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update model card 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_avf
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+ results: []
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+ ---
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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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+
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+ # rule_learning_1mm_many_negatives_spanpred_avf
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+
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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: 0.0732
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:-----:|:----:|:---------------:|
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+ | 0.1215 | 0.16 | 20 | 0.1191 |
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+ | 0.1091 | 0.32 | 40 | 0.1079 |
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+ | 0.0993 | 0.48 | 60 | 0.0993 |
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+ | 0.0938 | 0.64 | 80 | 0.0952 |
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+ | 0.085 | 0.8 | 100 | 0.0858 |
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+ | 0.0837 | 0.96 | 120 | 0.0842 |
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+ | 0.0811 | 1.12 | 140 | 0.0827 |
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+ | 0.0799 | 1.28 | 160 | 0.0809 |
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+ | 0.078 | 1.44 | 180 | 0.0786 |
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+ | 0.0792 | 1.6 | 200 | 0.0781 |
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+ | 0.0797 | 1.76 | 220 | 0.0765 |
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+ | 0.0775 | 1.92 | 240 | 0.0758 |
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+ | 0.0735 | 2.08 | 260 | 0.0748 |
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+ | 0.0704 | 2.24 | 280 | 0.0744 |
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+ | 0.0744 | 2.4 | 300 | 0.0737 |
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+ | 0.0752 | 2.56 | 320 | 0.0733 |
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+ | 0.075 | 2.72 | 340 | 0.0738 |
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+ | 0.0701 | 2.88 | 360 | 0.0732 |
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+
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+
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+ ### Framework versions
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+
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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