readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - enoriega/odinsynth_dataset
  model-index:
  - name: rule_learning_margin_1mm_spanpred_nospec
    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. -->
  
  # rule_learning_margin_1mm_spanpred_nospec
  
  This model is a fine-tuned version of [enoriega/rule_softmatching](https://huggingface.co/enoriega/rule_softmatching) on the enoriega/odinsynth_dataset dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3972
  - Margin Accuracy: 0.8136
  
  ## 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: 4
  - eval_batch_size: 4
  - seed: 42
  - gradient_accumulation_steps: 2000
  - total_train_batch_size: 8000
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Margin Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------------:|
  | 0.5864        | 0.16  | 20   | 0.5454          | 0.7564          |
  | 0.4995        | 0.32  | 40   | 0.4761          | 0.7867          |
  | 0.4866        | 0.48  | 60   | 0.4353          | 0.8057          |
  | 0.4568        | 0.64  | 80   | 0.4229          | 0.8098          |
  | 0.4409        | 0.8   | 100  | 0.4136          | 0.8140          |
  | 0.4369        | 0.96  | 120  | 0.4124          | 0.8118          |
  | 0.4172        | 1.12  | 140  | 0.4043          | 0.8118          |
  | 0.4208        | 1.28  | 160  | 0.4072          | 0.8119          |
  | 0.4256        | 1.44  | 180  | 0.4041          | 0.8124          |
  | 0.4201        | 1.6   | 200  | 0.4041          | 0.8127          |
  | 0.4159        | 1.76  | 220  | 0.4006          | 0.8125          |
  | 0.4103        | 1.92  | 240  | 0.4004          | 0.8131          |
  | 0.4282        | 2.08  | 260  | 0.3999          | 0.8138          |
  | 0.4169        | 2.24  | 280  | 0.4006          | 0.8136          |
  | 0.4263        | 2.4   | 300  | 0.3962          | 0.8133          |
  | 0.4252        | 2.56  | 320  | 0.3994          | 0.8137          |
  | 0.4202        | 2.72  | 340  | 0.3965          | 0.8137          |
  | 0.4146        | 2.88  | 360  | 0.3967          | 0.8139          |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.11.0
  - Datasets 2.2.1
  - Tokenizers 0.12.1
  