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 pytorch_model.bin from facebook/bart-large-xsum: direct link, hf CLI and curl.
- Browser
- Download file 1.63 GB
-
https://huggingface.co/facebook/bart-large-xsum/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/bart-large-xsum/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/bart-large-xsum/resolve/main/pytorch_model.bin
1.63 GB
- Xet hash:
- 8e11dc1c46346d89957ff09fc896b87fe9adf97d08896a2bcb7a9ce074ea4aaf
- Size of remote file:
- 1.63 GB
- SHA256:
- 40a72b702fcbb97501f537b9fdfacdb3c310590cf08b7fe580d8bbda0f3704d1
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