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
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# Quick Summarization
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This is a
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### Gradio
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# Quick Summarization
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This is a Text Summarization Model that has been trained by [Kaludi](https://huggingface.co/Kaludi) to Transform long and complex texts into concise and meaningful summaries. Get a quick and accurate overview of any document in seconds, saving you time and effort.
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### Gradio
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