readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - enoriega/odinsynth_dataset
  model-index:
  - name: rule_learning_margin_3mm_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_3mm_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: 0.2196
  - Margin Accuracy: 0.8969
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------------:|
  | 0.3149        | 0.16  | 60   | 0.3098          | 0.8608          |
  | 0.2754        | 0.32  | 120  | 0.2725          | 0.8733          |
  | 0.2619        | 0.48  | 180  | 0.2512          | 0.8872          |
  | 0.2378        | 0.64  | 240  | 0.2391          | 0.8925          |
  | 0.2451        | 0.8   | 300  | 0.2305          | 0.8943          |
  | 0.2357        | 0.96  | 360  | 0.2292          | 0.8949          |
  | 0.2335        | 1.12  | 420  | 0.2269          | 0.8952          |
  | 0.2403        | 1.28  | 480  | 0.2213          | 0.8957          |
  | 0.2302        | 1.44  | 540  | 0.2227          | 0.8963          |
  | 0.2353        | 1.6   | 600  | 0.2222          | 0.8961          |
  | 0.2271        | 1.76  | 660  | 0.2207          | 0.8964          |
  | 0.228         | 1.92  | 720  | 0.2218          | 0.8967          |
  | 0.2231        | 2.08  | 780  | 0.2201          | 0.8967          |
  | 0.2128        | 2.24  | 840  | 0.2219          | 0.8967          |
  | 0.2186        | 2.4   | 900  | 0.2202          | 0.8967          |
  | 0.2245        | 2.56  | 960  | 0.2205          | 0.8969          |
  | 0.2158        | 2.72  | 1020 | 0.2196          | 0.8969          |
  | 0.2106        | 2.88  | 1080 | 0.2192          | 0.8968          |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.11.0
  - Datasets 2.2.1
  - Tokenizers 0.12.1
  