t5-small-finetuned-xsum

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

---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->

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

Framework versions

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