readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: bert-base-uncased-finetuned-math_punctuation-25-01-two_linear_layers-frozen_bert
    results: []
  ---
  
  <!-- 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-uncased-finetuned-math_punctuation-25-01-two_linear_layers-frozen_bert
  
  This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2150
  - Micro f1: 0.8910
  - Macro f1: 0.2672
  - Weighted f1: 0.8495
  
  ## 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: 4
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 12
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Micro f1 | Macro f1 | Weighted f1 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-----------:|
  | 0.193         | 0.62  | 500  | 0.2146          | 0.8937   | 0.2360   | 0.8435      |
  | 0.1936        | 1.23  | 1000 | 0.2130          | 0.8937   | 0.2360   | 0.8435      |
  | 0.1924        | 1.85  | 1500 | 0.2119          | 0.8937   | 0.2361   | 0.8435      |
  | 0.1911        | 2.47  | 2000 | 0.2128          | 0.8936   | 0.2369   | 0.8437      |
  | 0.1909        | 3.09  | 2500 | 0.2114          | 0.8937   | 0.2369   | 0.8437      |
  | 0.1904        | 3.7   | 3000 | 0.2137          | 0.8935   | 0.2407   | 0.8445      |
  | 0.1935        | 4.32  | 3500 | 0.2138          | 0.8934   | 0.2469   | 0.8458      |
  | 0.1874        | 4.94  | 4000 | 0.2118          | 0.8929   | 0.2561   | 0.8479      |
  | 0.1908        | 5.56  | 4500 | 0.2134          | 0.8925   | 0.2588   | 0.8483      |
  | 0.1877        | 6.17  | 5000 | 0.2135          | 0.8918   | 0.2628   | 0.8490      |
  | 0.1881        | 6.79  | 5500 | 0.2133          | 0.8931   | 0.2554   | 0.8478      |
  | 0.1902        | 7.41  | 6000 | 0.2137          | 0.8922   | 0.2603   | 0.8485      |
  | 0.1883        | 8.02  | 6500 | 0.2155          | 0.8914   | 0.2655   | 0.8493      |
  | 0.19          | 8.64  | 7000 | 0.2154          | 0.8914   | 0.2647   | 0.8490      |
  | 0.1881        | 9.26  | 7500 | 0.2149          | 0.8915   | 0.2645   | 0.8492      |
  | 0.1876        | 9.88  | 8000 | 0.2141          | 0.8911   | 0.2671   | 0.8496      |
  | 0.1879        | 10.49 | 8500 | 0.2155          | 0.8897   | 0.2722   | 0.8501      |
  | 0.1897        | 11.11 | 9000 | 0.2156          | 0.8910   | 0.2670   | 0.8494      |
  | 0.1883        | 11.73 | 9500 | 0.2150          | 0.8910   | 0.2672   | 0.8495      |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 2.0.0.dev20230111
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  