readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - xlsum
  metrics:
  - rouge
  model-index:
  - name: t5-small-finetuned-xlsum-chinese-tradition
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: xlsum
        type: xlsum
        args: chinese_traditional
      metrics:
      - name: Rouge1
        type: rouge
        value: 0.8887
  ---
  
  <!-- 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-xlsum-chinese-tradition
  
  This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xlsum dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.2061
  - Rouge1: 0.8887
  - Rouge2: 0.0671
  - Rougel: 0.889
  - Rougelsum: 0.8838
  - Gen Len: 6.8779
  
  ## 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: 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: 6
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
  | 1.4231        | 1.0   | 2336  | 1.2586          | 0.711  | 0.0528 | 0.7029 | 0.7053    | 7.3368  |
  | 1.378         | 2.0   | 4672  | 1.2281          | 0.9688 | 0.05   | 0.9574 | 0.9656    | 7.0392  |
  | 1.3567        | 3.0   | 7008  | 1.2182          | 0.9534 | 0.1035 | 0.9531 | 0.9472    | 6.7437  |
  | 1.3339        | 4.0   | 9344  | 1.2096          | 0.9969 | 0.0814 | 0.9969 | 0.9938    | 7.4503  |
  | 1.3537        | 5.0   | 11680 | 1.2072          | 0.8429 | 0.0742 | 0.8372 | 0.838     | 6.8049  |
  | 1.3351        | 6.0   | 14016 | 1.2061          | 0.8887 | 0.0671 | 0.889  | 0.8838    | 6.8779  |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  