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:
- dc23fcd1680547408b7009aa4cef024d41529428566ced76bb1a949f6683871e
- Size of remote file:
- 16.2 MB
- SHA256:
- 72f741e7347ec41a9f27909eb042dbe0d830be3f9c8d201d46e790ceafc07e32
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