Instructions to use erbacher/longformercls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use erbacher/longformercls with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="erbacher/longformercls")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("erbacher/longformercls") model = AutoModelForSequenceClassification.from_pretrained("erbacher/longformercls") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4fb494347c38b17d4dacd425a22c5ec2be1e0c0b6a136c482d67e7c96214c867
- Size of remote file:
- 595 MB
- SHA256:
- 5b161a29e1800a9a3fa98b4eeba4f15d42183cfc6d8c4ef18d5043850f01e201
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