Instructions to use philipp-zettl/bart-large-cnn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use philipp-zettl/bart-large-cnn with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("summarization", model="philipp-zettl/bart-large-cnn")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("philipp-zettl/bart-large-cnn") model = AutoModelForSeq2SeqLM.from_pretrained("philipp-zettl/bart-large-cnn", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download encoder_model.onnx from philipp-zettl/bart-large-cnn: direct link, hf CLI and curl.
- Browser
- Download file 815 MB
-
https://huggingface.co/philipp-zettl/bart-large-cnn/resolve/main/encoder_model.onnx
- Command line
-
hf download hf://philipp-zettl/bart-large-cnn/encoder_model.onnx
-
curl -L -o encoder_model.onnx https://huggingface.co/philipp-zettl/bart-large-cnn/resolve/main/encoder_model.onnx
815 MB
- Xet hash:
- df7edb3f44107d51ebe23831383e5cab3be098c63e856c4846bdc20a688b7a2f
- Size of remote file:
- 815 MB
- SHA256:
- 30bdca3c614e1ddf747bc53d105bff293b6830d16f54efc575915fbd1c5f325e
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