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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-32-21 |
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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-32-21 |
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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: 0.8940 |
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- Accuracy: 0.875 |
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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 | 2 | 0.8845 | 0.875 | |
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| No log | 2.0 | 4 | 0.8817 | 0.875 | |
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| No log | 3.0 | 6 | 0.8770 | 0.875 | |
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| No log | 4.0 | 8 | 0.8735 | 0.875 | |
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| 0.4208 | 5.0 | 10 | 0.8676 | 0.875 | |
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| 0.4208 | 6.0 | 12 | 0.8661 | 0.875 | |
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| 0.4208 | 7.0 | 14 | 0.8671 | 0.875 | |
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| 0.4208 | 8.0 | 16 | 0.8603 | 0.875 | |
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| 0.4208 | 9.0 | 18 | 0.8539 | 0.875 | |
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| 0.3008 | 10.0 | 20 | 0.8486 | 0.875 | |
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| 0.3008 | 11.0 | 22 | 0.8322 | 0.875 | |
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| 0.3008 | 12.0 | 24 | 0.8044 | 0.875 | |
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| 0.3008 | 13.0 | 26 | 0.7829 | 0.875 | |
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| 0.3008 | 14.0 | 28 | 0.7727 | 0.875 | |
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| 0.1225 | 15.0 | 30 | 0.7704 | 0.875 | |
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| 0.1225 | 16.0 | 32 | 0.7792 | 0.8594 | |
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| 0.1225 | 17.0 | 34 | 0.7959 | 0.8594 | |
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| 0.1225 | 18.0 | 36 | 0.8441 | 0.8594 | |
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| 0.1225 | 19.0 | 38 | 0.8519 | 0.8594 | |
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| 0.0141 | 20.0 | 40 | 0.8216 | 0.8594 | |
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| 0.0141 | 21.0 | 42 | 0.7810 | 0.875 | |
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| 0.0141 | 22.0 | 44 | 0.7611 | 0.875 | |
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| 0.0141 | 23.0 | 46 | 0.7566 | 0.875 | |
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| 0.0141 | 24.0 | 48 | 0.7634 | 0.875 | |
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| 0.0011 | 25.0 | 50 | 0.7747 | 0.875 | |
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| 0.0011 | 26.0 | 52 | 0.7894 | 0.8594 | |
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| 0.0011 | 27.0 | 54 | 0.8063 | 0.8594 | |
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| 0.0011 | 28.0 | 56 | 0.8136 | 0.8594 | |
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| 0.0011 | 29.0 | 58 | 0.8142 | 0.8594 | |
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| 0.0003 | 30.0 | 60 | 0.8096 | 0.8594 | |
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| 0.0003 | 31.0 | 62 | 0.8001 | 0.8594 | |
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| 0.0003 | 32.0 | 64 | 0.7901 | 0.8594 | |
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| 0.0003 | 33.0 | 66 | 0.7819 | 0.875 | |
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| 0.0003 | 34.0 | 68 | 0.7763 | 0.875 | |
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| 0.0002 | 35.0 | 70 | 0.7729 | 0.875 | |
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| 0.0002 | 36.0 | 72 | 0.7707 | 0.875 | |
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| 0.0002 | 37.0 | 74 | 0.7693 | 0.875 | |
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| 0.0002 | 38.0 | 76 | 0.7684 | 0.875 | |
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| 0.0002 | 39.0 | 78 | 0.7684 | 0.875 | |
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| 0.0002 | 40.0 | 80 | 0.7686 | 0.875 | |
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| 0.0002 | 41.0 | 82 | 0.7692 | 0.875 | |
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| 0.0002 | 42.0 | 84 | 0.7701 | 0.875 | |
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| 0.0002 | 43.0 | 86 | 0.7712 | 0.875 | |
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| 0.0002 | 44.0 | 88 | 0.7726 | 0.875 | |
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| 0.0002 | 45.0 | 90 | 0.7741 | 0.875 | |
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| 0.0002 | 46.0 | 92 | 0.7758 | 0.875 | |
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| 0.0002 | 47.0 | 94 | 0.7778 | 0.875 | |
