bart-large-cnn-samsum
If you want to use the model you should try a newer fine-tuned FLAN-T5 version philschmid/flan-t5-base-samsum out socring the BART version with
+6
onROGUE1
achieving47.24
.
TRY philschmid/flan-t5-base-samsum
This model was trained using Amazon SageMaker and the new Hugging Face Deep Learning container.
For more information look at:
- π€ Transformers Documentation: Amazon SageMaker
- Example Notebooks
- Amazon SageMaker documentation for Hugging Face
- Python SDK SageMaker documentation for Hugging Face
- Deep Learning Container
Hyperparameters
{
"dataset_name": "samsum",
"do_eval": true,
"do_predict": true,
"do_train": true,
"fp16": true,
"learning_rate": 5e-05,
"model_name_or_path": "facebook/bart-large-cnn",
"num_train_epochs": 3,
"output_dir": "/opt/ml/model",
"per_device_eval_batch_size": 4,
"per_device_train_batch_size": 4,
"predict_with_generate": true,
"seed": 7
}
Usage
from transformers import pipeline
summarizer = pipeline("summarization", model="philschmid/bart-large-cnn-samsum")
conversation = '''Jeff: Can I train a π€ Transformers model on Amazon SageMaker?
Philipp: Sure you can use the new Hugging Face Deep Learning Container.
Jeff: ok.
Jeff: and how can I get started?
Jeff: where can I find documentation?
Philipp: ok, ok you can find everything here. https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face
'''
summarizer(conversation)
Results
key | value |
---|---|
eval_rouge1 | 42.621 |
eval_rouge2 | 21.9825 |
eval_rougeL | 33.034 |
eval_rougeLsum | 39.6783 |
test_rouge1 | 41.3174 |
test_rouge2 | 20.8716 |
test_rougeL | 32.1337 |
test_rougeLsum | 38.4149 |
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Evaluation results
- Validation ROGUE-1 on SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarizationself-reported42.621
- Validation ROGUE-2 on SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarizationself-reported21.983
- Validation ROGUE-L on SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarizationself-reported33.034
- Test ROGUE-1 on SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarizationself-reported41.317
- Test ROGUE-2 on SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarizationself-reported20.872
- Test ROGUE-L on SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarizationself-reported32.134
- ROUGE-1 on samsumtest set verified41.328
- ROUGE-2 on samsumtest set verified20.875
- ROUGE-L on samsumtest set verified32.135
- ROUGE-LSUM on samsumtest set verified38.401