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README.md
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---
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tags:
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- generated_from_trainer
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model-index:
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- name: rule_learning_margin_1mm_spanpred
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# rule_learning_margin_1mm_spanpred
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This model is a fine-tuned version of [enoriega/rule_softmatching](https://huggingface.co/enoriega/rule_softmatching) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3252
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- Margin Accuracy: 0.8517
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- gradient_accumulation_steps: 2000
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- total_train_batch_size: 8000
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 3.0
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Margin Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:---------------:|
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| 0.5448 | 0.16 | 20 | 0.5229 | 0.7717 |
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| 0.4571 | 0.32 | 40 | 0.4292 | 0.8109 |
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| 0.4296 | 0.48 | 60 | 0.4009 | 0.8193 |
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| 0.4028 | 0.64 | 80 | 0.3855 | 0.8296 |
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| 0.3878 | 0.8 | 100 | 0.3757 | 0.8334 |
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| 0.3831 | 0.96 | 120 | 0.3643 | 0.8367 |
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| 0.3591 | 1.12 | 140 | 0.3582 | 0.8393 |
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| 0.3598 | 1.28 | 160 | 0.3533 | 0.8401 |
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| 0.3635 | 1.44 | 180 | 0.3442 | 0.8427 |
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| 0.3478 | 1.6 | 200 | 0.3406 | 0.8472 |
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| 0.342 | 1.76 | 220 | 0.3352 | 0.8479 |
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| 0.3327 | 1.92 | 240 | 0.3352 | 0.8486 |
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| 0.3487 | 2.08 | 260 | 0.3293 | 0.8487 |
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| 0.3387 | 2.24 | 280 | 0.3298 | 0.8496 |
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| 0.3457 | 2.4 | 300 | 0.3279 | 0.8505 |
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| 0.3483 | 2.56 | 320 | 0.3286 | 0.8510 |
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| 0.3421 | 2.72 | 340 | 0.3245 | 0.8517 |
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| 0.3332 | 2.88 | 360 | 0.3252 | 0.8517 |
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### Framework versions
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- Transformers 4.19.2
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- Pytorch 1.11.0
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- Datasets 2.2.1
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- Tokenizers 0.12.1
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