Instructions to use VictorZhu/Anchor-Classification-DMV with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VictorZhu/Anchor-Classification-DMV with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VictorZhu/Anchor-Classification-DMV")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VictorZhu/Anchor-Classification-DMV") model = AutoModelForSequenceClassification.from_pretrained("VictorZhu/Anchor-Classification-DMV", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a6559a08511da1d81018b15307926f4c88aabd7d5f40071cd42604c50d588f1e
- Size of remote file:
- 268 MB
- SHA256:
- 9cea3c5ad3b6647f7d1782fb238fd305b40089c982bc68eae1694474f3daece2
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