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") - Inference
- Notebooks
- Google Colab
- Kaggle
Summarization is slow when use with pipeline
It takes a few minutes to summarize a paragraph with around 1000 tokens. Is it normal? Is there any way I could increase the speed? I tried to run it on GPU but the speed was not improved
Running it on GPU should already drastically improve the speed. Would you mind sharing your code here?
Running it on GPU should already drastically improve the speed. Would you mind sharing your code here?
Hi,
I now run it on Google Colab with high RAM T4 GPU in this way
summarizer_bart = pipeline("summarization", model="facebook/bart-large-cnn",device=0)
summarizer_bart(tg, max_length=256, min_length=64, do_sample=False, truncation=True)
where len(tg) is 3000
It still takes 4-5 minutes or even longer. Any way I can improve? Many thanks!
Yes for some reason it now takes way longer then it used to.