Summarization
Transformers
PyTorch
TensorFlow
JAX
Rust
English
bart
text2text-generation
Eval Results (legacy)
Instructions to use facebook/bart-large-xsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/bart-large-xsum 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-xsum")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-xsum") model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-xsum", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download rust_model.ot from facebook/bart-large-xsum: direct link, hf CLI and curl.
- Browser
- Download file 2.04 GB
-
https://huggingface.co/facebook/bart-large-xsum/resolve/refs%2Fpr%2F1/rust_model.ot
- Command line
-
hf download hf://facebook/bart-large-xsum@refs/pr/1/rust_model.ot
-
curl -L -o rust_model.ot https://huggingface.co/facebook/bart-large-xsum/resolve/refs%2Fpr%2F1/rust_model.ot
2.04 GB
- Xet hash:
- 346a254a7c8eb7557d2c4d48371d041f7db004b92fa2e5cb451a05b73884f444
- Size of remote file:
- 2.04 GB
- SHA256:
- 3706d7f9974a64867550bb5c12cb33fd75d5d60f2f5af0ecd149f684f0308121
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.