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---
license: apache-2.0
tags:
- generated_from_trainer
- sibyl
datasets:
- amazon_polarity
metrics:
- accuracy
model-index:
- name: bert-base-uncased-amazon_polarity
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_polarity
type: amazon_polarity
args: amazon_polarity
metrics:
- name: Accuracy
type: accuracy
value: 0.94647
- task:
type: text-classification
name: Text Classification
dataset:
name: amazon_polarity
type: amazon_polarity
config: amazon_polarity
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.9464875
verified: true
- name: Precision
type: precision
value: 0.9528844934702675
verified: true
- name: Recall
type: recall
value: 0.939425
verified: true
- name: AUC
type: auc
value: 0.9863499156250001
verified: true
- name: F1
type: f1
value: 0.9461068798388619
verified: true
- name: loss
type: loss
value: 0.2944573760032654
verified: true
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-amazon_polarity
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the amazon_polarity dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2945
- Accuracy: 0.9465
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1782000
- training_steps: 17820000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.7155 | 0.0 | 2000 | 0.7060 | 0.4622 |
| 0.7054 | 0.0 | 4000 | 0.6925 | 0.5165 |
| 0.6842 | 0.0 | 6000 | 0.6653 | 0.6116 |
| 0.6375 | 0.0 | 8000 | 0.5721 | 0.7909 |
| 0.4671 | 0.0 | 10000 | 0.3238 | 0.8770 |
| 0.3403 | 0.0 | 12000 | 0.3692 | 0.8861 |
| 0.4162 | 0.0 | 14000 | 0.4560 | 0.8908 |
| 0.4728 | 0.0 | 16000 | 0.5071 | 0.8980 |
| 0.5111 | 0.01 | 18000 | 0.5204 | 0.9015 |
| 0.4792 | 0.01 | 20000 | 0.5193 | 0.9076 |
| 0.544 | 0.01 | 22000 | 0.4835 | 0.9133 |
| 0.4745 | 0.01 | 24000 | 0.4689 | 0.9170 |
| 0.4403 | 0.01 | 26000 | 0.4778 | 0.9177 |
| 0.4405 | 0.01 | 28000 | 0.4754 | 0.9163 |
| 0.4375 | 0.01 | 30000 | 0.4808 | 0.9175 |
| 0.4628 | 0.01 | 32000 | 0.4340 | 0.9244 |
| 0.4488 | 0.01 | 34000 | 0.4162 | 0.9265 |
| 0.4608 | 0.01 | 36000 | 0.4031 | 0.9271 |
| 0.4478 | 0.01 | 38000 | 0.4502 | 0.9253 |
| 0.4237 | 0.01 | 40000 | 0.4087 | 0.9279 |
| 0.4601 | 0.01 | 42000 | 0.4133 | 0.9269 |
| 0.4153 | 0.01 | 44000 | 0.4230 | 0.9306 |
| 0.4096 | 0.01 | 46000 | 0.4108 | 0.9301 |
| 0.4348 | 0.01 | 48000 | 0.4138 | 0.9309 |
| 0.3787 | 0.01 | 50000 | 0.4066 | 0.9324 |
| 0.4172 | 0.01 | 52000 | 0.4812 | 0.9206 |
| 0.3897 | 0.02 | 54000 | 0.4013 | 0.9325 |
| 0.3787 | 0.02 | 56000 | 0.3837 | 0.9344 |
| 0.4253 | 0.02 | 58000 | 0.3925 | 0.9347 |
| 0.3959 | 0.02 | 60000 | 0.3907 | 0.9353 |
| 0.4402 | 0.02 | 62000 | 0.3708 | 0.9341 |
| 0.4115 | 0.02 | 64000 | 0.3477 | 0.9361 |
