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---
license: mit
base_model: prajjwal1/bert-small
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-small-finetuned
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-small-finetuned
This model is a fine-tuned version of [prajjwal1/bert-small](https://huggingface.co/prajjwal1/bert-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0048
- Accuracy: 0.6038
- F1 Score: 0.6018
## 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: 86
- eval_batch_size: 86
- 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 | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 1.3167 | 1.0 | 18 | 1.2414 | 0.4151 | 0.3857 |
| 1.1845 | 2.0 | 36 | 1.1500 | 0.5148 | 0.5148 |
| 1.0823 | 3.0 | 54 | 1.0743 | 0.5499 | 0.5543 |
| 0.995 | 4.0 | 72 | 1.0359 | 0.5553 | 0.5529 |
| 0.9242 | 5.0 | 90 | 1.0195 | 0.5849 | 0.5781 |
| 0.8742 | 6.0 | 108 | 1.0028 | 0.5741 | 0.5758 |
| 0.8237 | 7.0 | 126 | 1.0033 | 0.5930 | 0.5901 |
| 0.7893 | 8.0 | 144 | 0.9967 | 0.5930 | 0.5922 |
| 0.7332 | 9.0 | 162 | 1.0088 | 0.5957 | 0.5924 |
| 0.6997 | 10.0 | 180 | 1.0048 | 0.6038 | 0.6018 |
| 0.6836 | 11.0 | 198 | 1.0120 | 0.6011 | 0.5981 |
| 0.6571 | 12.0 | 216 | 1.0084 | 0.5849 | 0.5864 |
| 0.6253 | 13.0 | 234 | 1.0167 | 0.5903 | 0.5938 |
| 0.5902 | 14.0 | 252 | 1.0184 | 0.5930 | 0.5965 |
| 0.5766 | 15.0 | 270 | 1.0340 | 0.5930 | 0.5925 |
| 0.5591 | 16.0 | 288 | 1.0399 | 0.5930 | 0.5931 |
| 0.5353 | 17.0 | 306 | 1.0364 | 0.5930 | 0.5944 |
| 0.5205 | 18.0 | 324 | 1.0412 | 0.5876 | 0.5889 |
| 0.5197 | 19.0 | 342 | 1.0410 | 0.5849 | 0.5867 |
| 0.5222 | 20.0 | 360 | 1.0418 | 0.5984 | 0.5990 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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