Initial commit

README.md

@@ -146,15 +146,11 @@ This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus
 * Release: 2021-12-08
 * source language(s): fin
 * 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-2021-12-08.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-eng/opusTCv20210807+bt-2021-12-08.zip)
 * more information released models: [OPUS-MT fin-eng README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fin-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
 
@@ -164,8 +160,8 @@ A short example code:
 from transformers import MarianMTModel, MarianTokenizer
 
 src_text = [
-    ">>eng<< Replace this with text in an accepted source language.",
-    ">>eng<< This is the second sentence."
+    "Kolme kolmanteen on kaksikymmentäseitsemän.",
+    "Heille syntyi poikavauva."
 ]
 
 model_name = "pytorch-models/opus-mt-tc-big-fi-en"
@@ -182,7 +178,7 @@ 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-fi-en")
-print(pipe(">>eng<< Replace this with text in an accepted source language."))
+print(pipe("Kolme kolmanteen on kaksikymmentäseitsemän."))
 ```
 
 ## Benchmarks
@@ -213,5 +209,5 @@ The work is supported by the [European Language Grid](https://www.european-langu
 
 * transformers version: 4.16.2
 * OPUS-MT git hash: f084bad
-* port time: Tue Mar 22 14:44:34 EET 2022
+* port time: Tue Mar 22 14:52:19 EET 2022
 * port machine: LM0-400-22516.local

README.md~

@@ -0,0 +1,213 @@
+---
+language:
+- en
+- fi
+
+tags:
+- translation
+
+license: cc-by-4.0
+model-index:
+- name: opus-mt-tc-big-fi-en
+  results:
+  - task:
+      name: Translation fin-eng
+      type: translation
+      args: fin-eng
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: fin eng devtest
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 35.4
+  - task:
+      name: Translation fin-eng
+      type: translation
+      args: fin-eng
+    dataset:
+      name: newsdev2015
+      type: newsdev2015
+      args: fin-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 28.6
+  - task:
+      name: Translation fin-eng
+      type: translation
+      args: fin-eng
+    dataset:
+      name: tatoeba-test-v2021-08-07
+      type: tatoeba_mt
+      args: fin-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 57.4
+  - task:
+      name: Translation fin-eng
+      type: translation
+      args: fin-eng
+    dataset:
+      name: newstest2015
+      type: wmt-2015-news
+      args: fin-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 29.9
+  - task:
+      name: Translation fin-eng
+      type: translation
+      args: fin-eng
+    dataset:
+      name: newstest2016
+      type: wmt-2016-news
+      args: fin-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 34.3
+  - task:
+      name: Translation fin-eng
+      type: translation
+      args: fin-eng
+    dataset:
+      name: newstest2017
+      type: wmt-2017-news
+      args: fin-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 37.3
+  - task:
+      name: Translation fin-eng
+      type: translation
+      args: fin-eng
+    dataset:
+      name: newstest2018
+      type: wmt-2018-news
+      args: fin-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 27.1
+  - task:
+      name: Translation fin-eng
+      type: translation
+      args: fin-eng
+    dataset:
+      name: newstest2019
+      type: wmt-2019-news
+      args: fin-eng
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 32.7
+---
+# opus-mt-tc-big-fi-en
+
+Neural machine translation model for translating from Finnish (fi) 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: 2021-12-08
+* source language(s): fin
+* target language(s): eng
+* model: transformer-big (big)
+* data: opusTCv20210807+bt ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
+* tokenization: SentencePiece (spm32k,spm32k)
+* original model: [opusTCv20210807+bt-2021-12-08.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-eng/opusTCv20210807+bt-2021-12-08.zip)
+* more information released models: [OPUS-MT fin-eng README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fin-eng/README.md)
+
+## Usage
+
+A short example code:
+
+```python
+from transformers import MarianMTModel, MarianTokenizer
+
+src_text = [
+    "Replace this with text in an accepted source language.",
+    "This is the second sentence."
+]
+
+model_name = "pytorch-models/opus-mt-tc-big-fi-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) )
+```
+
+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-fi-en")
+print(pipe("Replace this with text in an accepted source language."))
+```
+
+## Benchmarks
+
+* test set translations: [opusTCv20210807+bt-2021-12-08.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-eng/opusTCv20210807+bt-2021-12-08.test.txt)
+* test set scores: [opusTCv20210807+bt-2021-12-08.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-eng/opusTCv20210807+bt-2021-12-08.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 |
+|----------|---------|-------|-------|-------|--------|
+| fin-eng | tatoeba-test-v2021-08-07 | 0.72298 | 57.4 | 10690 | 80552 |
+| fin-eng | flores101-devtest | 0.62521 | 35.4 | 1012 | 24721 |
+| fin-eng | newsdev2015 | 0.56232 | 28.6 | 1500 | 32012 |
+| fin-eng | newstest2015 | 0.57469 | 29.9 | 1370 | 27270 |
+| fin-eng | newstest2016 | 0.60715 | 34.3 | 3000 | 62945 |
+| fin-eng | newstest2017 | 0.63050 | 37.3 | 3002 | 61846 |
+| fin-eng | newstest2018 | 0.54199 | 27.1 | 3000 | 62325 |
+| fin-eng | newstest2019 | 0.59620 | 32.7 | 1996 | 36215 |
+| fin-eng | newstestB2016 | 0.55472 | 27.9 | 3000 | 62945 |
+| fin-eng | newstestB2017 | 0.58847 | 31.1 | 3002 | 61846 |
+
+## 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: Tue Mar 22 14:52:19 EET 2022
+* port machine: LM0-400-22516.local