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---
license: apache-2.0
base_model: Yama/bert-base-uncased-finetuned-swag
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-uncased-finetuned-swag
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-base-uncased-finetuned-swag
This model is a fine-tuned version of [Yama/bert-base-uncased-finetuned-swag](https://huggingface.co/Yama/bert-base-uncased-finetuned-swag) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0061
- Accuracy: 0.9958
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 150 | 1.3780 | 0.3592 |
| No log | 2.0 | 300 | 1.3234 | 0.4383 |
| No log | 3.0 | 450 | 1.3158 | 0.4992 |
| 1.3577 | 4.0 | 600 | 1.1356 | 0.5792 |
| 1.3577 | 5.0 | 750 | 0.7939 | 0.7217 |
| 1.3577 | 6.0 | 900 | 0.6167 | 0.7958 |
| 1.0479 | 7.0 | 1050 | 0.4737 | 0.8467 |
| 1.0479 | 8.0 | 1200 | 0.3424 | 0.8867 |
| 1.0479 | 9.0 | 1350 | 0.2448 | 0.9142 |
| 0.5968 | 10.0 | 1500 | 0.2117 | 0.9158 |
| 0.5968 | 11.0 | 1650 | 0.1589 | 0.9467 |
| 0.5968 | 12.0 | 1800 | 0.1420 | 0.9492 |
| 0.5968 | 13.0 | 1950 | 0.0970 | 0.9675 |
| 0.3341 | 14.0 | 2100 | 0.1014 | 0.9725 |
| 0.3341 | 15.0 | 2250 | 0.0678 | 0.9742 |
| 0.3341 | 16.0 | 2400 | 0.0624 | 0.9825 |
| 0.1802 | 17.0 | 2550 | 0.0407 | 0.9783 |
| 0.1802 | 18.0 | 2700 | 0.0501 | 0.9858 |
| 0.1802 | 19.0 | 2850 | 0.0341 | 0.9867 |
| 0.1213 | 20.0 | 3000 | 0.0284 | 0.9883 |
| 0.1213 | 21.0 | 3150 | 0.0398 | 0.9883 |
| 0.1213 | 22.0 | 3300 | 0.0290 | 0.9908 |
| 0.1213 | 23.0 | 3450 | 0.0211 | 0.9908 |
| 0.0758 | 24.0 | 3600 | 0.0179 | 0.9908 |
| 0.0758 | 25.0 | 3750 | 0.0151 | 0.9917 |
| 0.0758 | 26.0 | 3900 | 0.0154 | 0.9933 |
| 0.0464 | 27.0 | 4050 | 0.0216 | 0.9942 |
| 0.0464 | 28.0 | 4200 | 0.0124 | 0.9942 |
| 0.0464 | 29.0 | 4350 | 0.0122 | 0.9942 |
| 0.0306 | 30.0 | 4500 | 0.0103 | 0.9942 |
| 0.0306 | 31.0 | 4650 | 0.0094 | 0.9942 |
| 0.0306 | 32.0 | 4800 | 0.0083 | 0.9942 |
| 0.0306 | 33.0 | 4950 | 0.0079 | 0.9958 |
| 0.0201 | 34.0 | 5100 | 0.0079 | 0.9950 |
| 0.0201 | 35.0 | 5250 | 0.0069 | 0.9958 |
| 0.0201 | 36.0 | 5400 | 0.0069 | 0.9950 |
| 0.0205 | 37.0 | 5550 | 0.0060 | 0.9967 |
| 0.0205 | 38.0 | 5700 | 0.0060 | 0.9958 |
| 0.0205 | 39.0 | 5850 | 0.0061 | 0.9958 |
| 0.0102 | 40.0 | 6000 | 0.0061 | 0.9958 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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