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--- |
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license: apache-2.0 |
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base_model: albert-base-v2 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: best_model-yelp_polarity-16-21 |
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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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# best_model-yelp_polarity-16-21 |
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This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8234 |
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- Accuracy: 0.75 |
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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: 1e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 150 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 1.0 | 1 | 0.8218 | 0.625 | |
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| No log | 2.0 | 2 | 0.8206 | 0.625 | |
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| No log | 3.0 | 3 | 0.8183 | 0.625 | |
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| No log | 4.0 | 4 | 0.8150 | 0.625 | |
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| No log | 5.0 | 5 | 0.8107 | 0.625 | |
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| No log | 6.0 | 6 | 0.8057 | 0.625 | |
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| No log | 7.0 | 7 | 0.8001 | 0.6562 | |
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| No log | 8.0 | 8 | 0.7944 | 0.6875 | |
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| No log | 9.0 | 9 | 0.7887 | 0.7188 | |
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| 0.5647 | 10.0 | 10 | 0.7834 | 0.7188 | |
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| 0.5647 | 11.0 | 11 | 0.7784 | 0.7188 | |
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| 0.5647 | 12.0 | 12 | 0.7738 | 0.7188 | |
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| 0.5647 | 13.0 | 13 | 0.7695 | 0.7188 | |
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| 0.5647 | 14.0 | 14 | 0.7651 | 0.7188 | |
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| 0.5647 | 15.0 | 15 | 0.7606 | 0.7188 | |
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| 0.5647 | 16.0 | 16 | 0.7558 | 0.7188 | |
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| 0.5647 | 17.0 | 17 | 0.7506 | 0.7188 | |
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| 0.5647 | 18.0 | 18 | 0.7451 | 0.7188 | |
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| 0.5647 | 19.0 | 19 | 0.7392 | 0.7188 | |
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| 0.472 | 20.0 | 20 | 0.7329 | 0.7188 | |
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| 0.472 | 21.0 | 21 | 0.7262 | 0.7188 | |
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| 0.472 | 22.0 | 22 | 0.7190 | 0.7188 | |
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| 0.472 | 23.0 | 23 | 0.7112 | 0.75 | |
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| 0.472 | 24.0 | 24 | 0.7029 | 0.75 | |
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| 0.472 | 25.0 | 25 | 0.6941 | 0.75 | |
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| 0.472 | 26.0 | 26 | 0.6847 | 0.75 | |
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| 0.472 | 27.0 | 27 | 0.6749 | 0.75 | |
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| 0.472 | 28.0 | 28 | 0.6647 | 0.75 | |
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| 0.472 | 29.0 | 29 | 0.6545 | 0.75 | |
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| 0.3267 | 30.0 | 30 | 0.6445 | 0.75 | |
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| 0.3267 | 31.0 | 31 | 0.6350 | 0.6562 | |
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| 0.3267 | 32.0 | 32 | 0.6261 | 0.6562 | |
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| 0.3267 | 33.0 | 33 | 0.6177 | 0.6875 | |
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| 0.3267 | 34.0 | 34 | 0.6100 | 0.6875 | |
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| 0.3267 | 35.0 | 35 | 0.6031 | 0.6875 | |
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| 0.3267 | 36.0 | 36 | 0.5973 | 0.6875 | |
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| 0.3267 | 37.0 | 37 | 0.5926 | 0.7188 | |
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| 0.3267 | 38.0 | 38 | 0.5895 | 0.7188 | |
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| 0.3267 | 39.0 | 39 | 0.5869 | 0.7188 | |
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| 0.1824 | 40.0 | 40 | 0.5842 | 0.75 | |
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| 0.1824 | 41.0 | 41 | 0.5796 | 0.75 | |
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| 0.1824 | 42.0 | 42 | 0.5730 | 0.75 | |
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| 0.1824 | 43.0 | 43 | 0.5651 | 0.75 | |
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| 0.1824 | 44.0 | 44 | 0.5555 | 0.75 | |
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| 0.1824 | 45.0 | 45 | 0.5466 | 0.7812 | |
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| 0.1824 | 46.0 | 46 | 0.5408 | 0.7812 | |
