Instructions to use hf-tiny-model-private/tiny-random-LukeForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-LukeForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-tiny-model-private/tiny-random-LukeForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LukeForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-LukeForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ea4e983c05a7947536c7552b6b6fbc9ee1b70c07176d74f7f5279fd5b4ae8dd8
- Size of remote file:
- 6.81 MB
- SHA256:
- 4e9bf0ea8934a22d3515489cc0db79e4a3a99f7aee1f0018177b771d37510ed9
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