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| 0.0002 | 48.0 | 96 | 0.7796 | 0.875 | |
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| 0.0002 | 49.0 | 98 | 0.7815 | 0.875 | |
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| 0.0001 | 50.0 | 100 | 0.7835 | 0.875 | |
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| 0.0001 | 51.0 | 102 | 0.7855 | 0.875 | |
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| 0.0001 | 52.0 | 104 | 0.7872 | 0.875 | |
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| 0.0001 | 53.0 | 106 | 0.7888 | 0.875 | |
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| 0.0001 | 54.0 | 108 | 0.7905 | 0.875 | |
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| 0.0001 | 55.0 | 110 | 0.7922 | 0.875 | |
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| 0.0001 | 56.0 | 112 | 0.7938 | 0.875 | |
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| 0.0001 | 57.0 | 114 | 0.7954 | 0.875 | |
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| 0.0001 | 58.0 | 116 | 0.7969 | 0.875 | |
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| 0.0001 | 59.0 | 118 | 0.7982 | 0.875 | |
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| 0.0001 | 60.0 | 120 | 0.7995 | 0.875 | |
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| 0.0001 | 61.0 | 122 | 0.8007 | 0.875 | |
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| 0.0001 | 62.0 | 124 | 0.8020 | 0.875 | |
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| 0.0001 | 63.0 | 126 | 0.8031 | 0.875 | |
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| 0.0001 | 64.0 | 128 | 0.8041 | 0.875 | |
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| 0.0001 | 65.0 | 130 | 0.8052 | 0.875 | |
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| 0.0001 | 66.0 | 132 | 0.8063 | 0.875 | |
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| 0.0001 | 67.0 | 134 | 0.8073 | 0.875 | |
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| 0.0001 | 68.0 | 136 | 0.8084 | 0.875 | |
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| 0.0001 | 69.0 | 138 | 0.8095 | 0.875 | |
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| 0.0001 | 70.0 | 140 | 0.8104 | 0.875 | |
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| 0.0001 | 71.0 | 142 | 0.8115 | 0.875 | |
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| 0.0001 | 72.0 | 144 | 0.8125 | 0.875 | |
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| 0.0001 | 73.0 | 146 | 0.8135 | 0.875 | |
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| 0.0001 | 74.0 | 148 | 0.8143 | 0.875 | |
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| 0.0001 | 75.0 | 150 | 0.8151 | 0.875 | |
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| 0.0001 | 76.0 | 152 | 0.8159 | 0.875 | |
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| 0.0001 | 77.0 | 154 | 0.8167 | 0.875 | |
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| 0.0001 | 78.0 | 156 | 0.8176 | 0.875 | |
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| 0.0001 | 79.0 | 158 | 0.8187 | 0.875 | |
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| 0.0001 | 80.0 | 160 | 0.8198 | 0.875 | |
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| 0.0001 | 81.0 | 162 | 0.8210 | 0.875 | |
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| 0.0001 | 82.0 | 164 | 0.8222 | 0.875 | |
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| 0.0001 | 83.0 | 166 | 0.8232 | 0.875 | |
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| 0.0001 | 84.0 | 168 | 0.8243 | 0.875 | |
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| 0.0001 | 85.0 | 170 | 0.8254 | 0.875 | |
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| 0.0001 | 86.0 | 172 | 0.8266 | 0.875 | |
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| 0.0001 | 87.0 | 174 | 0.8278 | 0.875 | |
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| 0.0001 | 88.0 | 176 | 0.8290 | 0.875 | |
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| 0.0001 | 89.0 | 178 | 0.8302 | 0.875 | |
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| 0.0001 | 90.0 | 180 | 0.8314 | 0.875 | |
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| 0.0001 | 91.0 | 182 | 0.8326 | 0.875 | |
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| 0.0001 | 92.0 | 184 | 0.8337 | 0.875 | |
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| 0.0001 | 93.0 | 186 | 0.8347 | 0.875 | |
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| 0.0001 | 94.0 | 188 | 0.8358 | 0.875 | |
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| 0.0001 | 95.0 | 190 | 0.8369 | 0.875 | |
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| 0.0001 | 96.0 | 192 | 0.8379 | 0.875 | |
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| 0.0001 | 97.0 | 194 | 0.8390 | 0.875 | |
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| 0.0001 | 98.0 | 196 | 0.8401 | 0.875 | |
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| 0.0001 | 99.0 | 198 | 0.8411 | 0.875 | |
