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---
license: apache-2.0
tags:
- generated_from_trainer
- sibyl
datasets:
- yelp_polarity
metrics:
- accuracy
model-index:
- name: bert-base-uncased-yelp_polarity
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: yelp_polarity
type: yelp_polarity
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.9516052631578947
---
<!-- 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-yelp_polarity
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the yelp_polarity dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3222
- Accuracy: 0.9516
## 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: 277200
- training_steps: 2772000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8067 | 0.0 | 2000 | 0.8241 | 0.4975 |
| 0.5482 | 0.01 | 4000 | 0.3507 | 0.8591 |
| 0.3427 | 0.01 | 6000 | 0.3750 | 0.9139 |
| 0.4133 | 0.01 | 8000 | 0.5520 | 0.9016 |
| 0.4301 | 0.02 | 10000 | 0.3803 | 0.9304 |
| 0.3716 | 0.02 | 12000 | 0.4168 | 0.9337 |
| 0.4076 | 0.03 | 14000 | 0.5042 | 0.9170 |
| 0.3674 | 0.03 | 16000 | 0.4806 | 0.9268 |
| 0.3813 | 0.03 | 18000 | 0.4227 | 0.9261 |
| 0.3723 | 0.04 | 20000 | 0.3360 | 0.9418 |
| 0.3876 | 0.04 | 22000 | 0.3255 | 0.9407 |
| 0.3351 | 0.04 | 24000 | 0.3283 | 0.9404 |
| 0.34 | 0.05 | 26000 | 0.3489 | 0.9430 |
| 0.3006 | 0.05 | 28000 | 0.3302 | 0.9464 |
| 0.349 | 0.05 | 30000 | 0.3853 | 0.9375 |
| 0.3696 | 0.06 | 32000 | 0.2992 | 0.9454 |
| 0.3301 | 0.06 | 34000 | 0.3484 | 0.9464 |
| 0.3151 | 0.06 | 36000 | 0.3529 | 0.9455 |
| 0.3682 | 0.07 | 38000 | 0.3052 | 0.9420 |
| 0.3184 | 0.07 | 40000 | 0.3323 | 0.9466 |
| 0.3207 | 0.08 | 42000 | 0.3133 | 0.9532 |
| 0.3346 | 0.08 | 44000 | 0.3826 | 0.9414 |
| 0.3008 | 0.08 | 46000 | 0.3059 | 0.9484 |
| 0.3306 | 0.09 | 48000 | 0.3089 | 0.9475 |
| 0.342 | 0.09 | 50000 | 0.3611 | 0.9486 |
| 0.3424 | 0.09 | 52000 | 0.3227 | 0.9445 |
| 0.3044 | 0.1 | 54000 | 0.3130 | 0.9489 |
| 0.3278 | 0.1 | 56000 | 0.3827 | 0.9368 |
| 0.288 | 0.1 | 58000 | 0.3080 | 0.9504 |
| 0.3342 | 0.11 | 60000 | 0.3252 | 0.9471 |
| 0.3737 | 0.11 | 62000 | 0.4250 | 0.9343 |
### Framework versions
- Transformers 4.10.2
- Pytorch 1.7.1
- Datasets 1.6.1
- Tokenizers 0.10.3
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