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RMWeerasinghe
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t5-small-govReport-boardpapers-3072

Summarization
Transformers
Safetensors
t5
text2text-generation
Summarization
Generated from Trainer
text-generation-inference
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t5-small-govReport-boardpapers-3072 / README.md
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metadata
license: apache-2.0
base_model: RMWeerasinghe/t5-small-finetuned-govReport-3072
tags:
  - Summarization
  - generated_from_trainer
metrics:
  - rouge
model-index:
  - name: t5-small-govReport-boardpapers-3072
    results: []

t5-small-govReport-boardpapers-3072

This model is a fine-tuned version of RMWeerasinghe/t5-small-finetuned-govReport-3072 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 3.6701
  • Rouge1: 0.0443
  • Rouge2: 0.0194
  • Rougel: 0.0382
  • Rougelsum: 0.0443

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: 4e-05
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 40

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum
No log 0.67 1 3.9496 0.0584 0.0214 0.0482 0.0572
No log 2.0 3 3.9252 0.0562 0.0223 0.0463 0.0562
No log 2.67 4 3.9121 0.0597 0.0223 0.0485 0.0596
No log 4.0 6 3.8880 0.0597 0.0223 0.0485 0.0596
No log 4.67 7 3.8755 0.0597 0.0223 0.0485 0.0596
No log 6.0 9 3.8506 0.0597 0.0223 0.0485 0.0596
No log 6.67 10 3.8395 0.0553 0.0197 0.0441 0.0541
No log 8.0 12 3.8172 0.0582 0.0262 0.049 0.057
No log 8.67 13 3.8065 0.0582 0.0262 0.049 0.057
No log 10.0 15 3.7862 0.0582 0.0257 0.049 0.057
No log 10.67 16 3.7769 0.057 0.0262 0.049 0.0556
No log 12.0 18 3.7599 0.0577 0.0294 0.0495 0.0575
No log 12.67 19 3.7522 0.0487 0.0174 0.042 0.0474
4.3528 14.0 21 3.7378 0.048 0.0155 0.0406 0.0461
4.3528 14.67 22 3.7310 0.0536 0.0206 0.0421 0.0511
4.3528 16.0 24 3.7187 0.048 0.017 0.0394 0.0448
4.3528 16.67 25 3.7132 0.043 0.017 0.0374 0.041
4.3528 18.0 27 3.7031 0.043 0.017 0.0374 0.041
4.3528 18.67 28 3.6985 0.043 0.017 0.0374 0.041
4.3528 20.0 30 3.6905 0.043 0.017 0.0374 0.041
4.3528 20.67 31 3.6869 0.043 0.017 0.0374 0.041
4.3528 22.0 33 3.6807 0.0442 0.0194 0.0381 0.0423
4.3528 22.67 34 3.6781 0.0442 0.0194 0.0381 0.0423
4.3528 24.0 36 3.6740 0.0442 0.0194 0.0381 0.0423
4.3528 24.67 37 3.6725 0.0442 0.0194 0.0381 0.0423
4.3528 26.0 39 3.6705 0.0443 0.0194 0.0382 0.0443
4.0602 26.67 40 3.6701 0.0443 0.0194 0.0382 0.0443

Framework versions

  • Transformers 4.37.0
  • Pytorch 2.1.2
  • Datasets 2.17.0
  • Tokenizers 0.15.1