Summarization
Transformers
PyTorch
English
t5
text2text-generation
extractive
summary
abstractive
multi-task
document summary
text-generation-inference
Instructions to use jordiclive/flan-t5-11b-summarizer-filtered-1.5-epoch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jordiclive/flan-t5-11b-summarizer-filtered-1.5-epoch 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="jordiclive/flan-t5-11b-summarizer-filtered-1.5-epoch")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("jordiclive/flan-t5-11b-summarizer-filtered-1.5-epoch") model = AutoModelForSeq2SeqLM.from_pretrained("jordiclive/flan-t5-11b-summarizer-filtered-1.5-epoch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# Multi-purpose Summarizer (Fine-tuned 11B google/flan-t5-xxl on several Summarization datasets)
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<a href="https://colab.research.google.com/drive/
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
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# Multi-purpose Summarizer (Fine-tuned 11B google/flan-t5-xxl on several Summarization datasets)
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<a href="https://colab.research.google.com/drive/1MQYzGD8Ksi2GDjHhNN0t-DY7LxuRz5N9">
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
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