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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- enoriega/odinsynth_dataset |
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model-index: |
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- name: rule_learning_margin_1mm_spanpred_attention |
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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_attention |
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This model is a fine-tuned version of [enoriega/rule_softmatching](https://huggingface.co/enoriega/rule_softmatching) on the enoriega/odinsynth_dataset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3237 |
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- Margin Accuracy: 0.8518 |
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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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| 0.5768 | 0.16 | 20 | 0.5693 | 0.7577 | |
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| 0.4593 | 0.32 | 40 | 0.4338 | 0.8105 | |
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| 0.4219 | 0.48 | 60 | 0.3958 | 0.8218 | |
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| 0.3953 | 0.64 | 80 | 0.3809 | 0.8308 | |
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| 0.383 | 0.8 | 100 | 0.3684 | 0.8355 | |
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| 0.3781 | 0.96 | 120 | 0.3591 | 0.8396 | |
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| 0.354 | 1.12 | 140 | 0.3535 | 0.8420 | |
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| 0.3521 | 1.28 | 160 | 0.3491 | 0.8430 | |
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| 0.3533 | 1.44 | 180 | 0.3423 | 0.8466 | |
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| 0.344 | 1.6 | 200 | 0.3372 | 0.8472 | |
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| 0.3352 | 1.76 | 220 | 0.3345 | 0.8478 | |
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| 0.3318 | 1.92 | 240 | 0.3320 | 0.8487 | |
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| 0.3478 | 2.08 | 260 | 0.3286 | 0.8494 | |
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| 0.3329 | 2.24 | 280 | 0.3286 | 0.8505 | |
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| 0.3424 | 2.4 | 300 | 0.3262 | 0.8506 | |
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| 0.3463 | 2.56 | 320 | 0.3264 | 0.8512 | |
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| 0.3416 | 2.72 | 340 | 0.3247 | 0.8518 | |
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| 0.329 | 2.88 | 360 | 0.3247 | 0.8516 | |
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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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