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| 0.1824 | 47.0 | 47 | 0.5379 | 0.7812 | |
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| 0.1824 | 48.0 | 48 | 0.5386 | 0.7812 | |
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| 0.1824 | 49.0 | 49 | 0.5419 | 0.7812 | |
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| 0.0885 | 50.0 | 50 | 0.5482 | 0.7812 | |
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| 0.0885 | 51.0 | 51 | 0.5568 | 0.7812 | |
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| 0.0885 | 52.0 | 52 | 0.5662 | 0.7812 | |
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| 0.0885 | 53.0 | 53 | 0.5761 | 0.7812 | |
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| 0.0885 | 54.0 | 54 | 0.5834 | 0.7812 | |
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| 0.0885 | 55.0 | 55 | 0.5897 | 0.8125 | |
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| 0.0885 | 56.0 | 56 | 0.5929 | 0.8125 | |
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| 0.0885 | 57.0 | 57 | 0.5930 | 0.8125 | |
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| 0.0885 | 58.0 | 58 | 0.5905 | 0.7812 | |
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| 0.0885 | 59.0 | 59 | 0.5869 | 0.7812 | |
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| 0.0497 | 60.0 | 60 | 0.5830 | 0.7812 | |
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| 0.0497 | 61.0 | 61 | 0.5795 | 0.75 | |
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| 0.0497 | 62.0 | 62 | 0.5776 | 0.75 | |
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| 0.0497 | 63.0 | 63 | 0.5777 | 0.75 | |
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| 0.0497 | 64.0 | 64 | 0.5800 | 0.75 | |
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| 0.0497 | 65.0 | 65 | 0.5832 | 0.75 | |
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| 0.0497 | 66.0 | 66 | 0.5887 | 0.75 | |
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| 0.0497 | 67.0 | 67 | 0.5962 | 0.7812 | |
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| 0.0497 | 68.0 | 68 | 0.6062 | 0.7812 | |
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| 0.0497 | 69.0 | 69 | 0.6192 | 0.75 | |
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| 0.0306 | 70.0 | 70 | 0.6332 | 0.75 | |
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| 0.0306 | 71.0 | 71 | 0.6475 | 0.75 | |
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| 0.0306 | 72.0 | 72 | 0.6610 | 0.75 | |
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| 0.0306 | 73.0 | 73 | 0.6726 | 0.75 | |
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| 0.0306 | 74.0 | 74 | 0.6824 | 0.75 | |
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| 0.0306 | 75.0 | 75 | 0.6910 | 0.75 | |
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| 0.0306 | 76.0 | 76 | 0.6989 | 0.75 | |
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| 0.0306 | 77.0 | 77 | 0.7058 | 0.75 | |
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| 0.0306 | 78.0 | 78 | 0.7122 | 0.75 | |
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| 0.0306 | 79.0 | 79 | 0.7179 | 0.7188 | |
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| 0.0175 | 80.0 | 80 | 0.7230 | 0.7188 | |
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| 0.0175 | 81.0 | 81 | 0.7281 | 0.7188 | |
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| 0.0175 | 82.0 | 82 | 0.7331 | 0.7188 | |
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| 0.0175 | 83.0 | 83 | 0.7385 | 0.7188 | |
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| 0.0175 | 84.0 | 84 | 0.7428 | 0.7188 | |
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| 0.0175 | 85.0 | 85 | 0.7462 | 0.7188 | |
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| 0.0175 | 86.0 | 86 | 0.7491 | 0.75 | |
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| 0.0175 | 87.0 | 87 | 0.7520 | 0.75 | |
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| 0.0175 | 88.0 | 88 | 0.7544 | 0.75 | |
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| 0.0175 | 89.0 | 89 | 0.7566 | 0.75 | |
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| 0.0111 | 90.0 | 90 | 0.7584 | 0.75 | |
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| 0.0111 | 91.0 | 91 | 0.7604 | 0.75 | |
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| 0.0111 | 92.0 | 92 | 0.7622 | 0.75 | |
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| 0.0111 | 93.0 | 93 | 0.7641 | 0.75 | |
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| 0.0111 | 94.0 | 94 | 0.7665 | 0.75 | |
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| 0.0111 | 95.0 | 95 | 0.7693 | 0.75 | |
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| 0.0111 | 96.0 | 96 | 0.7724 | 0.75 | |
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| 0.0111 | 97.0 | 97 | 0.7757 | 0.75 | |
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| 0.0111 | 98.0 | 98 | 0.7792 | 0.75 | |
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| 0.0111 | 99.0 | 99 | 0.7828 | 0.75 | |
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| 0.0078 | 100.0 | 100 | 0.7868 | 0.75 | |
