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metadata
tags:
  - summarization
  - generated_from_trainer
metrics:
  - rouge
base_model: Ameer05/model-tokenizer-repo
model-index:
  - name: distilbart-cnn-12-6-finetuned-resume-summarizer
    results: []

distilbart-cnn-12-6-finetuned-resume-summarizer

This model is a fine-tuned version of Ameer05/model-tokenizer-repo on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 2.1123
  • Rouge1: 52.5826
  • Rouge2: 34.3861
  • Rougel: 41.8525
  • Rougelsum: 51.0015

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum
No log 0.91 5 3.2243 42.8593 24.8652 34.1789 41.406
No log 1.91 10 2.6948 48.8571 28.6711 39.2648 46.188
No log 2.91 15 2.4665 50.6085 30.4034 39.7406 48.5449
No log 3.91 20 2.3329 52.2357 32.3398 41.574 49.4316
3.6611 4.91 25 2.2362 52.0134 33.1612 41.3103 50.255
3.6611 5.91 30 2.1833 51.5434 32.7045 40.5683 49.4238
3.6611 6.91 35 2.1462 53.5144 35.4518 42.8615 51.4053
3.6611 7.91 40 2.1518 52.0985 33.6754 41.5936 50.5159
2.0326 8.91 45 2.1075 53.1401 34.9721 42.2973 51.8454
2.0326 9.91 50 2.1123 52.5826 34.3861 41.8525 51.0015

Framework versions

  • Transformers 4.15.0
  • Pytorch 1.9.1
  • Datasets 2.0.0
  • Tokenizers 0.10.3