Instructions to use hf-tiny-model-private/tiny-random-LiltForSequenceClassification 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-LiltForSequenceClassification 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-LiltForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LiltForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-LiltForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7ee32347479f43bdcb54a3a17285fddea58895119ec0e615e2f1c9c65b1d4604
- Size of remote file:
- 297 kB
- SHA256:
- 4e393d238746d614e75da0dd82a33fb56cdc12a0bcbf835bbdcff3486989a7e6
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