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patent-summarization-t5-base-2022-09-20

Text2Text Generation
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PyTorch
t5
Generated from Trainer
Eval Results
text-generation-inference
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - farleyknight/big_patent_5_percent
metrics:
  - rouge
model-index:
  - name: patent-summarization-t5-base-2022-09-20
    results:
      - task:
          name: Summarization
          type: summarization
        dataset:
          name: farleyknight/big_patent_5_percent
          type: farleyknight/big_patent_5_percent
          config: all
          split: train
          args: all
        metrics:
          - name: Rouge1
            type: rouge
            value: 36.0843

patent-summarization-t5-base-2022-09-20

This model is a fine-tuned version of t5-base on the farleyknight/big_patent_5_percent dataset. It achieves the following results on the evaluation set:

  • Loss: 1.9975
  • Rouge1: 36.0843
  • Rouge2: 12.1856
  • Rougel: 25.8099
  • Rougelsum: 30.1664
  • Gen Len: 118.3137

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: 1
  • eval_batch_size: 1
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 1.0

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
2.2811 0.08 5000 2.1767 18.5624 6.8795 15.5361 16.6836 19.0
2.2551 0.17 10000 2.1327 19.077 6.8512 15.79 17.086 19.0
2.2818 0.25 15000 2.1029 18.8637 6.9233 15.7341 16.9717 19.0
2.1952 0.33 20000 2.0805 18.962 7.1157 15.8297 17.0333 19.0
2.157 0.41 25000 2.0641 19.1418 7.315 16.05 17.2551 19.0
2.1775 0.5 30000 2.0452 19.2387 7.3193 16.0852 17.3563 19.0
2.1376 0.58 35000 2.0308 19.291 7.363 16.1243 17.4151 19.0
2.1853 0.66 40000 2.0207 19.2808 7.4671 16.1593 17.3836 19.0
2.1416 0.75 45000 2.0113 19.0414 7.3335 15.9747 17.1899 19.0
2.1245 0.83 50000 2.0055 19.1445 7.3715 16.0166 17.2621 19.0
2.133 0.91 55000 1.9997 19.3033 7.4821 16.1413 17.3949 19.0
2.1191 0.99 60000 1.9973 19.4044 7.5483 16.2429 17.488 19.0

Framework versions

  • Transformers 4.23.0.dev0
  • Pytorch 1.12.0
  • Datasets 2.4.0
  • Tokenizers 0.12.1