Instructions to use hf-tiny-model-private/tiny-random-LongformerForSequenceClassification 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-LongformerForSequenceClassification 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-LongformerForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LongformerForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-LongformerForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 98939dee00185e44b36ddec7a37d75cb6ed6b30136eeaea4b67baf74e10eb5ab
- Size of remote file:
- 416 kB
- SHA256:
- 46355dbebcc6bddc73a0d78c1d4ad99c9f80060ab2b26aac1034632861cf31fd
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