t2p-nllb-200-distilled-600M-orfeo

t2p-nllb-200-distilled-600M-orfeo

t2p-nllb-200-distilled-600M-orfeo is a text-to-pictograms translation model built by fine-tuning the nllb-200-distilled-600M 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.

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 :

Environmental Impact

Fine-tuning was performed using a single Nvidia V100 GPU with 32 GB of memory which took 30 hours in total.

Using t2p-nllb-200-distilled-600M-orfeo 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-nllb-200-distilled-600M-orfeo")
model = AutoModelForSeq2SeqLM.from_pretrained("Propicto/t2p-nllb-200-distilled-600M-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}
}