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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-16-87 |
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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-16-87 |
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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: 0.0012 |
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- Accuracy: 1.0 |
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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 | 0.3437 | 0.875 | |
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| No log | 2.0 | 2 | 0.3444 | 0.875 | |
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| No log | 3.0 | 3 | 0.3459 | 0.875 | |
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| No log | 4.0 | 4 | 0.3481 | 0.875 | |
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| No log | 5.0 | 5 | 0.3509 | 0.875 | |
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| No log | 6.0 | 6 | 0.3542 | 0.875 | |
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| No log | 7.0 | 7 | 0.3577 | 0.875 | |
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| No log | 8.0 | 8 | 0.3605 | 0.875 | |
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| No log | 9.0 | 9 | 0.3609 | 0.875 | |
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| 1.0043 | 10.0 | 10 | 0.3571 | 0.875 | |
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| 1.0043 | 11.0 | 11 | 0.3490 | 0.875 | |
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| 1.0043 | 12.0 | 12 | 0.3367 | 0.875 | |
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| 1.0043 | 13.0 | 13 | 0.3202 | 0.875 | |
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| 1.0043 | 14.0 | 14 | 0.2996 | 0.875 | |
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| 1.0043 | 15.0 | 15 | 0.2751 | 0.875 | |
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| 1.0043 | 16.0 | 16 | 0.2470 | 0.9375 | |
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| 1.0043 | 17.0 | 17 | 0.2159 | 0.9375 | |
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| 1.0043 | 18.0 | 18 | 0.1832 | 0.9375 | |
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| 1.0043 | 19.0 | 19 | 0.1516 | 0.9375 | |
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| 0.6554 | 20.0 | 20 | 0.1241 | 0.9688 | |
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| 0.6554 | 21.0 | 21 | 0.1018 | 0.9688 | |
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| 0.6554 | 22.0 | 22 | 0.0818 | 0.9688 | |
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| 0.6554 | 23.0 | 23 | 0.0611 | 0.9688 | |
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| 0.6554 | 24.0 | 24 | 0.0378 | 0.9688 | |
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| 0.6554 | 25.0 | 25 | 0.0170 | 1.0 | |
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| 0.6554 | 26.0 | 26 | 0.0093 | 1.0 | |
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| 0.6554 | 27.0 | 27 | 0.0077 | 1.0 | |
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| 0.6554 | 28.0 | 28 | 0.0073 | 1.0 | |
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| 0.6554 | 29.0 | 29 | 0.0072 | 1.0 | |
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| 0.1962 | 30.0 | 30 | 0.0072 | 1.0 | |
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| 0.1962 | 31.0 | 31 | 0.0071 | 1.0 | |
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| 0.1962 | 32.0 | 32 | 0.0070 | 1.0 | |
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| 0.1962 | 33.0 | 33 | 0.0069 | 1.0 | |
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| 0.1962 | 34.0 | 34 | 0.0068 | 1.0 | |
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| 0.1962 | 35.0 | 35 | 0.0067 | 1.0 | |
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| 0.1962 | 36.0 | 36 | 0.0065 | 1.0 | |
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| 0.1962 | 37.0 | 37 | 0.0063 | 1.0 | |
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| 0.1962 | 38.0 | 38 | 0.0060 | 1.0 | |
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| 0.1962 | 39.0 | 39 | 0.0058 | 1.0 | |
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| 0.0075 | 40.0 | 40 | 0.0056 | 1.0 | |
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| 0.0075 | 41.0 | 41 | 0.0053 | 1.0 | |
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| 0.0075 | 42.0 | 42 | 0.0051 | 1.0 | |
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| 0.0075 | 43.0 | 43 | 0.0050 | 1.0 | |
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| 0.0075 | 44.0 | 44 | 0.0048 | 1.0 | |
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| 0.0075 | 45.0 | 45 | 0.0046 | 1.0 | |
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| 0.0075 | 46.0 | 46 | 0.0045 | 1.0 | |
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| 0.0075 | 47.0 | 47 | 0.0043 | 1.0 | |
