Instructions to use Scicom-intl/multilingual-dynamic-entity-decoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Scicom-intl/multilingual-dynamic-entity-decoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Scicom-intl/multilingual-dynamic-entity-decoder")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Scicom-intl/multilingual-dynamic-entity-decoder") model = AutoModelForTokenClassification.from_pretrained("Scicom-intl/multilingual-dynamic-entity-decoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 98a82eaf66ea785d448379a58d132f182a7ef2a0fb915940c4d3fddc1c16746a
- Size of remote file:
- 2.38 GB
- SHA256:
- 57c117b63df853baea1fe83df6086018b8c5c68da5a921fb9c2af1a82cb3bade
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