Summarization
Transformers
PyTorch
TensorFlow
JAX
Rust
Safetensors
English
bart
text2text-generation
Eval Results (legacy)
Instructions to use facebook/bart-large-cnn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/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="facebook/bart-large-cnn")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-cnn") model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-cnn", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/bart-large-cnn: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/facebook/bart-large-cnn/resolve/refs%2Fpr%2F48/pytorch_model.bin
- Command line
-
hf download hf://facebook/bart-large-cnn@refs/pr/48/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/bart-large-cnn/resolve/refs%2Fpr%2F48/pytorch_model.bin
1.63 GB
- Xet hash:
- 66ce7fd288c891f00ce1cb0021a3d93232ec4bffe232bd5c3651a642bac61bd2
- Size of remote file:
- 1.63 GB
- SHA256:
- 2ac2745c02ac987d82c78a14b426de58d5e4178ae8039ba1c6881eccff3e82f1
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