readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - enoriega/odinsynth_dataset
  model-index:
  - name: rule_learning_1mm_many_negatives_spanpred_margin_avg
    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_1mm_many_negatives_spanpred_margin_avg
  
  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: 0.2421
  - Margin Accuracy: 0.8897
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------------:|
  | 0.3867        | 0.16  | 20   | 0.4023          | 0.8187          |
  | 0.3506        | 0.32  | 40   | 0.3381          | 0.8523          |
  | 0.3195        | 0.48  | 60   | 0.3096          | 0.8613          |
  | 0.3052        | 0.64  | 80   | 0.2957          | 0.8640          |
  | 0.2859        | 0.8   | 100  | 0.2922          | 0.8679          |
  | 0.297         | 0.96  | 120  | 0.2871          | 0.8688          |
  | 0.2717        | 1.12  | 140  | 0.2761          | 0.8732          |
  | 0.2671        | 1.28  | 160  | 0.2751          | 0.8743          |
  | 0.2677        | 1.44  | 180  | 0.2678          | 0.8757          |
  | 0.2693        | 1.6   | 200  | 0.2627          | 0.8771          |
  | 0.2675        | 1.76  | 220  | 0.2573          | 0.8813          |
  | 0.2732        | 1.92  | 240  | 0.2546          | 0.8858          |
  | 0.246         | 2.08  | 260  | 0.2478          | 0.8869          |
  | 0.2355        | 2.24  | 280  | 0.2463          | 0.8871          |
  | 0.2528        | 2.4   | 300  | 0.2449          | 0.8886          |
  | 0.2512        | 2.56  | 320  | 0.2443          | 0.8892          |
  | 0.2527        | 2.72  | 340  | 0.2441          | 0.8893          |
  | 0.2346        | 2.88  | 360  | 0.2424          | 0.8895          |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.11.0
  - Datasets 2.2.1
  - Tokenizers 0.12.1
  