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| 0.0075 | 48.0 | 48 | 0.0042 | 1.0 | |
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| 0.0075 | 49.0 | 49 | 0.0041 | 1.0 | |
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| 0.0019 | 50.0 | 50 | 0.0040 | 1.0 | |
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| 0.0019 | 51.0 | 51 | 0.0039 | 1.0 | |
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| 0.0019 | 52.0 | 52 | 0.0038 | 1.0 | |
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| 0.0019 | 53.0 | 53 | 0.0037 | 1.0 | |
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| 0.0019 | 54.0 | 54 | 0.0036 | 1.0 | |
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| 0.0019 | 55.0 | 55 | 0.0035 | 1.0 | |
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| 0.0019 | 56.0 | 56 | 0.0035 | 1.0 | |
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| 0.0019 | 57.0 | 57 | 0.0034 | 1.0 | |
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| 0.0019 | 58.0 | 58 | 0.0033 | 1.0 | |
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| 0.0019 | 59.0 | 59 | 0.0033 | 1.0 | |
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| 0.0014 | 60.0 | 60 | 0.0032 | 1.0 | |
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| 0.0014 | 61.0 | 61 | 0.0032 | 1.0 | |
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| 0.0014 | 62.0 | 62 | 0.0031 | 1.0 | |
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| 0.0014 | 63.0 | 63 | 0.0031 | 1.0 | |
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| 0.0014 | 64.0 | 64 | 0.0030 | 1.0 | |
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| 0.0014 | 65.0 | 65 | 0.0030 | 1.0 | |
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| 0.0014 | 66.0 | 66 | 0.0029 | 1.0 | |
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| 0.0014 | 67.0 | 67 | 0.0029 | 1.0 | |
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| 0.0014 | 68.0 | 68 | 0.0029 | 1.0 | |
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| 0.0014 | 69.0 | 69 | 0.0028 | 1.0 | |
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| 0.0011 | 70.0 | 70 | 0.0028 | 1.0 | |
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| 0.0011 | 71.0 | 71 | 0.0028 | 1.0 | |
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| 0.0011 | 72.0 | 72 | 0.0027 | 1.0 | |
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| 0.0011 | 73.0 | 73 | 0.0027 | 1.0 | |
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| 0.0011 | 74.0 | 74 | 0.0027 | 1.0 | |
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| 0.0011 | 75.0 | 75 | 0.0026 | 1.0 | |
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| 0.0011 | 76.0 | 76 | 0.0026 | 1.0 | |
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| 0.0011 | 77.0 | 77 | 0.0026 | 1.0 | |
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| 0.0011 | 78.0 | 78 | 0.0026 | 1.0 | |
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| 0.0011 | 79.0 | 79 | 0.0025 | 1.0 | |
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| 0.0009 | 80.0 | 80 | 0.0025 | 1.0 | |
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| 0.0009 | 81.0 | 81 | 0.0025 | 1.0 | |
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| 0.0009 | 82.0 | 82 | 0.0024 | 1.0 | |
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| 0.0009 | 83.0 | 83 | 0.0024 | 1.0 | |
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| 0.0009 | 84.0 | 84 | 0.0024 | 1.0 | |
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| 0.0009 | 85.0 | 85 | 0.0023 | 1.0 | |
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| 0.0009 | 86.0 | 86 | 0.0023 | 1.0 | |
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| 0.0009 | 87.0 | 87 | 0.0023 | 1.0 | |
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| 0.0009 | 88.0 | 88 | 0.0022 | 1.0 | |
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| 0.0009 | 89.0 | 89 | 0.0022 | 1.0 | |
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| 0.0008 | 90.0 | 90 | 0.0022 | 1.0 | |
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| 0.0008 | 91.0 | 91 | 0.0021 | 1.0 | |
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| 0.0008 | 92.0 | 92 | 0.0021 | 1.0 | |
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| 0.0008 | 93.0 | 93 | 0.0021 | 1.0 | |
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| 0.0008 | 94.0 | 94 | 0.0020 | 1.0 | |
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| 0.0008 | 95.0 | 95 | 0.0020 | 1.0 | |
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| 0.0008 | 96.0 | 96 | 0.0020 | 1.0 | |
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| 0.0008 | 97.0 | 97 | 0.0019 | 1.0 | |
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| 0.0008 | 98.0 | 98 | 0.0019 | 1.0 | |
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| 0.0008 | 99.0 | 99 | 0.0019 | 1.0 | |
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| 0.0007 | 100.0 | 100 | 0.0019 | 1.0 | |
