Instructions to use wesleymorris/summary-longformer-content-da with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wesleymorris/summary-longformer-content-da with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wesleymorris/summary-longformer-content-da")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("wesleymorris/summary-longformer-content-da") model = AutoModelForSequenceClassification.from_pretrained("wesleymorris/summary-longformer-content-da", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from wesleymorris/summary-longformer-content-da: direct link, hf CLI and curl.
- Browser
- Download file 595 MB
-
https://huggingface.co/wesleymorris/summary-longformer-content-da/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://wesleymorris/summary-longformer-content-da/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/wesleymorris/summary-longformer-content-da/resolve/main/pytorch_model.bin
595 MB
- Xet hash:
- 8c10012e13150a9ee7768710f730770db2821d3e0e1fe1e6e680218809b98fc6
- Size of remote file:
- 595 MB
- SHA256:
- 8c1f0aceb8dca5ce385148c6354681eea1458d04206bec6020ed82a19d8abfb9
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