Edit model card

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 on ROGUE1 achieving 47.24.

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:

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 πŸ€— 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
Downloads last month
366,782
Inference API
Examples

Model tree for philschmid/bart-large-cnn-samsum

Adapters
1 model
Finetunes
5 models

Dataset used to train philschmid/bart-large-cnn-samsum

Spaces using philschmid/bart-large-cnn-samsum 61

Evaluation results

  • Validation ROGUE-1 on SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization
    self-reported
    42.621
  • Validation ROGUE-2 on SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization
    self-reported
    21.983
  • Validation ROGUE-L on SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization
    self-reported
    33.034
  • Test ROGUE-1 on SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization
    self-reported
    41.317
  • Test ROGUE-2 on SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization
    self-reported
    20.872
  • Test ROGUE-L on SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization
    self-reported
    32.134
  • ROUGE-1 on samsum
    test set verified
    41.328
  • ROUGE-2 on samsum
    test set verified
    20.875
  • ROUGE-L on samsum
    test set verified
    32.135
  • ROUGE-LSUM on samsum
    test set verified
    38.401