Summarization
Transformers
Safetensors
t5
text2text-generation
politics,
summarization,
climate
political
party,
press
european
text-generation-inference
Instructions to use tdickson17/Text_Summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tdickson17/Text_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="tdickson17/Text_Summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("tdickson17/Text_Summarization") model = AutoModelForSeq2SeqLM.from_pretrained("tdickson17/Text_Summarization") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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library_name: transformers
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pipeline_tag: summarization
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---
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# Text Summarization
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The model used in this summarization task is a T5 summarization transformer-based language model fine-tuned for abstractive summarization.
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library_name: transformers
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pipeline_tag: summarization
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---
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tags:
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- politics
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- summarization
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- climate change
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- political party
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- press release
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- political communication
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- European Union
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- Speech
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license: afl-3.0
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language:
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- en
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- es
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- da
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- de
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- it
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- fr
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- nl
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- pl
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# Text Summarization
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The model used in this summarization task is a T5 summarization transformer-based language model fine-tuned for abstractive summarization.
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