readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  model-index:
  - name: bert-base-uncased-finetuned-math_punctuation
    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
  
  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: 0.1688
  - Cls Report: {'EMPTY': {'precision': 0.9816733583732677, 'recall': 0.9870318722937851, 'f1-score': 0.9843453228066947, 'support': 92149}, '.': {'precision': 0.7987590486039297, 'recall': 0.7989242863053372, 'f1-score': 0.7988416589099182, 'support': 4834}, ',': {'precision': 0.6800739713361073, 'recall': 0.5517629407351838, 'f1-score': 0.6092358666390557, 'support': 2666}, '?': {'precision': 0.7534498620055198, 'recall': 0.7527573529411765, 'f1-score': 0.7531034482758621, 'support': 1088}, 'accuracy': 0.9639556468824761, 'macro avg': {'precision': 0.8034890600797061, 'recall': 0.7726191130688707, 'f1-score': 0.7863815741578827, 'support': 100737}, 'weighted avg': {'precision': 0.9624492510113833, 'recall': 0.9639556468824761, 'f1-score': 0.9630189215746797, 'support': 100737}}
  - Precision: 0.8035
  - Recall: 0.7726
  - F Score: 0.7864
  - Auc: 0.9378
  
  ## 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: 8
  - 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 | Cls Report                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | Precision | Recall | F Score | Auc    |
  |:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------:|:------:|:-------:|:------:|
  | 0.0872        | 0.61  | 500  | 0.0870          | {'EMPTY': {'precision': 0.9790177368052186, 'recall': 0.9853498138883764, 'f1-score': 0.9821735697210293, 'support': 92149}, '.': {'precision': 0.7199926021823562, 'recall': 0.8053371948696731, 'f1-score': 0.7602773166682941, 'support': 4834}, ',': {'precision': 0.647912885662432, 'recall': 0.40172543135783945, 'f1-score': 0.49594813614262556, 'support': 2666}, '?': {'precision': 0.7650214592274678, 'recall': 0.6553308823529411, 'f1-score': 0.7059405940594059, 'support': 1088}, 'accuracy': 0.9577017381895431, 'macro avg': {'precision': 0.7779861709693687, 'recall': 0.7119358306172076, 'f1-score': 0.7360849041478388, 'support': 100737}, 'weighted avg': {'precision': 0.9555141484124913, 'recall': 0.9577017381895431, 'f1-score': 0.9556742202198848, 'support': 100737}} | 0.7780    | 0.7119 | 0.7361  | 0.9357 |
  | 0.0649        | 1.23  | 1000 | 0.0828          | {'EMPTY': {'precision': 0.9791280947255113, 'recall': 0.9871078362217712, 'f1-score': 0.9831017730438966, 'support': 92149}, '.': {'precision': 0.7804928131416837, 'recall': 0.7863053371948697, 'f1-score': 0.7833882934872218, 'support': 4834}, ',': {'precision': 0.6437346437346437, 'recall': 0.4913728432108027, 'f1-score': 0.557328228036588, 'support': 2666}, '?': {'precision': 0.7918454935622318, 'recal