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t5-base-DreamBank-Generation-Act-Char

Text2Text Generation
Transformers
PyTorch
English
t5
Generated from Trainer
relation-extraction
text-generation-inference
Inference Endpoints
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t5-base-DreamBank-Generation-Act-Char / README.md
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - rouge
model-index:
  - name: t5-base-DreamBank-Generation-Act-Char
    results: []
language:
  - en
widget:
  - text: >-
      I was skating on the outdoor ice pond that used to be across the street
      from my house. I was not alone, but I did not recognize any of the other
      people who were skating around. I went through my whole repertoire of
      jumps, spires, and steps-some of which I can do and some of which I'm not
      yet sure of. They were all executed flawlessly-some I repeated, some I did
      only once. I seemed to know that if I went into competition, I would be
      sure of coming in third because there were only three contestants. Up to
      that time I hadn't considered it because I hadn't thought I was good
      enough, but now since everything was going so well, I decided to enter.
    example_title: Dream

t5-base-DreamBank-Generation-Act-Char

This model is a fine-tuned version of DReAMy-lib/t5-base-DreamBank-Generation-NER-Char on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3058
  • Rouge1: 0.4708
  • Rouge2: 0.4040
  • Rougel: 0.4576
  • Rougelsum: 0.4572

Model description

Training procedure

The model is trained end-to-end using a text2text solution to annotate dream reports following the Activity feature from the Hall and Van de Castle scoring framework. Given a report, the model generates texts of the form [(initialiser : activity type : receiver)]. For those cases where initialiser and receiver are the same entity, the output will follow the [(initialiser : alone : none)] setting.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.001
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum
No log 1.0 49 0.3674 0.4008 0.3122 0.3821 0.3812
No log 2.0 98 0.3200 0.4240 0.3433 0.4130 0.4121
No log 3.0 147 0.2845 0.4591 0.3883 0.4459 0.4455
No log 4.0 196 0.2508 0.4614 0.3930 0.4504 0.4497
No log 5.0 245 0.2632 0.4614 0.3929 0.4467 0.4459
No log 6.0 294 0.2688 0.4706 0.4036 0.4537 0.4534
No log 7.0 343 0.2790 0.4682 0.4043 0.4559 0.4556
No log 8.0 392 0.2895 0.4670 0.3972 0.4529 0.4534
No log 9.0 441 0.3058 0.4708 0.4040 0.4576 0.4572
No log 10.0 490 0.3169 0.4690 0.4001 0.4547 0.4544

Framework versions

  • Transformers 4.25.1
  • Pytorch 1.12.1
  • Datasets 2.5.1
  • Tokenizers 0.12.1