summarise_cy / README.md
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metadata
library_name: transformers
license: apache-2.0
base_model: google-t5/t5-large
tags:
  - generated_from_trainer
datasets:
  - generator
metrics:
  - rouge
model-index:
  - name: summarise_cy
    results:
      - task:
          name: Sequence-to-sequence Language Modeling
          type: text2text-generation
        dataset:
          name: generator
          type: generator
          config: default
          split: train
          args: default
        metrics:
          - name: Rouge1
            type: rouge
            value: 0.1434

summarise_cy

This model is a fine-tuned version of google-t5/t5-large on the generator dataset. It achieves the following results on the evaluation set:

  • Loss: nan
  • Rouge1: 0.1434
  • Rouge2: 0.0535
  • Rougel: 0.1286
  • Rougelsum: 0.1287
  • Gen Len: 20.0

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: 1
  • eval_batch_size: 1
  • seed: 42
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 10
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
No log 1.0 410 nan 0.1434 0.0535 0.1286 0.1287 20.0
0.0 2.0 820 nan 0.1434 0.0535 0.1286 0.1287 20.0
0.0 3.0 1230 nan 0.1434 0.0535 0.1286 0.1287 20.0
0.0 4.0 1640 nan 0.1434 0.0535 0.1286 0.1287 20.0
0.0 5.0 2050 nan 0.1434 0.0535 0.1286 0.1287 20.0
0.0 6.0 2460 nan 0.1434 0.0535 0.1286 0.1287 20.0
0.0 7.0 2870 nan 0.1434 0.0535 0.1286 0.1287 20.0
0.0 8.0 3280 nan 0.1434 0.0535 0.1286 0.1287 20.0
0.0 9.0 3690 nan 0.1434 0.0535 0.1286 0.1287 20.0
0.0 10.0 4100 nan 0.1434 0.0535 0.1286 0.1287 20.0

Framework versions

  • Transformers 4.52.4
  • Pytorch 2.6.0+cu124
  • Datasets 3.6.0
  • Tokenizers 0.21.1