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metadata
tags:
  - generated_from_trainer
datasets:
  - orange_sum
metrics:
  - rouge
model-index:
  - name: bert2gpt2SUMM-finetuned-mlsum-finetuned-mlorange_sum
    results:
      - task:
          name: Sequence-to-sequence Language Modeling
          type: text2text-generation
        dataset:
          name: orange_sum
          type: orange_sum
          args: abstract
        metrics:
          - name: Rouge1
            type: rouge
            value: 24.949
Map of positive probabilities per country.

bert2gpt2SUMM-finetuned-mlsum-finetuned-mlorange_sum

This model is a fine-tuned version of Chemsseddine/bert2gpt2SUMM-finetuned-mlsum on the orange_sum dataset. It achieves the following results on the evaluation set:

  • Loss: 3.1773
  • Rouge1: 24.949
  • Rouge2: 7.851
  • Rougel: 18.1575
  • Rougelsum: 18.4114
  • Gen Len: 39.7947

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: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 1
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
3.5484 1.0 1338 3.1773 24.949 7.851 18.1575 18.4114 39.7947

Framework versions

  • Transformers 4.20.0
  • Pytorch 1.11.0+cu113
  • Datasets 2.3.2
  • Tokenizers 0.12.1