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")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-xsum") model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-xsum") - Inference
- Notebooks
- Google Colab
- Kaggle
Add evaluation results on the default config and test split of xsum
#11 opened over 2 years ago
by
autoevaluator
Add evaluation results on the default config and test split of gigaword
#10 opened over 2 years ago
by
autoevaluator
Adding `safetensors` variant of this model
#9 opened about 3 years ago
by
SFconvertbot
Add verifyToken field to verify evaluation results are produced by Hugging Face's automatic model evaluator
#8 opened over 3 years ago
by
autoevaluator