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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 57e0f00cbc79af9fb5a006a34ae0c04ede5f28ff313b7e344cdd1ca1405cde09
- Size of remote file:
- 564 kB
- SHA256:
- 944580f3e48fc53eef9c011f389270664f379a183a0feaace47173cedc9e91dc
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