Summarization
Transformers
PyTorch
TensorFlow
JAX
English
pegasus
text2text-generation
Eval Results (legacy)
Instructions to use google/pegasus-xsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/pegasus-xsum 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="google/pegasus-xsum")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/pegasus-xsum") model = AutoModelForSeq2SeqLM.from_pretrained("google/pegasus-xsum") - Inference
- Notebooks
- Google Colab
- Kaggle
Add evaluation results on the default config and test split of xsum
#7
by autoevaluator HF Staff - opened
README.md
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metrics:
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- name: ROUGE-1
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type: rouge
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value: 46.
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verified: true
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- name: ROUGE-2
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type: rouge
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value: 24.
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verified: true
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type: rouge
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value: 39.
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verified: true
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- name: ROUGE-LSUM
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type: rouge
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value: 39.
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verified: true
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- name: loss
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type: loss
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value: 1.
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verified: true
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- name: gen_len
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type: gen_len
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metrics:
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- name: ROUGE-1
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type: rouge
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value: 46.861
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verified: true
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- name: ROUGE-2
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type: rouge
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value: 24.4341
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verified: true
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- name: ROUGE-L
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type: rouge
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value: 39.0618
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verified: true
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- name: ROUGE-LSUM
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type: rouge
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value: 39.0985
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verified: true
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- name: loss
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type: loss
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value: 1.5713257789611816
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verified: true
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- name: gen_len
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type: gen_len
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