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README.md

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+---
+language:
+- ar
+- gmq
+
+tags:
+- translation
+- opus-mt-tc
+
+license: cc-by-4.0
+model-index:
+- name: opus-mt-tc-big-gmq-ar
+  results:
+  - task:
+      name: Translation dan-ara
+      type: translation
+      args: dan-ara
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: dan ara devtest
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 19.9
+       - name: chr-F
+         type: chrf
+         value: 0.52841
+  - task:
+      name: Translation nob-ara
+      type: translation
+      args: nob-ara
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: nob ara devtest
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 16.8
+       - name: chr-F
+         type: chrf
+         value: 0.49670
+  - task:
+      name: Translation swe-ara
+      type: translation
+      args: swe-ara
+    dataset:
+      name: flores101-devtest
+      type: flores_101
+      args: swe ara devtest
+    metrics:
+       - name: BLEU
+         type: bleu
+         value: 19.3
+       - name: chr-F
+         type: chrf
+         value: 0.51882
+---
+# opus-mt-tc-big-gmq-ar
+
+## Table of Contents
+- [Model Details](#model-details)
+- [Uses](#uses)
+- [Risks, Limitations and Biases](#risks-limitations-and-biases)
+- [How to Get Started With the Model](#how-to-get-started-with-the-model)
+- [Training](#training)
+- [Evaluation](#evaluation)
+- [Citation Information](#citation-information)
+- [Acknowledgements](#acknowledgements)
+
+## Model Details
+
+Neural machine translation model for translating from North Germanic languages (gmq) to Arabic (ar).
+
+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).
+**Model Description:**
+- **Developed by:** Language Technology Research Group at the University of Helsinki
+- **Model Type:** Translation (transformer-big)
+- **Release**: 2022-07-27
+- **License:** CC-BY-4.0
+- **Language(s):**  
+  - Source Language(s): 
+  - Target Language(s): 
+  - Valid Target Language Labels: 
+- **Original Model**: [opusTCv20210807_transformer-big_2022-07-27.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-ara/opusTCv20210807_transformer-big_2022-07-27.zip)
+- **Resources for more information:**
+  - [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
+  - More information about released models for this language pair: [OPUS-MT gmq-ara README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gmq-ara/README.md)
+  - [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian)
+  - [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/
+
+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. `>><<`
+
+## Uses
+
+This model can be used for translation and text-to-text generation.
+
+## Risks, Limitations and Biases
+
+**CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.**
+
+Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
+
+## How to Get Started With the Model
+
+A short example code:
+
+```python
+from transformers import MarianMTModel, MarianTokenizer
+
+src_text = [
+    ">>ara<< Jeg elsker semitiske sprog.",
+    ">>ara<< Vad handlar boken om?"
+]
+
+model_name = "pytorch-models/opus-mt-tc-big-gmq-ar"
+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:
+#     أحبّ اللغات الساميّة.
+#     عن ماذا يتحدث الكتاب؟
+```
+
+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-gmq-ar")
+print(pipe(">>ara<< Jeg elsker semitiske sprog."))
+
+# expected output: أحبّ اللغات الساميّة.
+```
+
+## Training
+
+- **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
+- **Pre-processing**: SentencePiece (spm32k,spm32k)
+- **Model Type:**  transformer-big
+- **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-07-27.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-ara/opusTCv20210807_transformer-big_2022-07-27.zip)
+- **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
+
+## Evaluation
+
+* test set translations: [opusTCv20210807_transformer-big_2022-07-27.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-ara/opusTCv20210807_transformer-big_2022-07-27.test.txt)
+* test set scores: [opusTCv20210807_transformer-big_2022-07-27.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-ara/opusTCv20210807_transformer-big_2022-07-27.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 |
+|----------|---------|-------|-------|-------|--------|
+| dan-ara | flores101-devtest | 0.52841 | 19.9 | 1012 | 21357 |
+| nob-ara | flores101-devtest | 0.49670 | 16.8 | 1012 | 21357 |
+| swe-ara | flores101-devtest | 0.51882 | 19.3 | 1012 | 21357 |
+
+## Citation Information
+
+* 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",
+}
+```
+
+## 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: 8b9f0b0
+* port time: Fri Aug 12 17:35:27 EEST 2022
+* port machine: LM0-400-22516.local

benchmark_results.txt

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+dan-ara	flores101-dev	0.52400	20.3	997	20512
+nob-ara	flores101-dev	0.49420	17.1	997	20512
+swe-ara	flores101-dev	0.51750	20.2	997	20512
+dan-ara	flores101-devtest	0.52841	19.9	1012	21357
+nob-ara	flores101-devtest	0.49670	16.8	1012	21357
+swe-ara	flores101-devtest	0.51882	19.3	1012	21357

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+  "init_std": 0.02,
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+  "max_length": 512,
+  "max_position_embeddings": 1024,
+  "model_type": "marian",
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+  "num_beams": 4,
+  "num_hidden_layers": 6,
+  "pad_token_id": 60536,
+  "scale_embedding": true,
+  "share_encoder_decoder_embeddings": true,
+  "static_position_embeddings": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.18.0.dev0",
+  "use_cache": true,
+  "vocab_size": 60537
+}

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tokenizer_config.json

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