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
Commit ·
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Parent(s): 5c42623
edited readme
Browse files
README.md
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## Citation:
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@article{dickson2024going,
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title={Going against the grain: Climate change as a wedge issue for the radical right},
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author={Dickson, Zachary P and Hobolt, Sara B},
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journal={Comparative Political Studies},
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year={2024},
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publisher={SAGE Publications Sage CA: Los Angeles, CA}
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}
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