Update README.md

README.md

@@ -1,3 +1,135 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- fr
+library_name: transformers
+tags:
+- mbart
+- orfeo
+- pytorch
+- pictograms
+- translation
+metrics:
+- bleu
+inference: false
+---
+
+# t2p-mbart-large-cc25-orfeo
+
+*t2p-mbart-large-cc25-orfeo* 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-orféo dataset](https://www.ortolang.fr/market/corpora/propicto) is used, which was created from the CEFC-Orféo corpus. 
+This dataset was 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 | 231,374 |
+| valid | 28,796 |
+| test | 29,009 |
+
+### 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 fr \
+  --ddp-backend legacy_ddp \
+  --max-epoch 40 \
+  --save-dir models/checkpoints/mt_mbart_fr_frp_orfeo \
+  --keep-best-checkpoints 5 \
+  --keep-last-epochs 5
+```
+
+### Evaluation
+
+The model was evaluated with BLEU, where we compared the reference pictogram translation with the model hypothesis.
+
+```bash
+fairseq-generate orfeo_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-27886	ça sera tout madame<unk>
+T-27886	prochain celle-là être tout monsieur
+H-27886	-0.2824968993663788	▁prochain ▁celle - là ▁être ▁tout ▁monsieur
+D-27886	-0.2824968993663788	prochain celle-là être tout monsieur
+P-27886	-0.5773 -0.1780 -0.2587 -0.2361 -0.2726 -0.3167 -0.1312 -0.3103 -0.2615
+Generate test with beam=5: BLEU4 = 75.62, 85.7/78.9/73.9/69.3 (BP=0.986, ratio=0.986, syslen=407923, reflen=413636)
+```
+
+### Results
+
+Comparison to other translation models :
+| **Model** | **validation** | **test** |
+|:-----------:|:-----------------------:|:-----------------------:|
+| t2p-t5-large-orféo | 85.2 | 85.8 |
+| t2p-nmt-orféo | **87.2** | **87.4** | 
+| **t2p-mbart-large-cc25-orfeo** | 75.2 | 75.6 |
+| t2p-nllb-200-distilled-600M-orfeo | 86.3 | 86.9 |
+
+### Environmental Impact
+
+Fine-tuning was performed using a single Nvidia V100 GPU with 32 GB of memory which took 18 hours in total.
+
+## Using t2p-mbart-large-cc25-orfeo model
+
+The scripts to use the *t2p-mbart-large-cc25-orfeo* 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}
+}
+```