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vpelloin/MEDIA_NLU-flaubert_oral_ft

sourceHugging Faceupdated 3y agoView on Hugging Face

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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/MEDIANLU-flaubertoral_ft

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 using `nherve/flaubert-oral-ft` as its inital checkpoint. It obtained 11.98% CER (lower is better) in the MEDIA test set, in our Interspeech 2023 publication, using Kaldi ASR transcriptions.

Available MEDIA NLU models:

—`vpelloin/MEDIA_NLU-flaubert_base_cased`: MEDIA NLU model trained using `flaubert/flaubert_base_cased`. Obtains 13.20% CER on MEDIA test.
—`vpelloin/MEDIA_NLU-flaubert_base_uncased`: MEDIA NLU model trained using `flaubert/flaubert_base_uncased`. Obtains 12.40% CER on MEDIA test.
—`vpelloin/MEDIA_NLU-flaubert_oral_ft`: MEDIA NLU model trained using `nherve/flaubert-oral-ft`. Obtains 11.98% CER on MEDIA test.
—`vpelloin/MEDIA_NLU-flaubert_oral_mixed`: MEDIA NLU model trained using `nherve/flaubert-oral-mixed`. Obtains 12.47% CER on MEDIA test.
—`vpelloin/MEDIA_NLU-flaubert_oral_asr`: MEDIA NLU model trained using `nherve/flaubert-oral-asr`. Obtains 12.43% CER on MEDIA test.
—`vpelloin/MEDIA_NLU-flaubert_oral_asr_nb`: MEDIA NLU model trained using `nherve/flaubert-oral-asr_nb`. Obtains 12.24% CER on MEDIA test.

Usage with Pipeline

python

from transformers import pipeline

generator = pipeline(
    model="vpelloin/MEDIA_NLU-flaubert_oral_ft",
    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

python

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

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')
outputs = model(**inputs).logits
print([
    [model.config.id2label[i] for i in b]
    for b in outputs.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}
}