Instructions to use hf-internal-testing/tiny-random-LiltForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LiltForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-LiltForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-LiltForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-LiltForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4152f895c375b02732647c862a287e799c4172e34e9a7205d7b0e604988be392
- Size of remote file:
- 297 kB
- SHA256:
- c4e8a53e30ce79085357d8bfdffa68539e62e069261f189b08eaa0c23d601d25
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