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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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- sibyl |
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datasets: |
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- amazon_polarity |
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metrics: |
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- accuracy |
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model-index: |
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- name: bert-base-uncased-amazon_polarity |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: amazon_polarity |
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type: amazon_polarity |
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args: amazon_polarity |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.94647 |
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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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# bert-base-uncased-amazon_polarity |
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This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the amazon_polarity dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2945 |
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- Accuracy: 0.9465 |
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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: 1 |
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- eval_batch_size: 8 |
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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: 1782000 |
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- training_steps: 17820000 |
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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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| 0.7155 | 0.0 | 2000 | 0.7060 | 0.4622 | |
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| 0.7054 | 0.0 | 4000 | 0.6925 | 0.5165 | |
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| 0.6842 | 0.0 | 6000 | 0.6653 | 0.6116 | |
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| 0.6375 | 0.0 | 8000 | 0.5721 | 0.7909 | |
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| 0.4671 | 0.0 | 10000 | 0.3238 | 0.8770 | |
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| 0.3403 | 0.0 | 12000 | 0.3692 | 0.8861 | |
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| 0.4162 | 0.0 | 14000 | 0.4560 | 0.8908 | |
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| 0.4728 | 0.0 | 16000 | 0.5071 | 0.8980 | |
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| 0.5111 | 0.01 | 18000 | 0.5204 | 0.9015 | |
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| 0.4792 | 0.01 | 20000 | 0.5193 | 0.9076 | |
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| 0.544 | 0.01 | 22000 | 0.4835 | 0.9133 | |
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| 0.4745 | 0.01 | 24000 | 0.4689 | 0.9170 | |
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| 0.4403 | 0.01 | 26000 | 0.4778 | 0.9177 | |
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| 0.4405 | 0.01 | 28000 | 0.4754 | 0.9163 | |
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| 0.4375 | 0.01 | 30000 | 0.4808 | 0.9175 | |
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| 0.4628 | 0.01 | 32000 | 0.4340 | 0.9244 | |
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| 0.4488 | 0.01 | 34000 | 0.4162 | 0.9265 | |
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| 0.4608 | 0.01 | 36000 | 0.4031 | 0.9271 | |
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| 0.4478 | 0.01 | 38000 | 0.4502 | 0.9253 | |
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| 0.4237 | 0.01 | 40000 | 0.4087 | 0.9279 | |
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| 0.4601 | 0.01 | 42000 | 0.4133 | 0.9269 | |
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| 0.4153 | 0.01 | 44000 | 0.4230 | 0.9306 | |
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| 0.4096 | 0.01 | 46000 | 0.4108 | 0.9301 | |
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| 0.4348 | 0.01 | 48000 | 0.4138 | 0.9309 | |
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| 0.3787 | 0.01 | 50000 | 0.4066 | 0.9324 | |
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| 0.4172 | 0.01 | 52000 | 0.4812 | 0.9206 | |
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| 0.3897 | 0.02 | 54000 | 0.4013 | 0.9325 | |
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| 0.3787 | 0.02 | 56000 | 0.3837 | 0.9344 | |
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| 0.4253 | 0.02 | 58000 | 0.3925 | 0.9347 | |
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| 0.3959 | 0.02 | 60000 | 0.3907 | 0.9353 | |
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| 0.4402 | 0.02 | 62000 | 0.3708 | 0.9341 | |
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| 0.4115 | 0.02 | 64000 | 0.3477 | 0.9361 | |
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| 0.3876 | 0.02 | 66000 | 0.3634 | 0.9373 | |
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| 0.4286 | 0.02 | 68000 | 0.3778 | 0.9378 | |
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| 0.422 | 0.02 | 70000 | 0.3540 | 0.9361 | |
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| 0.3732 | 0.02 | 72000 | 0.3853 | 0.9378 | |
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| 0.3641 | 0.02 | 74000 | 0.3951 | 0.9386 | |
