Instructions to use hf-internal-testing/tiny-random-LukeForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LukeForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-LukeForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-LukeForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-LukeForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2495683a6f5d99d90c961efeb67ed9977cde816ebadae76b70b6c1117f0c09f7
- Size of remote file:
- 6.81 MB
- SHA256:
- 02b173e5439085fb9144458f3cf24668c564d3087736a02c4dac1b9e8642667f
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