Summarization
Transformers
PyTorch
English
bart
text2text-generation
sagemaker
Eval Results (legacy)
Instructions to use slauw87/bart_summarisation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use slauw87/bart_summarisation 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="slauw87/bart_summarisation")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("slauw87/bart_summarisation") model = AutoModelForSeq2SeqLM.from_pretrained("slauw87/bart_summarisation") - Notebooks
- Google Colab
- Kaggle
Fix typo in ROUGE metrics
#1
by lewtun HF Staff - opened
README.md
CHANGED
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@@ -18,23 +18,23 @@ model-index:
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name: "SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization"
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type: samsum
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metrics:
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- name: Validation
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type:
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value: 43.2111
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- name: Validation
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type:
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value: 22.3519
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- name: Validation
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type:
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value: 33.315
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- name: Test
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type:
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value: 41.8283
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- name: Test
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type:
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value: 20.9857
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- name: Test
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type:
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value: 32.3602
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widget:
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- text: |
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name: "SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization"
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type: samsum
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metrics:
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- name: Validation ROUGE-1
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type: rouge-1
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value: 43.2111
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- name: Validation ROUGE-2
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type: rouge-2
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value: 22.3519
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- name: Validation ROUGE-L
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type: rouge-l
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value: 33.315
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- name: Test ROUGE-1
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type: rouge-1
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value: 41.8283
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- name: Test ROUGE-2
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type: rouge-2
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value: 20.9857
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- name: Test ROUGE-L
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type: rouge-l
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value: 32.3602
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widget:
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- text: |
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