
# vpelloin/MEDIA_NLU-flaubert_base_uncased
This is a Natural Language Understanding (NLU) model for the French [MEDIA benchmark](https://catalogue.elra.info/en-us/repository/browse/ELRA-S0272/).
It maps each input words into outputs concepts tags (76 available).

This model is trained using [`flaubert/flaubert_base_uncased`](https://huggingface.co/flaubert/flaubert_base_uncased) as its inital checkpoint. It obtained 12.40% CER (*lower is better*) in the MEDIA test set, in [our Interspeech 2023 publication](http://doi.org/10.21437/Interspeech.2022-352), using Kaldi ASR transcriptions.

## Available MEDIA NLU models:
- [`vpelloin/MEDIA_NLU-flaubert_base_cased`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_base_cased): MEDIA NLU model trained using [`flaubert/flaubert_base_cased`](https://huggingface.co/flaubert/flaubert_base_cased). Obtains 13.20% CER on MEDIA test.
- [`vpelloin/MEDIA_NLU-flaubert_base_uncased`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_base_uncased): MEDIA NLU model trained using [`flaubert/flaubert_base_uncased`](https://huggingface.co/flaubert/flaubert_base_uncased). Obtains 12.40% CER on MEDIA test.
- [`vpelloin/MEDIA_NLU-flaubert_oral_ft`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_oral_ft): MEDIA NLU model trained using [`nherve/flaubert-oral-ft`](https://huggingface.co/nherve/flaubert-oral-ft). Obtains 11.98% CER on MEDIA test.
- [`vpelloin/MEDIA_NLU-flaubert_oral_mixed`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_oral_mixed): MEDIA NLU model trained using [`nherve/flaubert-oral-mixed`](https://huggingface.co/nherve/flaubert-oral-mixed). Obtains 12.47% CER on MEDIA test.
- [`vpelloin/MEDIA_NLU-flaubert_oral_asr`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_oral_asr): MEDIA NLU model trained using [`nherve/flaubert-oral-asr`](https://huggingface.co/nherve/flaubert-oral-asr). Obtains 12.43% CER on MEDIA test.
- [`vpelloin/MEDIA_NLU-flaubert_oral_asr_nb`](https://huggingface.co/vpelloin/MEDIA_NLU-flaubert_oral_asr_nb): MEDIA NLU model trained using [`nherve/flaubert-oral-asr_nb`](https://huggingface.co/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_base_uncased",
    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_base_uncased"
)
model = AutoModelForTokenClassification.from_pretrained(
    "vpelloin/MEDIA_NLU-flaubert_base_uncased"
)

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](http://doi.org/10.21437/Interspeech.2022-352):
```
@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}
}
```


{"language": "fr", "tags": ["bert", "flaubert", "natural language understanding", "NLU", "spoken language understanding", "SLU", "understanding", "MEDIA"], "pipeline_tag": "token-classification", "widget": [{"text": "je voudrais r\u00e9server une chambre \u00e0 paris pour demain et lundi"}, {"text": "d'accord pour l'h\u00f4tel \u00e0 quatre vingt dix euros la nuit"}, {"text": "deux nuits s'il vous plait"}, {"text": "dans un h\u00f4tel avec piscine \u00e0 marseille"}]}
transformers pytorch tensorboard flaubert token-classification bert natural language understanding NLU spoken language understanding SLU understanding MEDIA fr autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #flaubert #token-classification #bert #natural language understanding #NLU #spoken language understanding #SLU #understanding #MEDIA #fr #autotrain_compatible #endpoints_compatible #region-us 


# vpelloin/MEDIA_NLU-flaubert_base_uncased
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 'flaubert/flaubert_base_uncased' as its inital checkpoint. It obtained 12.40% 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

## Usage with AutoTokenizer/AutoModel


## 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:



# vpelloin/MEDIA_NLU-flaubert_base_uncased
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 'flaubert/flaubert_base_uncased' as its inital checkpoint. It obtained 12.40% 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 ## Usage with AutoTokenizer/AutoModel ## 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:
TAGS
#transformers #pytorch #tensorboard #flaubert #token-classification #bert #natural language understanding #NLU #spoken language understanding #SLU #understanding #MEDIA #fr #autotrain_compatible #endpoints_compatible #region-us 
 # vpelloin/MEDIA_NLU-flaubert_base_uncased
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 'flaubert/flaubert_base_uncased' as its inital checkpoint. It obtained 12.40% 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 ## Usage with AutoTokenizer/AutoModel ## 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:
passage: TAGS
#transformers #pytorch #tensorboard #flaubert #token-classification #bert #natural language understanding #NLU #spoken language understanding #SLU #understanding #MEDIA #fr #autotrain_compatible #endpoints_compatible #region-us 
# vpelloin/MEDIA_NLU-flaubert_base_uncased
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 'flaubert/flaubert_base_uncased' as its inital checkpoint. It obtained 12.40% CER (*lower is better*) in the MEDIA test set, in our Interspeech 2023 publication, using Kaldi ASR transcriptions.