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