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bart_extractive_1024_1000

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metadata
license: apache-2.0
base_model: facebook/bart-large
tags:
  - generated_from_trainer
metrics:
  - rouge
  - wer
model-index:
  - name: bart_extractive_1024_1000
    results: []

bart_extractive_1024_1000

This model is a fine-tuned version of facebook/bart-large on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8802
  • Rouge1: 0.7215
  • Rouge2: 0.4773
  • Rougel: 0.668
  • Rougelsum: 0.668
  • Wer: 0.4137
  • Bleurt: -0.027

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: 6
  • eval_batch_size: 6
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 2
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Wer Bleurt
No log 0.13 250 1.1362 0.6713 0.4064 0.6113 0.6111 0.4774 -0.1118
2.0454 0.27 500 1.0337 0.6869 0.4301 0.6289 0.6288 0.4555 -0.1734
2.0454 0.4 750 1.0002 0.7017 0.4465 0.6435 0.6434 0.4467 -0.357
1.0987 0.53 1000 0.9747 0.7008 0.4469 0.6423 0.6422 0.442 -0.0679
1.0987 0.66 1250 0.9589 0.7092 0.456 0.6521 0.652 0.4363 0.2669
1.0418 0.8 1500 0.9551 0.704 0.4538 0.6486 0.6485 0.4343 -0.1447
1.0418 0.93 1750 0.9316 0.7096 0.4605 0.6546 0.6544 0.4285 -0.0465
1.0031 1.06 2000 0.9150 0.7129 0.4653 0.6584 0.6583 0.4255 -0.1069
1.0031 1.2 2250 0.9094 0.7119 0.4658 0.6577 0.6576 0.4234 -0.4062
0.9052 1.33 2500 0.9101 0.721 0.4736 0.6665 0.6664 0.4206 0.2201
0.9052 1.46 2750 0.8983 0.7161 0.471 0.6619 0.6618 0.4184 0.0117
0.9045 1.6 3000 0.8917 0.7216 0.4762 0.6675 0.6674 0.4169 0.2346
0.9045 1.73 3250 0.8906 0.7167 0.474 0.6643 0.6642 0.4153 -0.0679
0.8767 1.86 3500 0.8797 0.7232 0.4787 0.6698 0.6697 0.4141 0.2346
0.8767 1.99 3750 0.8802 0.7215 0.4773 0.668 0.668 0.4137 -0.027

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2