readme: ---
  language:
  - zh_CN
  - zh_CN
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model_index:
  - name: filter-mlsum-pretrained
    results:
    - task:
        name: Translation
        type: translation
      metric:
        name: Rouge1
        type: rouge
        value: 42.1802
  ---
  
  <!-- 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. -->
  
  # filter-mlsum-pretrained
  
  This model is a fine-tuned version of [lincoln/mbart-mlsum-automatic-summarization](https://huggingface.co/lincoln/mbart-mlsum-automatic-summarization) on an unkown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.1258
  - Rouge1: 42.1802
  - Rouge2: 28.8282
  - Rougel: 38.353
  - Rougelsum: 38.4497
  - Gen Len: 15.7048
  
  ## 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: 5e-05
  - train_batch_size: 64
  - eval_batch_size: 64
  - seed: 13
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Bleu   | Gen Len | Validation Loss | Rouge-1 | Rouge-2 | Rouge-3 | Rouge-4 |
  |:-------------:|:-----:|:-----:|:------:|:-------:|:---------------:|:-------:|:-------:|:-------:|:-------:|
  | 3.2488        | 0.02  | 600   | 1.0077 | 16.5021 | 2.9137          | 0.3472  | 0.2187  | 0.129   | 0.0831  |
  | 2.8602        | 0.04  | 1200  | 1.0448 | 15.5959 | 2.7929          | 0.3555  | 0.231   | 0.1425  | 0.0948  |
  | 2.7612        | 0.06  | 1800  | 0.9912 | 15.9283 | 2.7275          | 0.3634  | 0.2327  | 0.139   | 0.0892  |
  | 2.71          | 0.08  | 2400  | 1.1238 | 16.029  | 2.6673          | 0.3705  | 0.2393  | 0.1448  | 0.0937  |
  | 2.6029        | 0.11  | 3000  | 1.091  | 15.8317 | 2.6153          | 0.3705  | 0.2382  | 0.1443  | 0.0943  |
  | 2.5834        | 0.13  | 3600  | 1.0894 | 15.9131 | 2.5937          | 0.3793  | 0.246   | 0.1517  | 0.1013  |
  | 2.5339        | 0.15  | 4200  | 1.1034 | 15.8331 | 2.5716          | 0.3758  | 0.2441  | 0.146   | 0.0948  |
  | 2.5176        | 0.17  | 4800  | 1.1365 | 16.2552 | 2.5338          | 0.3695  | 0.2385  | 0.1454  | 0.0942  |
  | 2.4962        | 0.19  | 5400  | 1.1237 | 16.0041 | 2.5145          | 0.3773  | 0.2462  | 0.1533  | 0.1017  |
  | 2.4573        | 0.21  | 6000  | 0.9416 | 16.1241 | 2.5056          | 0.3753  | 0.2457  | 0.1541  | 0.1012  |
  | 2.4324        | 0.23  | 6600  | 1.122  | 15.3448 | 2.4891          | 0.3824  | 0.2531  | 0.157   | 0.1033  |
  | 2.4343        | 0.25  | 7200  | 1.8299 | 15.5959 | 2.4728          | 0.384   | 0.2512  | 0.1556  | 0.1026  |
  | 2.4089        | 0.28  | 7800  | 1.7741 | 16.3421 | 2.4608          | 0.3818  | 0.2501  | 0.1556  | 0.102   |
  | 2.376         | 0.3   | 8400  | 1.1575 | 15.3834 | 2.4402          | 0.3887  | 0.2582  | 0.1611  | 0.1058  |
  | 2.3739        | 0.32  | 9000  | 1.7924 | 15.6455 | 2.4331          | 0.3902  | 0.2561  | 0.1587  | 0.1042  |
  | 2.3485        | 0.34  | 9600  | 2.2605 | 15.5407 | 2.4215          | 0.3712  | 0.2423  | 0.1493  | 0.0984  |
  | 2.3535        | 0.36  | 10200 | 1.2569 | 16.2538 | 2.4047          | 0.3837  | 0.2524  | 0.1572  | 0.1045  |
  | 2.3359        | 0.38  | 10800 | 1.2334 | 15.4607 | 2.4025          | 0.3808  | 0.2488  | 0.1531  | 0.0994  |
  | 2.3265        | 0.4   | 11400 | 1.116  | 16.2703 | 2.3926          | 0.3909  | 0.2574  | 0.159   | 0.1049  |
  | 2.3024        | 0.42  | 12000 | 1.0944 | 15.3807 | 2.3964          | 0.3883  | 0.2554  | 0.158   | 0.1043  |
  | 2.2988        | 0.45  | 12600 | 1.6318 | 15.5062 | 2.3616          | 0.3889  | 0.259   | 0.1617  | 0.107   |
  | 2.2966        | 0.47  | 13200 | 1.1887 | 15.8041 | 2.3728          | 0.3835  | 0.2556  | 0.1633  | 0.1111  |
  | 2.2823        | 0.49  | 13800 | 1.1252 | 15.9972 | 2.3591          | 0.3805  | 0.249   | 0.1571  | 0.1052  |
  | 2.2748        | 0.51  | 14400 | 1.0418 | 15.3021 | 2.3619          | 0.3862  | 0.2569  | 0.161   | 0.1072  |
  | 2.2624        | 0.53  | 15000 | 1.0299 | 15.8634 | 2.3415          | 0.3909  | 0.2575  | 0.1608  | 0.1072  |
  | 2.2585        | 0.55  | 15600 | 1.0671 | 15.5503 | 2.3557          | 0.3899  | 0.2586  | 0.1622  | 0.1077  |
  | 2.2586        | 0.57  | 16200 | 1.6521 | 15.4345 | 2.3431          | 0.389   | 0.2593  | 0.1642  | 0.1105  |
  | 2.2464        | 0.59  | 16800 | 1.2836 | 15.6124 | 2.3609          | 0.3934  | 0.2591  | 0.1593  | 0.1041  |
  | 2.2523        | 0.62  | 17400 | 1.7653 | 15.8648 | 2.3339          | 0.3958  | 0.2653  | 0.1683  | 0.1133  |
  | 2.2287        | 0.64  | 18000 | 1.3186 | 16.4455 | 2.3188          | 0.3911  | 0.2617  | 0.1678  | 0.1143  |
  | 2.2068        | 0.66  | 18600 