Summarization
Transformers
PyTorch
Safetensors
English
pegasus
text2text-generation
Eval Results (legacy)
Instructions to use pszemraj/pegasus-large-summary-explain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pszemraj/pegasus-large-summary-explain 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="pszemraj/pegasus-large-summary-explain")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("pszemraj/pegasus-large-summary-explain") model = AutoModelForSeq2SeqLM.from_pretrained("pszemraj/pegasus-large-summary-explain", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
#4
by librarian-bot - opened
README.md
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@@ -165,6 +165,7 @@ inference:
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length_penalty: 0.5
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num_beams: 4
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early_stopping: true
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model-index:
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- name: pszemraj/pegasus-large-summary-explain
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results:
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length_penalty: 0.5
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num_beams: 4
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early_stopping: true
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base_model: google/pegasus-large
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model-index:
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- name: pszemraj/pegasus-large-summary-explain
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results:
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