multinews_model / README.md
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metadata
license: apache-2.0
base_model: t5-small
tags:
  - generated_from_trainer
datasets:
  - multi_news
metrics:
  - rouge
model-index:
  - name: multinews_model
    results:
      - task:
          name: Sequence-to-sequence Language Modeling
          type: text2text-generation
        dataset:
          name: multi_news
          type: multi_news
          config: default
          split: test
          args: default
        metrics:
          - name: Rouge1
            type: rouge
            value: 0.1482

multinews_model

This model is a fine-tuned version of t5-small on the multi_news dataset. It achieves the following results on the evaluation set:

  • Loss: 2.7165
  • Rouge1: 0.1482
  • Rouge2: 0.0472
  • Rougel: 0.1132
  • Rougelsum: 0.1132
  • Gen Len: 19.0

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: 10
  • eval_batch_size: 10
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 12

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
No log 1.0 450 2.8616 0.1388 0.0418 0.1057 0.1056 19.0
3.2544 2.0 900 2.7991 0.1427 0.0438 0.1089 0.1089 19.0
2.999 3.0 1350 2.7693 0.1449 0.046 0.1115 0.1114 19.0
2.958 4.0 1800 2.7531 0.1466 0.0462 0.112 0.1118 19.0
2.9198 5.0 2250 2.7431 0.1466 0.0465 0.112 0.1119 19.0
2.8838 6.0 2700 2.7328 0.1474 0.0461 0.1125 0.1123 19.0
2.8774 7.0 3150 2.7270 0.1477 0.0463 0.1126 0.1124 19.0
2.8712 8.0 3600 2.7226 0.148 0.0466 0.1128 0.1127 19.0
2.854 9.0 4050 2.7197 0.1479 0.047 0.1129 0.1128 19.0
2.8541 10.0 4500 2.7188 0.1485 0.0471 0.113 0.1129 19.0
2.8541 11.0 4950 2.7168 0.1483 0.0472 0.1131 0.1131 19.0
2.8466 12.0 5400 2.7165 0.1482 0.0472 0.1132 0.1132 19.0

Framework versions

  • Transformers 4.33.3
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.13.3