Instructions to use vinhainsec/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vinhainsec/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vinhainsec/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vinhainsec/results") model = AutoModelForSequenceClassification.from_pretrained("vinhainsec/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2ff5ef528d0911c8babe1a96976e49a1aa885ac80fd989e63413c20cd04f695c
- Size of remote file:
- 5.18 kB
- SHA256:
- 5ec2e6cc925779798971a564318106a6adeed12095bfcce09d2a578333280444
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