Instructions to use Kaludi/Quick-Summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kaludi/Quick-Summarization 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="Kaludi/Quick-Summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Kaludi/Quick-Summarization") model = AutoModelForSeq2SeqLM.from_pretrained("Kaludi/Quick-Summarization") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e2f0e5ac5062d55a1d6224fafb1321d2d356e3a75d96d367a4eff2dbdac07d4a
- Size of remote file:
- 6.6 MB
- SHA256:
- 3437b69cfccfc4c8b3f2d1818d9cf853ed2b4a419ddb024d76d6d8fd3584b113
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.