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:
- 02f95450f2c0f7a6b6dd01b9827ef6dc8bfe596f96d9c9bb231c2042085a5d7a
- Size of remote file:
- 268 MB
- SHA256:
- fb390bbe3e8b73336bd6a5c28c0bdedee2fc094ed6330e4ad6f84b7ae86a58f9
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