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| 0.0078 | 101.0 | 101 | 0.7911 | 0.75 | |
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| 0.0078 | 102.0 | 102 | 0.7959 | 0.75 | |
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| 0.0078 | 103.0 | 103 | 0.8010 | 0.75 | |
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| 0.0078 | 104.0 | 104 | 0.8059 | 0.75 | |
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| 0.0078 | 105.0 | 105 | 0.8106 | 0.75 | |
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| 0.0078 | 106.0 | 106 | 0.8150 | 0.75 | |
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| 0.0078 | 107.0 | 107 | 0.8193 | 0.75 | |
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| 0.0078 | 108.0 | 108 | 0.8230 | 0.75 | |
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| 0.0078 | 109.0 | 109 | 0.8263 | 0.75 | |
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| 0.0061 | 110.0 | 110 | 0.8290 | 0.75 | |
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| 0.0061 | 111.0 | 111 | 0.8312 | 0.75 | |
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| 0.0061 | 112.0 | 112 | 0.8328 | 0.75 | |
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| 0.0061 | 113.0 | 113 | 0.8339 | 0.75 | |
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| 0.0061 | 114.0 | 114 | 0.8345 | 0.75 | |
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| 0.0061 | 115.0 | 115 | 0.8348 | 0.75 | |
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| 0.0061 | 116.0 | 116 | 0.8347 | 0.75 | |
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| 0.0061 | 117.0 | 117 | 0.8338 | 0.75 | |
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| 0.0061 | 118.0 | 118 | 0.8329 | 0.75 | |
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| 0.0061 | 119.0 | 119 | 0.8322 | 0.75 | |
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| 0.0048 | 120.0 | 120 | 0.8315 | 0.75 | |
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| 0.0048 | 121.0 | 121 | 0.8308 | 0.75 | |
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| 0.0048 | 122.0 | 122 | 0.8301 | 0.75 | |
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| 0.0048 | 123.0 | 123 | 0.8296 | 0.75 | |
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| 0.0048 | 124.0 | 124 | 0.8294 | 0.75 | |
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| 0.0048 | 125.0 | 125 | 0.8296 | 0.75 | |
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| 0.0048 | 126.0 | 126 | 0.8299 | 0.75 | |
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| 0.0048 | 127.0 | 127 | 0.8302 | 0.75 | |
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| 0.0048 | 128.0 | 128 | 0.8302 | 0.75 | |
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| 0.0048 | 129.0 | 129 | 0.8304 | 0.75 | |
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| 0.0039 | 130.0 | 130 | 0.8306 | 0.75 | |
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| 0.0039 | 131.0 | 131 | 0.8305 | 0.75 | |
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| 0.0039 | 132.0 | 132 | 0.8301 | 0.75 | |
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| 0.0039 | 133.0 | 133 | 0.8296 | 0.7812 | |
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| 0.0039 | 134.0 | 134 | 0.8292 | 0.7812 | |
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| 0.0039 | 135.0 | 135 | 0.8283 | 0.7812 | |
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| 0.0039 | 136.0 | 136 | 0.8272 | 0.7812 | |
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| 0.0039 | 137.0 | 137 | 0.8259 | 0.7812 | |
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| 0.0039 | 138.0 | 138 | 0.8247 | 0.7812 | |
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| 0.0039 | 139.0 | 139 | 0.8237 | 0.75 | |
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| 0.0032 | 140.0 | 140 | 0.8228 | 0.75 | |
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| 0.0032 | 141.0 | 141 | 0.8222 | 0.75 | |
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| 0.0032 | 142.0 | 142 | 0.8222 | 0.75 | |
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| 0.0032 | 143.0 | 143 | 0.8220 | 0.75 | |
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| 0.0032 | 144.0 | 144 | 0.8220 | 0.75 | |
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| 0.0032 | 145.0 | 145 | 0.8218 | 0.75 | |
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| 0.0032 | 146.0 | 146 | 0.8217 | 0.75 | |
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| 0.0032 | 147.0 | 147 | 0.8218 | 0.75 | |
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| 0.0032 | 148.0 | 148 | 0.8222 | 0.75 | |
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| 0.0032 | 149.0 | 149 | 0.8228 | 0.75 | |
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| 0.0028 | 150.0 | 150 | 0.8234 | 0.75 | |
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### Framework versions |
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- Transformers 4.32.0.dev0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.4.0 |
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- Tokenizers 0.13.3 |
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