Instructions to use hf-tiny-model-private/tiny-random-TransfoXLForSequenceClassification 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-TransfoXLForSequenceClassification 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-TransfoXLForSequenceClassification")# Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-TransfoXLForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6710db08b05a76adb8eba0cdf2bb4c70ac846cafa8257565dd1f557459101b23
- Size of remote file:
- 4.67 MB
- SHA256:
- 74355acd019148eee600a331f36e7e606ff41ddc4ff1888807b67e72da6d7989
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