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| 0.0001 | 100.0 | 200 | 0.8421 | 0.875 | |
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| 0.0001 | 101.0 | 202 | 0.8431 | 0.875 | |
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| 0.0001 | 102.0 | 204 | 0.8442 | 0.875 | |
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| 0.0001 | 103.0 | 206 | 0.8454 | 0.875 | |
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| 0.0001 | 104.0 | 208 | 0.8464 | 0.875 | |
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| 0.0001 | 105.0 | 210 | 0.8475 | 0.875 | |
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| 0.0001 | 106.0 | 212 | 0.8486 | 0.875 | |
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| 0.0001 | 107.0 | 214 | 0.8498 | 0.875 | |
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| 0.0001 | 108.0 | 216 | 0.8510 | 0.875 | |
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| 0.0001 | 109.0 | 218 | 0.8520 | 0.875 | |
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| 0.0001 | 110.0 | 220 | 0.8532 | 0.875 | |
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| 0.0001 | 111.0 | 222 | 0.8544 | 0.875 | |
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| 0.0001 | 112.0 | 224 | 0.8556 | 0.875 | |
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| 0.0001 | 113.0 | 226 | 0.8568 | 0.875 | |
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| 0.0001 | 114.0 | 228 | 0.8580 | 0.875 | |
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| 0.0 | 115.0 | 230 | 0.8591 | 0.875 | |
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| 0.0 | 116.0 | 232 | 0.8601 | 0.875 | |
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| 0.0 | 117.0 | 234 | 0.8612 | 0.875 | |
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| 0.0 | 118.0 | 236 | 0.8623 | 0.875 | |
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| 0.0 | 119.0 | 238 | 0.8633 | 0.875 | |
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| 0.0 | 120.0 | 240 | 0.8643 | 0.875 | |
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| 0.0 | 121.0 | 242 | 0.8652 | 0.875 | |
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| 0.0 | 122.0 | 244 | 0.8662 | 0.875 | |
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| 0.0 | 123.0 | 246 | 0.8671 | 0.875 | |
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| 0.0 | 124.0 | 248 | 0.8680 | 0.875 | |
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| 0.0 | 125.0 | 250 | 0.8689 | 0.875 | |
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| 0.0 | 126.0 | 252 | 0.8699 | 0.875 | |
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| 0.0 | 127.0 | 254 | 0.8708 | 0.875 | |
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| 0.0 | 128.0 | 256 | 0.8717 | 0.875 | |
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| 0.0 | 129.0 | 258 | 0.8727 | 0.875 | |
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| 0.0 | 130.0 | 260 | 0.8736 | 0.875 | |
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| 0.0 | 131.0 | 262 | 0.8746 | 0.875 | |
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| 0.0 | 132.0 | 264 | 0.8755 | 0.875 | |
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| 0.0 | 133.0 | 266 | 0.8764 | 0.875 | |
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| 0.0 | 134.0 | 268 | 0.8774 | 0.875 | |
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| 0.0 | 135.0 | 270 | 0.8784 | 0.875 | |
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| 0.0 | 136.0 | 272 | 0.8794 | 0.875 | |
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| 0.0 | 137.0 | 274 | 0.8803 | 0.875 | |
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| 0.0 | 138.0 | 276 | 0.8814 | 0.875 | |
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| 0.0 | 139.0 | 278 | 0.8825 | 0.875 | |
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| 0.0 | 140.0 | 280 | 0.8835 | 0.875 | |
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| 0.0 | 141.0 | 282 | 0.8846 | 0.875 | |
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| 0.0 | 142.0 | 284 | 0.8857 | 0.875 | |
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| 0.0 | 143.0 | 286 | 0.8869 | 0.875 | |
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| 0.0 | 144.0 | 288 | 0.8880 | 0.875 | |
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| 0.0 | 145.0 | 290 | 0.8890 | 0.875 | |
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| 0.0 | 146.0 | 292 | 0.8900 | 0.875 | |
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| 0.0 | 147.0 | 294 | 0.8911 | 0.875 | |
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| 0.0 | 148.0 | 296 | 0.8921 | 0.875 | |
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| 0.0 | 149.0 | 298 | 0.8931 | 0.875 | |
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| 0.0 | 150.0 | 300 | 0.8940 | 0.875 | |
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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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