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---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-tiny-mlm-finetuned-imdb
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-tiny-mlm-finetuned-imdb
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4487
## 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: 3e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 4.1774 | 1.04 | 500 | 3.7705 |
| 4.041 | 2.09 | 1000 | 3.7196 |
| 3.9982 | 3.13 | 1500 | 3.6826 |
| 3.9614 | 4.18 | 2000 | 3.6543 |
| 3.9274 | 5.22 | 2500 | 3.6438 |
| 3.9089 | 6.26 | 3000 | 3.6294 |
| 3.8929 | 7.31 | 3500 | 3.6217 |
| 3.873 | 8.35 | 4000 | 3.6083 |
| 3.8659 | 9.39 | 4500 | 3.5900 |
| 3.8484 | 10.44 | 5000 | 3.5791 |
| 3.8261 | 11.48 | 5500 | 3.5731 |
| 3.8228 | 12.53 | 6000 | 3.5579 |
| 3.8098 | 13.57 | 6500 | 3.5576 |
| 3.8028 | 14.61 | 7000 | 3.5532 |
| 3.7881 | 15.66 | 7500 | 3.5440 |
| 3.7829 | 16.7 | 8000 | 3.5440 |
| 3.7727 | 17.75 | 8500 | 3.5372 |
| 3.7648 | 18.79 | 9000 | 3.5248 |
| 3.7504 | 19.83 | 9500 | 3.5223 |
| 3.7487 | 20.88 | 10000 | 3.5212 |
| 3.7497 | 21.92 | 10500 | 3.5166 |
| 3.7344 | 22.96 | 11000 | 3.5103 |
| 3.7339 | 24.01 | 11500 | 3.5052 |
| 3.722 | 25.05 | 12000 | 3.5067 |
| 3.7188 | 26.1 | 12500 | 3.4941 |
| 3.7127 | 27.14 | 13000 | 3.4951 |
| 3.7113 | 28.18 | 13500 | 3.4904 |
| 3.7042 | 29.23 | 14000 | 3.4813 |
| 3.7011 | 30.27 | 14500 | 3.4805 |
| 3.6936 | 31.32 | 15000 | 3.4886 |
| 3.6889 | 32.36 | 15500 | 3.4825 |
| 3.6771 | 33.4 | 16000 | 3.4785 |
| 3.6753 | 34.45 | 16500 | 3.4819 |
| 3.6743 | 35.49 | 17000 | 3.4744 |
| 3.6686 | 36.53 | 17500 | 3.4658 |
| 3.669 | 37.58 | 18000 | 3.4607 |
| 3.6623 | 38.62 | 18500 | 3.4688 |
| 3.6648 | 39.67 | 19000 | 3.4676 |
| 3.6574 | 40.71 | 19500 | 3.4581 |
| 3.652 | 41.75 | 20000 | 3.4601 |
| 3.6506 | 42.8 | 20500 | 3.4630 |
| 3.6466 | 43.84 | 21000 | 3.4530 |
| 3.637 | 44.89 | 21500 | 3.4507 |
| 3.6428 | 45.93 | 22000 | 3.4557 |
| 3.6408 | 46.97 | 22500 | 3.4483 |
| 3.6368 | 48.02 | 23000 | 3.4505 |
| 3.6322 | 49.06 | 23500 | 3.4494 |
| 3.6256 | 50.1 | 24000 | 3.4487 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
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