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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_3mm_many_negatives_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_3mm_many_negatives_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 None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2192
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- Margin Accuracy: 0.8968
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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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| 0.3149 | 0.16 | 60 | 0.3098 | 0.8608 |
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| 0.2754 | 0.32 | 120 | 0.2725 | 0.8733 |
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| 0.2619 | 0.48 | 180 | 0.2512 | 0.8872 |
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| 0.2378 | 0.64 | 240 | 0.2391 | 0.8925 |
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| 0.2451 | 0.8 | 300 | 0.2305 | 0.8943 |
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| 0.2357 | 0.96 | 360 | 0.2292 | 0.8949 |
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| 0.2335 | 1.12 | 420 | 0.2269 | 0.8952 |
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| 0.2403 | 1.28 | 480 | 0.2213 | 0.8957 |
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| 0.2302 | 1.44 | 540 | 0.2227 | 0.8963 |
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| 0.2353 | 1.6 | 600 | 0.2222 | 0.8961 |
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| 0.2271 | 1.76 | 660 | 0.2207 | 0.8964 |
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| 0.228 | 1.92 | 720 | 0.2218 | 0.8967 |
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| 0.2231 | 2.08 | 780 | 0.2201 | 0.8967 |
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| 0.2128 | 2.24 | 840 | 0.2219 | 0.8967 |
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| 0.2186 | 2.4 | 900 | 0.2202 | 0.8967 |
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| 0.2245 | 2.56 | 960 | 0.2205 | 0.8969 |
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| 0.2158 | 2.72 | 1020 | 0.2196 | 0.8969 |
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| 0.2106 | 2.88 | 1080 | 0.2192 | 0.8968 |
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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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