Instructions to use hf-internal-testing/tiny-random-longformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-longformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-longformer")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-longformer") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-longformer") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f452914f9eade2aca44847df50eab0e24f0a10d58dd3ec46392860f4a8ea0b7a
- Size of remote file:
- 18.8 MB
- SHA256:
- 07913467fb798d1e3226752050c3c2aadaeb8e1b9767412702ae1141a902b694
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