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update README
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README.md
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---
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language:
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- ga
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- en
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tags:
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- translation
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license: cc-by-4.0
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---
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This repository contains the translation model for
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### Model Info
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* Source language:
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* Target language:
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*
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* Model architecture: Transformer-base
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* Tokenizer: SentencePiece (Unigram)
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* Cleaning: We used OpusCleaner with a set of basic rules. Details can be found in the filter files in [Github](https://github.com/hplt-project/HPLT-MT-Models/tree/main/v1.0/data/en
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You can also read our deliverable report [here](https://hplt-project.org/HPLT_D5_1___Translation_models_for_select_language_pairs.pdf) for more details.
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### Usage
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The model has been trained with Marian. To run inference, refer to the [Inference/Decoding/Translation](https://github.com/hplt-project/HPLT-MT-Models/tree/main/v1.0#inferencedecodingtranslation) section of our GitHub repository.
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The model can be used with the Hugging Face framework if the weights are converted to the Hugging Face format. We might provide this in the future; contributions are also welcome.
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| testset | BLEU | chrF++ | COMET22 |
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| -------------------------------------- | ---- | ----- | ----- |
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| flores200 | 29.
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| ntrex |
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### Acknowledgements
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---
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language:
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- en
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- ar
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tags:
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- translation
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license: cc-by-4.0
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---
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## HPLT MT release v1.0
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This repository contains the translation model for en-ar trained with OPUS and HPLT data. For usage instructions, evaluation scripts, and inference scripts, please refer to the [HPLT-MT-Models v1.0](https://github.com/hplt-project/HPLT-MT-Models/tree/main/v1.0) GitHub repository.
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### Model Info
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* Source language: English
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* Target language: Arabic
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* Dataset: OPUS and HPLT data
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* Model architecture: Transformer-base
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* Tokenizer: SentencePiece (Unigram)
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* Cleaning: We used OpusCleaner with a set of basic rules. Details can be found in the filter files in [Github](https://github.com/hplt-project/HPLT-MT-Models/tree/main/v1.0/data/ar-en/raw/v2)
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You can also read our deliverable report [here](https://hplt-project.org/HPLT_D5_1___Translation_models_for_select_language_pairs.pdf) for more details.
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### Usage
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The model has been trained with Marian. To run inference, refer to the [Inference/Decoding/Translation](https://github.com/hplt-project/HPLT-MT-Models/tree/main/v1.0#inferencedecodingtranslation) section of our GitHub repository.
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The model can be used with the Hugging Face framework if the weights are converted to the Hugging Face format. We might provide this in the future; contributions are also welcome.
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### Benchmarks
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| testset | BLEU | chrF++ | COMET22 |
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| -------------------------------------- | ---- | ----- | ----- |
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| flores200 | 29.2 | 56.9 | 0.8542 |
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| ntrex | 25.8 | 52.2 | 0.8245 |
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### Acknowledgements
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