Token Classification
Transformers
PyTorch
TensorBoard
French
flaubert
bert
natural language understanding
NLU
spoken language understanding
SLU
understanding
MEDIA
Instructions to use vpelloin/MEDIA_NLU-flaubert_base_uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vpelloin/MEDIA_NLU-flaubert_base_uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="vpelloin/MEDIA_NLU-flaubert_base_uncased")# Load model directly 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 76aafcf112744a676ee21d25722549f6fc66eed30ef7c21e0870fc215e7dc67b
- Size of remote file:
- 550 MB
- SHA256:
- 2f74c67ae5df34e090cc678253a4825e75bff8d8efdbab76cfc4d0aa85af4f1c
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