Instructions to use VictorZhu/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VictorZhu/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="VictorZhu/results")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("VictorZhu/results") model = AutoModelForSequenceClassification.from_pretrained("VictorZhu/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 433afa9ca22d7d11f701a9bb82c93187791d9778d8c4b307f3084ae81d035bd2
- Size of remote file:
- 2.99 kB
- SHA256:
- 6a09ceee3d92bbbd25afbe29455acac8fbd046a39c9f361287da42a277c32615
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