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---
license: apache-2.0
language:
- fr
library_name: transformers
tags:
- mbart
- commonvoice
- pytorch
- pictograms
- translation
metrics:
- sacrebleu
inference: false
---
# t2p-mbart-large-cc25-commonvoice
*t2p-mbart-large-cc25-commonvoice* is a text-to-pictograms translation model built by fine-tuning the [mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) model on a dataset of pairs of transcriptions / pictogram token sequence (each token is linked to a pictogram image from [ARASAAC](https://arasaac.org/)).
The model is used only for **inference**.
## Training details
The model was trained with [Fairseq](https://github.com/facebookresearch/fairseq/blob/main/examples/mbart/README.md).
### Datasets
The [Propicto-commonvoice dataset](https://www.ortolang.fr/market/corpora/propicto) is used, which was created from the CommmonVoice v.15.0 corpus.
This dataset was built with the method presented in the research paper titled ["A Multimodal French Corpus of Aligned Speech, Text, and Pictogram Sequences for Speech-to-Pictogram Machine Translation](https://aclanthology.org/2024.lrec-main.76/)" at LREC-Coling 2024. The dataset was split into training, validation, and test sets.
| **Split** | **Number of utterances** |
|:-----------:|:-----------------------:|
| train | 527,390 |
| valid | 16,124 |
| test | 16,120 |
### Parameters
This is the arguments in the training pipeline :
```bash
fairseq-train $DATA \
--encoder-normalize-before --decoder-normalize-before \
--arch mbart_large --layernorm-embedding \
--task translation_from_pretrained_bart \
--source-lang fr --target-lang frp \
--criterion label_smoothed_cross_entropy --label-smoothing 0.2 \
--optimizer adam --adam-eps 1e-06 --adam-betas '(0.9, 0.98)' \
--lr-scheduler polynomial_decay --lr 3e-05 --warmup-updates 2500 --total-num-update 40000 \
--dropout 0.3 --attention-dropout 0.1 --weight-decay 0.0 \
--max-tokens 1024 --update-freq 2 \
--save-interval 1 --save-interval-updates 5000 --keep-interval-updates 5 \
--seed 222 --log-format simple --log-interval 2 \
--langs $langs \
--ddp-backend legacy_ddp \
--max-epoch 40 \
--save-dir models/checkpoints/mt_mbart_fr_frp_commonvoice_langs \
--keep-best-checkpoints 5 \
--keep-last-epochs 5
```
### Evaluation
The model was evaluated with sacreBLEU, where we compared the reference pictogram translation with the model hypothesis.
```bash
fairseq-generate commonvoice_data/data/ \
--path $model_dir/checkpoint_best.pt \
--task translation_from_pretrained_bart \
--gen-subset test \
-t frp -s fr \
--bpe 'sentencepiece' --sentencepiece-model mbart.cc25.v2/sentence.bpe.model \
--sacrebleu \
--batch-size 32 --langs $langs > out.txt
```
The output file prints the following information :
```txt
S-1071 cette collaboration dure trois ans<unk>
T-1071 le collaboration durer 3 année
H-1071 -0.2111533135175705 ▁le ▁collaboration ▁dur er ▁3 ▁année
D-1071 -0.2111533135175705 le collaboration durer 3 année
P-1071 -0.2783 -0.0584 -0.2309 -0.2009 -0.2145 -0.1210 -0.3330 -0.2523
Generate test with beam=5: BLEU4 = 72.31, 84.3/77.4/72.3/67.7 (BP=0.962, ratio=0.963, syslen=227722, reflen=236545)
```
### Results
Comparison to other translation models :
| **Model** | **validation** | **test** |
|:-----------:|:-----------------------:|:-----------------------:|
| t2p-t5-large-commonvoice | 86.3 | 86.5 |
| t2p-nmt-commonvoice | 86.0 | 82.6 |
| **t2p-mbart-large-cc25-commonvoice** | 72.3 | 72.3 |
| t2p-nllb-200-distilled-600M-commonvoice | **87.4** | **87.6** |
### Environmental Impact
Training was performed using a single Nvidia V100 GPU with 32 GB of memory which took around 18 hours in total.
## Using t2p-mbart-large-cc25-commonvoice
The scripts to use the *t2p-mbart-large-cc25-commonvoice* model are located in the [speech-to-pictograms GitHub repository](https://github.com/macairececile/speech-to-pictograms).
## Information
- **Language(s):** French
- **License:** Apache-2.0
- **Developed by:** Cécile Macaire
- **Funded by**
- GENCI-IDRIS (Grant 2023-AD011013625R1)
- PROPICTO ANR-20-CE93-0005
- **Authors**
- Cécile Macaire
- Chloé Dion
- Emmanuelle Esperança-Rodier
- Benjamin Lecouteux
- Didier Schwab
## Citation
If you use this model for your own research work, please cite as follows:
```bibtex
@inproceedings{macaire_jeptaln2024,
title = {{Approches cascade et de bout-en-bout pour la traduction automatique de la parole en pictogrammes}},
author = {Macaire, C{\'e}cile and Dion, Chlo{\'e} and Schwab, Didier and Lecouteux, Benjamin and Esperan{\c c}a-Rodier, Emmanuelle},
url = {https://inria.hal.science/hal-04623007},
booktitle = {{35{\`e}mes Journ{\'e}es d'{\'E}tudes sur la Parole (JEP 2024) 31{\`e}me Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles (TALN 2024) 26{\`e}me Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RECITAL 2024)}},
address = {Toulouse, France},
publisher = {{ATALA \& AFPC}},
volume = {1 : articles longs et prises de position},
pages = {22-35},
year = {2024}
}
```
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