pytorch bert text-classification dataset:amazon_polarity transformers generated_from_trainer sibyl license:apache-2.0 model-index
---
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
---

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# 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
