Instructions to use psaegert/nlinec-D-coarse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use psaegert/nlinec-D-coarse with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="psaegert/nlinec-D-coarse")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("psaegert/nlinec-D-coarse") model = AutoModelForSequenceClassification.from_pretrained("psaegert/nlinec-D-coarse", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 53122baa5d3bf5d582c9662be627253f9e7341ddddbcc59d671a0d5deafaebc1
- Size of remote file:
- 1.42 GB
- SHA256:
- 5a9e68ccbe8c9319cfe72fd6b00f568bc834d7bb41c1dde8d970c2e2309a5851
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