README.md

---
language:
- fr
- en
metrics:
- bleu
pipeline_tag: translation
model-index:
- name: NMT-EN-FR
  results:
  - task:
      type: translation
    dataset:
      name: UN Corpus
      type: bilingual
    metrics:
    - name: BLEU
      type: BLEU
      value: 49
library_name: ctranslate2
license: cc-by-sa-4.0
---

# Model Details

French-to-English Machine Translation model trained by Yasmin Moslem.
This model depends on the Transformer (base) architecture.
The model was originally trained with OpenNMT-py and then converted to the CTranslate2 format for efficient inference.

## Tools

- OpenNMT-py
- CTranslate2

## Data

This model is trained on the French-to-English portion of the [UN Corpus](https://conferences.unite.un.org/UNCorpus/),
consisting of approx. 20 million segments.

## Tokenizer

The tokenizer was trained using [SentencePiece](https://github.com/google/sentencepiece) on shared vocabulary.
Hence, there is only one SentencePiece model that can be used for tokenizing both the source and target texts.

## Demo

A demo of this model is available at: https://www.machinetranslation.io/

The demo also illustrates word-level auto-suggestions with teacher forcing.


## Inference

If you want to run this model locally, you can use the [CTranslate2](https://github.com/OpenNMT/CTranslate2) library.

## Citation

```
@inproceedings{moslem-etal-2022-translation,
    title = "Translation Word-Level Auto-Completion: What Can We Achieve Out of the Box?",
    author = "Moslem, Yasmin  and
      Haque, Rejwanul  and
      Way, Andy",
    booktitle = "Proceedings of the Seventh Conference on Machine Translation (WMT)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.wmt-1.119",
    pages = "1176--1181",
}
```