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这个模型是根据这个一步一步完成的,如果想自己微调,请参考https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/summarization.ipynb This model is completed step by step according to this, if you want to fine-tune yourself, please refer to https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/summarization.ipynb


license: apache-2.0 tags:

  • generated_from_trainer datasets:
  • xsum metrics:
  • rouge model-index:
  • name: t5-small-finetuned-xsum results:
    • task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: xsum type: xsum args: default metrics:
      • name: Rouge1 type: rouge value: 28.6901

t5-small-finetuned-xsum

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

  • Loss: 2.4500
  • Rouge1: 28.6901
  • Rouge2: 8.0102
  • Rougel: 22.6087
  • Rougelsum: 22.6105
  • Gen Len: 18.824

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: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 1
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
2.6799 1.0 25506 2.4500 28.6901 8.0102 22.6087 22.6105 18.824

Framework versions

  • Transformers 4.12.3
  • Pytorch 1.9.0+cu111
  • Datasets 1.15.1
  • Tokenizers 0.10.3
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