Instructions to use hf-internal-testing/tiny-random-LongformerForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LongformerForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-LongformerForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-LongformerForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-LongformerForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1d0f3351705f1eaf2fe909160139d54fe904b36944d63738a4ea58567a03df14
- Size of remote file:
- 442 kB
- SHA256:
- 2aecdf69ff5dfc9a01a191acdd52582dd7850f9ccdcfe0ad6652cc10590eb0f5
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