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| 0.0007 | 101.0 | 101 | 0.0018 | 1.0 | |
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| 0.0007 | 102.0 | 102 | 0.0018 | 1.0 | |
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| 0.0007 | 103.0 | 103 | 0.0018 | 1.0 | |
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| 0.0007 | 104.0 | 104 | 0.0018 | 1.0 | |
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| 0.0007 | 105.0 | 105 | 0.0018 | 1.0 | |
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| 0.0007 | 106.0 | 106 | 0.0017 | 1.0 | |
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| 0.0007 | 107.0 | 107 | 0.0017 | 1.0 | |
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| 0.0007 | 108.0 | 108 | 0.0017 | 1.0 | |
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| 0.0007 | 109.0 | 109 | 0.0017 | 1.0 | |
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| 0.0006 | 110.0 | 110 | 0.0017 | 1.0 | |
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| 0.0006 | 111.0 | 111 | 0.0016 | 1.0 | |
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| 0.0006 | 112.0 | 112 | 0.0016 | 1.0 | |
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| 0.0006 | 113.0 | 113 | 0.0016 | 1.0 | |
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| 0.0006 | 114.0 | 114 | 0.0016 | 1.0 | |
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| 0.0006 | 115.0 | 115 | 0.0016 | 1.0 | |
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| 0.0006 | 116.0 | 116 | 0.0016 | 1.0 | |
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| 0.0006 | 117.0 | 117 | 0.0015 | 1.0 | |
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| 0.0006 | 118.0 | 118 | 0.0015 | 1.0 | |
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| 0.0006 | 119.0 | 119 | 0.0015 | 1.0 | |
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| 0.0005 | 120.0 | 120 | 0.0015 | 1.0 | |
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| 0.0005 | 121.0 | 121 | 0.0015 | 1.0 | |
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| 0.0005 | 122.0 | 122 | 0.0015 | 1.0 | |
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| 0.0005 | 123.0 | 123 | 0.0015 | 1.0 | |
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| 0.0005 | 124.0 | 124 | 0.0015 | 1.0 | |
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| 0.0005 | 125.0 | 125 | 0.0014 | 1.0 | |
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| 0.0005 | 126.0 | 126 | 0.0014 | 1.0 | |
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| 0.0005 | 127.0 | 127 | 0.0014 | 1.0 | |
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| 0.0005 | 128.0 | 128 | 0.0014 | 1.0 | |
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| 0.0005 | 129.0 | 129 | 0.0014 | 1.0 | |
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| 0.0005 | 130.0 | 130 | 0.0014 | 1.0 | |
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| 0.0005 | 131.0 | 131 | 0.0014 | 1.0 | |
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| 0.0005 | 132.0 | 132 | 0.0014 | 1.0 | |
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| 0.0005 | 133.0 | 133 | 0.0014 | 1.0 | |
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| 0.0005 | 134.0 | 134 | 0.0014 | 1.0 | |
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| 0.0005 | 135.0 | 135 | 0.0013 | 1.0 | |
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| 0.0005 | 136.0 | 136 | 0.0013 | 1.0 | |
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| 0.0005 | 137.0 | 137 | 0.0013 | 1.0 | |
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| 0.0005 | 138.0 | 138 | 0.0013 | 1.0 | |
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| 0.0005 | 139.0 | 139 | 0.0013 | 1.0 | |
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| 0.0004 | 140.0 | 140 | 0.0013 | 1.0 | |
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| 0.0004 | 141.0 | 141 | 0.0013 | 1.0 | |
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| 0.0004 | 142.0 | 142 | 0.0013 | 1.0 | |
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| 0.0004 | 143.0 | 143 | 0.0013 | 1.0 | |
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| 0.0004 | 144.0 | 144 | 0.0013 | 1.0 | |
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| 0.0004 | 145.0 | 145 | 0.0013 | 1.0 | |
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| 0.0004 | 146.0 | 146 | 0.0013 | 1.0 | |
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| 0.0004 | 147.0 | 147 | 0.0013 | 1.0 | |
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| 0.0004 | 148.0 | 148 | 0.0012 | 1.0 | |
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| 0.0004 | 149.0 | 149 | 0.0012 | 1.0 | |
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| 0.0004 | 150.0 | 150 | 0.0012 | 1.0 | |
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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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