Summarization
Transformers
PyTorch
Safetensors
English
bart
text2text-generation
seq2seq
Eval Results (legacy)
Instructions to use lidiya/bart-base-samsum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lidiya/bart-base-samsum 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="lidiya/bart-base-samsum")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("lidiya/bart-base-samsum") model = AutoModelForSeq2SeqLM.from_pretrained("lidiya/bart-base-samsum") - Notebooks
- Google Colab
- Kaggle
Fix typo in ROUGE metrics
Browse filesThis PR fixes a small typo in the ROUGE metrics, replacing "rogue" with "rouge"
README.md
CHANGED
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@@ -22,23 +22,23 @@ 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: 46.6619
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- name: Validation
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type:
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value: 23.3285
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- name: Validation
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type:
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value: 39.4811
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- name: Test
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type:
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value: 44.9932
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- name: Test
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type:
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value: 21.7286
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- name: Test
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type:
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value: 38.1921
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- task:
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type: summarization
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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: 46.6619
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- name: Validation ROUGE-2
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type: rouge-2
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value: 23.3285
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- name: Validation ROUGE-L
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type: rouge-l
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value: 39.4811
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- name: Test ROUGE-1
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type: rouge-1
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value: 44.9932
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- name: Test ROUGE-2
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type: rouge-2
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value: 21.7286
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- name: Test ROUGE-L
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type: rouge-l
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value: 38.1921
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- task:
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type: summarization
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