metadata

language: fr
pipeline_tag: token-classification
widget:
  - text: je voudrais réserver une chambre à paris pour demain et lundi
  - text: d'accord pour l'hôtel à quatre vingt dix euros la nuit
  - text: deux nuits s'il vous plait
  - text: dans un hôtel avec piscine à marseille
tags:
  - bert
  - flaubert
  - natural language understanding
  - NLU
  - spoken language understanding
  - SLU
  - understanding
  - MEDIA

vpelloin/MEDIA_NLU-flaubert_oral_asr

This is a Natural Language Understanding (NLU) model for the French MEDIA benchmark. It maps each input words into outputs concepts tags (76 available).

This model is trained with flaubert-oral-asr as it's inital checkpoint.

Available MEDIA NLU models:

MEDIA_NLU-flaubert_base_cased: model trained with flaubert_base_cased as it's inital checkpoint
MEDIA_NLU-flaubert_base_uncased: model trained with flaubert_base_uncased as it's inital checkpoint
MEDIA_NLU-flaubert_oral_ft: model trained with flaubert-oral-ft as it's inital checkpoint
MEDIA_NLU-flaubert_oral_mixed: model trained with flaubert-oral-mixed as it's inital checkpoint
MEDIA_NLU-flaubert_oral_asr: model trained with flaubert-oral-asr as it's inital checkpoint
MEDIA_NLU-flaubert_oral_asr_nb: model trained with flaubert-oral-asr_nb as it's inital checkpoint

Usage with Pipeline

from transformers import pipeline

generator = pipeline(model="vpelloin/MEDIA_NLU-flaubert_oral_asr", task="token-classification")
sentences = [
    "je voudrais réserver une chambre à paris pour demain et lundi",
    "d'accord pour l'hôtel à quatre vingt dix euros la nuit",
    "deux nuits s'il vous plait",
    "dans un hôtel avec piscine à marseille"
 ]

for sentence in sentences:
    print([(tok['word'], tok['entity']) for tok in generator(sentence)])

Usage with AutoTokenizer/AutoModel

from transformers import (
    AutoTokenizer,
    AutoModelForTokenClassification
)
tokenizer = AutoTokenizer.from_pretrained("vpelloin/MEDIA_NLU-flaubert_oral_asr")
model = AutoModelForTokenClassification.from_pretrained("vpelloin/MEDIA_NLU-flaubert_oral_asr")

sentences = [
    "je voudrais réserver une chambre à paris pour demain et lundi",
    "d'accord pour l'hôtel à quatre vingt dix euros la nuit",
    "deux nuits s'il vous plait",
    "dans un hôtel avec piscine à marseille"
 ]
inputs = tokenizer(sentences, padding=True, return_tensors='pt')
outptus = model(**inputs).logits
print([[model.config.id2label[i] for i in b] for b in outptus.argmax(dim=-1).tolist()])

Reference

If you use this model for your scientific publication, or if you find the resources in this repository useful, please cite the following paper:

@inproceedings{pelloin22_interspeech,
  author={Valentin Pelloin and Franck Dary and Nicolas Hervé and Benoit Favre and Nathalie Camelin and Antoine LAURENT and Laurent Besacier},
  title={ASR-Generated Text for Language Model Pre-training Applied to Speech Tasks},
  year=2022,
  booktitle={Proc. Interspeech 2022},
  pages={3453--3457},
  doi={10.21437/Interspeech.2022-352}
}