Summarization
Transformers
PyTorch
English
bart
text2text-generation
sagemaker
Eval Results (legacy)
Instructions to use slauw87/bart_summarisation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use slauw87/bart_summarisation 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="slauw87/bart_summarisation")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("slauw87/bart_summarisation") model = AutoModelForSeq2SeqLM.from_pretrained("slauw87/bart_summarisation") - Notebooks
- Google Colab
- Kaggle
Add evaluation results on the default config and test split of xsum
#6 opened over 2 years ago
by
autoevaluator
Add Core ML conversion
#4 opened almost 3 years ago
by
blackhole1123
Error when loading model
1
#3 opened almost 3 years ago
by
pritish
Adding `safetensors` variant of this model
#2 opened about 3 years ago
by
SFconvertbot
Fix typo in ROUGE metrics
#1 opened almost 4 years ago
by
lewtun