Instructions to use nicolauduran45/checkpoint-55652 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nicolauduran45/checkpoint-55652 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nicolauduran45/checkpoint-55652")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nicolauduran45/checkpoint-55652") model = AutoModelForSequenceClassification.from_pretrained("nicolauduran45/checkpoint-55652", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1badcb7df927aef79eb09ea5f46bfdbb60eef32e5cfde483815b0c7ff7a5bfbb
- Size of remote file:
- 5.2 kB
- SHA256:
- 8b3eacf0850472fbfa3c01cbfc3e3057df00b60cf6c23427df229f30a3256f8b
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