| 0.3876 | 0.02 | 66000 | 0.3634 | 0.9373 |
| 0.4286 | 0.02 | 68000 | 0.3778 | 0.9378 |
| 0.422 | 0.02 | 70000 | 0.3540 | 0.9361 |
| 0.3732 | 0.02 | 72000 | 0.3853 | 0.9378 |
| 0.3641 | 0.02 | 74000 | 0.3951 | 0.9386 |
| 0.3701 | 0.02 | 76000 | 0.3582 | 0.9388 |
| 0.4498 | 0.02 | 78000 | 0.3268 | 0.9375 |
| 0.3587 | 0.02 | 80000 | 0.3825 | 0.9401 |
| 0.4474 | 0.02 | 82000 | 0.3155 | 0.9391 |
| 0.3598 | 0.02 | 84000 | 0.3666 | 0.9388 |
| 0.389 | 0.02 | 86000 | 0.3745 | 0.9377 |
| 0.3625 | 0.02 | 88000 | 0.3776 | 0.9387 |
| 0.3511 | 0.03 | 90000 | 0.4275 | 0.9336 |
| 0.3428 | 0.03 | 92000 | 0.4301 | 0.9336 |
| 0.4042 | 0.03 | 94000 | 0.3547 | 0.9359 |
| 0.3583 | 0.03 | 96000 | 0.3763 | 0.9396 |
| 0.3887 | 0.03 | 98000 | 0.3213 | 0.9412 |
| 0.3915 | 0.03 | 100000 | 0.3557 | 0.9409 |
| 0.3378 | 0.03 | 102000 | 0.3627 | 0.9418 |
| 0.349 | 0.03 | 104000 | 0.3614 | 0.9402 |
| 0.3596 | 0.03 | 106000 | 0.3834 | 0.9381 |
| 0.3519 | 0.03 | 108000 | 0.3560 | 0.9421 |
| 0.3598 | 0.03 | 110000 | 0.3485 | 0.9419 |
| 0.3642 | 0.03 | 112000 | 0.3754 | 0.9395 |
| 0.3477 | 0.03 | 114000 | 0.3634 | 0.9426 |
| 0.4202 | 0.03 | 116000 | 0.3071 | 0.9427 |
| 0.3656 | 0.03 | 118000 | 0.3155 | 0.9441 |
| 0.3709 | 0.03 | 120000 | 0.2923 | 0.9433 |
| 0.374 | 0.03 | 122000 | 0.3272 | 0.9441 |
| 0.3142 | 0.03 | 124000 | 0.3348 | 0.9444 |
| 0.3452 | 0.04 | 126000 | 0.3603 | 0.9436 |
| 0.3365 | 0.04 | 128000 | 0.3339 | 0.9434 |
| 0.3353 | 0.04 | 130000 | 0.3471 | 0.9450 |
| 0.343 | 0.04 | 132000 | 0.3508 | 0.9418 |
| 0.3174 | 0.04 | 134000 | 0.3753 | 0.9436 |
| 0.3009 | 0.04 | 136000 | 0.3687 | 0.9422 |
| 0.3785 | 0.04 | 138000 | 0.3818 | 0.9396 |
| 0.3199 | 0.04 | 140000 | 0.3291 | 0.9438 |
| 0.4049 | 0.04 | 142000 | 0.3372 | 0.9454 |
| 0.3435 | 0.04 | 144000 | 0.3315 | 0.9459 |
| 0.3814 | 0.04 | 146000 | 0.3462 | 0.9401 |
| 0.359 | 0.04 | 148000 | 0.3981 | 0.9361 |
| 0.3552 | 0.04 | 150000 | 0.3226 | 0.9469 |
| 0.345 | 0.04 | 152000 | 0.3731 | 0.9384 |
| 0.3228 | 0.04 | 154000 | 0.2956 | 0.9471 |
| 0.3637 | 0.04 | 156000 | 0.2869 | 0.9477 |
| 0.349 | 0.04 | 158000 | 0.3331 | 0.9430 |
| 0.3374 | 0.04 | 160000 | 0.4159 | 0.9340 |
| 0.3718 | 0.05 | 162000 | 0.3241 | 0.9459 |
| 0.315 | 0.05 | 164000 | 0.3544 | 0.9391 |
| 0.3215 | 0.05 | 166000 | 0.3311 | 0.9451 |
| 0.3464 | 0.05 | 168000 | 0.3682 | 0.9453 |
| 0.3495 | 0.05 | 170000 | 0.3193 | 0.9469 |
| 0.305 | 0.05 | 172000 | 0.4132 | 0.9389 |
| 0.3479 | 0.05 | 174000 | 0.3465 | 0.947 |
| 0.3537 | 0.05 | 176000 | 0.3277 | 0.9449 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.7.1
- Datasets 1.12.1
- Tokenizers 0.10.3
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