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| 0.3701 | 0.02 | 76000 | 0.3582 | 0.9388 | |
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| 0.4498 | 0.02 | 78000 | 0.3268 | 0.9375 | |
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| 0.3587 | 0.02 | 80000 | 0.3825 | 0.9401 | |
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| 0.4474 | 0.02 | 82000 | 0.3155 | 0.9391 | |
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| 0.3598 | 0.02 | 84000 | 0.3666 | 0.9388 | |
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| 0.389 | 0.02 | 86000 | 0.3745 | 0.9377 | |
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| 0.3625 | 0.02 | 88000 | 0.3776 | 0.9387 | |
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| 0.3511 | 0.03 | 90000 | 0.4275 | 0.9336 | |
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| 0.3428 | 0.03 | 92000 | 0.4301 | 0.9336 | |
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| 0.4042 | 0.03 | 94000 | 0.3547 | 0.9359 | |
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| 0.3583 | 0.03 | 96000 | 0.3763 | 0.9396 | |
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| 0.3887 | 0.03 | 98000 | 0.3213 | 0.9412 | |
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| 0.3915 | 0.03 | 100000 | 0.3557 | 0.9409 | |
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| 0.3378 | 0.03 | 102000 | 0.3627 | 0.9418 | |
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| 0.349 | 0.03 | 104000 | 0.3614 | 0.9402 | |
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| 0.3596 | 0.03 | 106000 | 0.3834 | 0.9381 | |
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| 0.3519 | 0.03 | 108000 | 0.3560 | 0.9421 | |
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| 0.3598 | 0.03 | 110000 | 0.3485 | 0.9419 | |
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| 0.3642 | 0.03 | 112000 | 0.3754 | 0.9395 | |
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| 0.3477 | 0.03 | 114000 | 0.3634 | 0.9426 | |
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| 0.4202 | 0.03 | 116000 | 0.3071 | 0.9427 | |
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| 0.3656 | 0.03 | 118000 | 0.3155 | 0.9441 | |
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| 0.3709 | 0.03 | 120000 | 0.2923 | 0.9433 | |
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| 0.374 | 0.03 | 122000 | 0.3272 | 0.9441 | |
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| 0.3142 | 0.03 | 124000 | 0.3348 | 0.9444 | |
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| 0.3452 | 0.04 | 126000 | 0.3603 | 0.9436 | |
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| 0.3365 | 0.04 | 128000 | 0.3339 | 0.9434 | |
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| 0.3353 | 0.04 | 130000 | 0.3471 | 0.9450 | |
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| 0.343 | 0.04 | 132000 | 0.3508 | 0.9418 | |
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| 0.3174 | 0.04 | 134000 | 0.3753 | 0.9436 | |
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| 0.3009 | 0.04 | 136000 | 0.3687 | 0.9422 | |
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| 0.3785 | 0.04 | 138000 | 0.3818 | 0.9396 | |
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| 0.3199 | 0.04 | 140000 | 0.3291 | 0.9438 | |
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| 0.4049 | 0.04 | 142000 | 0.3372 | 0.9454 | |
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| 0.3435 | 0.04 | 144000 | 0.3315 | 0.9459 | |
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| 0.3814 | 0.04 | 146000 | 0.3462 | 0.9401 | |
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| 0.359 | 0.04 | 148000 | 0.3981 | 0.9361 | |
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| 0.3552 | 0.04 | 150000 | 0.3226 | 0.9469 | |
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| 0.345 | 0.04 | 152000 | 0.3731 | 0.9384 | |
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| 0.3228 | 0.04 | 154000 | 0.2956 | 0.9471 | |
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| 0.3637 | 0.04 | 156000 | 0.2869 | 0.9477 | |
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| 0.349 | 0.04 | 158000 | 0.3331 | 0.9430 | |
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| 0.3374 | 0.04 | 160000 | 0.4159 | 0.9340 | |
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| 0.3718 | 0.05 | 162000 | 0.3241 | 0.9459 | |
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| 0.315 | 0.05 | 164000 | 0.3544 | 0.9391 | |
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| 0.3215 | 0.05 | 166000 | 0.3311 | 0.9451 | |
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| 0.3464 | 0.05 | 168000 | 0.3682 | 0.9453 | |
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| 0.3495 | 0.05 | 170000 | 0.3193 | 0.9469 | |
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| 0.305 | 0.05 | 172000 | 0.4132 | 0.9389 | |
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| 0.3479 | 0.05 | 174000 | 0.3465 | 0.947 | |
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| 0.3537 | 0.05 | 176000 | 0.3277 | 0.9449 | |
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### Framework versions |
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- Transformers 4.10.2 |
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- Pytorch 1.7.1 |
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- Datasets 1.12.1 |
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- Tokenizers 0.10.3 |
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