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---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert_sst2_padding70model
results: []
---
<!-- 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_sst2_padding70model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6816
- Accuracy: 0.9209
## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 433 | 0.2422 | 0.9121 |
| 0.3225 | 2.0 | 866 | 0.3330 | 0.9094 |
| 0.15 | 3.0 | 1299 | 0.4125 | 0.9193 |
| 0.0796 | 4.0 | 1732 | 0.4849 | 0.9088 |
| 0.033 | 5.0 | 2165 | 0.6146 | 0.9023 |
| 0.0252 | 6.0 | 2598 | 0.5862 | 0.9105 |
| 0.0147 | 7.0 | 3031 | 0.6562 | 0.9121 |
| 0.0147 | 8.0 | 3464 | 0.6735 | 0.9171 |
| 0.01 | 9.0 | 3897 | 0.7122 | 0.9099 |
| 0.017 | 10.0 | 4330 | 0.6584 | 0.9149 |
| 0.0106 | 11.0 | 4763 | 0.7113 | 0.9171 |
| 0.0077 | 12.0 | 5196 | 0.7330 | 0.9149 |
| 0.0108 | 13.0 | 5629 | 0.6942 | 0.9143 |
| 0.0126 | 14.0 | 6062 | 0.6131 | 0.9160 |
| 0.0126 | 15.0 | 6495 | 0.6609 | 0.9182 |
| 0.0074 | 16.0 | 6928 | 0.6579 | 0.9193 |
| 0.0075 | 17.0 | 7361 | 0.6388 | 0.9220 |
| 0.002 | 18.0 | 7794 | 0.6524 | 0.9253 |
| 0.0014 | 19.0 | 8227 | 0.6741 | 0.9209 |
| 0.0009 | 20.0 | 8660 | 0.6816 | 0.9209 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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