readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - null
  model_index:
  - name: bert-base-chinese-complaint-128
    results:
    - task:
        name: Masked Language Modeling
        type: fill-mask
  ---
  
  <!-- 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. -->
  
  # bert-base-chinese-complaint-128
  
  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.3004
  
  ## 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: 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: 16
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss |
  |:-------------:|:-----:|:-----:|:---------------:|
  | 3.3735        | 1.0   | 1250  | 2.4628          |
  | 2.2412        | 2.0   | 2500  | 2.0378          |
  | 1.9251        | 3.0   | 3750  | 1.8368          |
  | 1.7407        | 4.0   | 5000  | 1.6972          |
  | 1.6137        | 5.0   | 6250  | 1.5937          |
  | 1.5365        | 6.0   | 7500  | 1.5315          |
  | 1.4662        | 7.0   | 8750  | 1.4921          |
  | 1.3985        | 8.0   | 10000 | 1.4517          |
  | 1.3509        | 9.0   | 11250 | 1.4308          |
  | 1.3047        | 10.0  | 12500 | 1.3906          |
  | 1.2745        | 11.0  | 13750 | 1.3467          |
  | 1.2377        | 12.0  | 15000 | 1.3306          |
  | 1.2139        | 13.0  | 16250 | 1.3205          |
  | 1.2027        | 14.0  | 17500 | 1.3098          |
  | 1.1722        | 15.0  | 18750 | 1.2845          |
  | 1.1697        | 16.0  | 20000 | 1.3004          |
  
  
  ### Framework versions
  
  - Transformers 4.8.2
  - Pytorch 1.7.1
  - Datasets 1.16.1
  - Tokenizers 0.10.3
  