readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - enoriega/odinsynth_dataset
  model-index:
  - name: rule_learning_margin_1mm_many_negatives_spanpred_attention
    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_many_negatives_spanpred_attention
  
  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: 0.2369
  - Margin Accuracy: 0.8923
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------------:|
  | 0.3814        | 0.16  | 20   | 0.3909          | 0.8317          |
  | 0.349         | 0.32  | 40   | 0.3335          | 0.8463          |
  | 0.3196        | 0.48  | 60   | 0.3101          | 0.8587          |
  | 0.3083        | 0.64  | 80   | 0.3010          | 0.8645          |
  | 0.2828        | 0.8   | 100  | 0.2871          | 0.8686          |
  | 0.294         | 0.96  | 120  | 0.2800          | 0.8715          |
  | 0.2711        | 1.12  | 140  | 0.2708          | 0.8741          |
  | 0.2663        | 1.28  | 160  | 0.2671          | 0.8767          |
  | 0.2656        | 1.44  | 180  | 0.2612          | 0.8822          |
  | 0.2645        | 1.6   | 200  | 0.2537          | 0.8851          |
  | 0.2625        | 1.76  | 220  | 0.2483          | 0.8878          |
  | 0.2651        | 1.92  | 240  | 0.2471          | 0.8898          |
  | 0.2407        | 2.08  | 260  | 0.2438          | 0.8905          |
  | 0.2315        | 2.24  | 280  | 0.2408          | 0.8909          |
  | 0.2461        | 2.4   | 300  | 0.2390          | 0.8918          |
  | 0.2491        | 2.56  | 320  | 0.2390          | 0.8921          |
  | 0.2511        | 2.72  | 340  | 0.2369          | 0.8918          |
  | 0.2341        | 2.88  | 360  | 0.2363          | 0.8921          |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.11.0
  - Datasets 2.2.1
  - Tokenizers 0.12.1
  