Update README.md

README.md

@@ -1,3 +1,112 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- fr
+library_name: transformers
+tags:
+- NMT
+- commonvoice
+- pytorch
+- pictograms
+- translation
+metrics:
+- bleu
+---
+
+# t2p-nmt-commonvoice
+
+*t2p-nmt-commonvoice* is a text-to-pictograms translation model built by training from scratch the [NMT](https://github.com/facebookresearch/fairseq/blob/main/examples/translation/README.md) 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
+
+### 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
+
+A full list of the parameters is available in the config.json file. This is the arguments in the training pipeline :
+
+```bash
+fairseq-train \
+    data-bin/commonvoice.tokenized.fr-frp \
+    --arch transformer_iwslt_de_en --share-decoder-input-output-embed \
+    --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \
+    --lr 5e-4 --lr-scheduler inverse_sqrt --warmup-updates 4000 \
+    --dropout 0.3 --weight-decay 0.0001 \
+    --save-dir exp_commonvoice/checkpoints/nmt_fr_frp_commonvoice \
+    --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \
+    --max-tokens 4096 \
+    --eval-bleu \
+    --eval-bleu-args '{"beam": 5, "max_len_a": 1.2, "max_len_b": 10}' \
+    --eval-bleu-detok moses \
+    --eval-bleu-remove-bpe \
+    --eval-bleu-print-samples \
+    --best-checkpoint-metric bleu --maximize-best-checkpoint-metric \
+    --max-epoch 40 \
+    --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.
+
+### 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
+
+Fine-tuning was performed using a single Nvidia V100 GPU with 32 GB of memory which took around 2 hours in total.
+
+## Using t2p-nmt-commonvoice model
+
+The scripts to use the *t2p-nmt-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}
+}
+```