586 |
+| eng-spa | newstest2009 | 0.57836 | 30.2 | 2525 | 68111 |
+| eng-spa | newstest2010 | 0.62357 | 37.6 | 2489 | 65480 |
+| eng-spa | newstest2011 | 0.62415 | 38.9 | 3003 | 79476 |
+| eng-spa | newstest2012 | 0.63031 | 39.5 | 3003 | 79006 |
+| eng-spa | newstest2013 | 0.60354 | 35.9 | 3000 | 70528 |
+| eng-spa | tico19-test | 0.73554 | 53.0 | 2100 | 66563 |
+
+## 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 18:03:53 EEST 2022
+* port machine: LM0-400-22516.local

benchmark_results.txt

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+eng-spa	flores101-dev	0.56008	28.0	997	27793
+eng-spa	flores101-devtest	0.56440	28.5	1012	29199
+eng-spa	newssyscomb2009	0.58415	31.5	502	12503
+eng-spa	news-test2008	0.56707	30.1	2051	52586
+eng-spa	newstest2009	0.57836	30.2	2525	68111
+eng-spa	newstest2010	0.62357	37.6	2489	65480
+eng-spa	newstest2011	0.62415	38.9	3003	79476
+eng-spa	newstest2012	0.63031	39.5	3003	79006
+eng-spa	newstest2013	0.60354	35.9	3000	70528
+eng-spa	tatoeba-test-v2020-07-28	0.72124	55.0	10000	77311
+eng-spa	tatoeba-test-v2021-03-30	0.72663	55.8	11940	93423
+eng-spa	tatoeba-test-v2021-08-07	0.73863	57.2	16583	134710
+eng-spa	tico19-test	0.73554	53.0	2100	66563

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config.json

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+  "static_position_embeddings": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.18.0.dev0",
+  "use_cache": true,
+  "vocab_size": 55027
+}

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tokenizer_config.json

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