license: apache-2.0
language:
- fr
library_name: transformers
tags:
- t5
- orfeo
- pytorch
- pictograms
- translation
metrics:
- bleu
inference: false
t2p-t5-large-orféo
t2p-t5-large-orféo is a text-to-pictograms translation model built by fine-tuning the t5-large model on a dataset of pairs of transcriptions / pictogram token sequence (each token is linked to a pictogram image from ARASAAC). The model is used only for inference.
Training details
Datasets
The Propicto-orféo dataset 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" 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
A full list of the parameters is available in the config.json file. This is the arguments in the training pipeline :
training_args = Seq2SeqTrainingArguments(
output_dir="checkpoints_orfeo/",
evaluation_strategy="epoch",
save_strategy="epoch",
learning_rate=2e-5,
per_device_train_batch_size=32,
per_device_eval_batch_size=32,
weight_decay=0.01,
save_total_limit=3,
num_train_epochs=40,
predict_with_generate=True,
fp16=True,
load_best_model_at_end=True
)
Evaluation
The model was evaluated with sacreBLEU, where we compared the reference pictogram translation with the model hypothesis.
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 16 hours in total.
Using t2p-t5-large-orféo model with HuggingFace transformers
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
source_lang = "fr"
target_lang = "frp"
max_input_length = 128
max_target_length = 128
tokenizer = AutoTokenizer.from_pretrained("Propicto/t2p-t5-large-orfeo")
model = AutoModelForSeq2SeqLM.from_pretrained("Propicto/t2p-t5-large-orfeo")
inputs = tokenizer("Je mange une pomme", return_tensors="pt").input_ids
outputs = model.generate(inputs.to("cuda:0"), max_new_tokens=40, do_sample=True, top_k=30, top_p=0.95)
pred = tokenizer.decode(outputs[0], skip_special_tokens=True)
Linking the predicted sequence of tokens to the corresponding ARASAAC pictograms
import pandas as pd
def process_output_trad(pred):
return pred.split()
def read_lexicon(lexicon):
df = pd.read_csv(lexicon, sep='\t')
df['keyword_no_cat'] = df['lemma'].str.split(' #').str[0].str.strip().str.replace(' ', '_')
return df
def get_id_picto_from_predicted_lemma(df_lexicon, lemma):
id_picto = df_lexicon.loc[df_lexicon['keyword_no_cat'] == lemma, 'id_picto'].tolist()
return (id_picto[0], lemma) if id_picto else (0, lemma)
lexicon = read_lexicon("lexicon.csv")
sentence_to_map = process_output_trad(pred)
pictogram_ids = [get_id_picto_from_predicted_lemma(lexicon, lemma) for lemma in sentence_to_map]
Viewing the predicted sequence of ARASAAC pictograms in a HTML file
def generate_html(ids):
html_content = '<html><body>'
for picto_id, lemma in ids:
if picto_id != 0: # ignore invalid IDs
img_url = f"https://static.arasaac.org/pictograms/{picto_id}/{picto_id}_500.png"
html_content += f'''
<figure style="display:inline-block; margin:1px;">
<img src="{img_url}" alt="{lemma}" width="200" height="200" />
<figcaption>{lemma}</figcaption>
</figure>
'''
html_content += '</body></html>'
return html_content
html = generate_html(pictogram_ids)
with open("pictograms.html", "w") as file:
file.write(html)
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:
@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}
}