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
Update README.md
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README.md
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### Gradio
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Tis model supports a [Gradio](https://github.com/gradio-app/gradio) Web UI to run the data-food-classification model:
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[](https://huggingface.co/spaces/Kaludi/
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## Validation Metrics
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### Gradio
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Tis model supports a [Gradio](https://github.com/gradio-app/gradio) Web UI to run the data-food-classification model:
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[](https://huggingface.co/spaces/Kaludi/Quick-Summarizer_App)
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## Validation Metrics
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