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license: mit
base_model: prajjwal1/bert-tiny
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: INT03-PC
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. -->
# INT03-PC
This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0158
- Accuracy: 1.0
- F1: 1.0
## 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: 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 0.0 | 50 | 0.6785 | 0.74 | 0.7413 |
| No log | 0.01 | 100 | 0.6047 | 0.79 | 0.7903 |
| No log | 0.01 | 150 | 0.4424 | 0.88 | 0.8769 |
| No log | 0.02 | 200 | 0.3601 | 0.89 | 0.8868 |
| No log | 0.02 | 250 | 0.3436 | 0.89 | 0.8868 |
| No log | 0.03 | 300 | 0.3311 | 0.9 | 0.8975 |
| No log | 0.03 | 350 | 0.3145 | 0.89 | 0.8876 |
| No log | 0.04 | 400 | 0.3113 | 0.9 | 0.8982 |
| No log | 0.04 | 450 | 0.2994 | 0.9 | 0.8982 |
| 0.4886 | 0.05 | 500 | 0.2806 | 0.88 | 0.8785 |
| 0.4886 | 0.05 | 550 | 0.2179 | 0.92 | 0.9194 |
| 0.4886 | 0.06 | 600 | 0.2388 | 0.88 | 0.8803 |
| 0.4886 | 0.06 | 650 | 0.1716 | 0.94 | 0.9398 |
| 0.4886 | 0.07 | 700 | 0.1774 | 0.93 | 0.9302 |
| 0.4886 | 0.07 | 750 | 0.1456 | 0.95 | 0.9501 |
| 0.4886 | 0.08 | 800 | 0.1518 | 0.94 | 0.9402 |
| 0.4886 | 0.08 | 850 | 0.1564 | 0.93 | 0.9303 |
| 0.4886 | 0.09 | 900 | 0.1684 | 0.92 | 0.9204 |
| 0.4886 | 0.09 | 950 | 0.1372 | 0.96 | 0.9602 |
| 0.2446 | 0.1 | 1000 | 0.1368 | 0.94 | 0.9402 |
| 0.2446 | 0.1 | 1050 | 0.1502 | 0.95 | 0.9502 |
| 0.2446 | 0.11 | 1100 | 0.1385 | 0.95 | 0.9502 |
| 0.2446 | 0.11 | 1150 | 0.1297 | 0.96 | 0.9602 |
| 0.2446 | 0.12 | 1200 | 0.1917 | 0.95 | 0.9502 |
| 0.2446 | 0.12 | 1250 | 0.1042 | 0.97 | 0.9700 |
| 0.2446 | 0.13 | 1300 | 0.1502 | 0.96 | 0.9602 |
| 0.2446 | 0.13 | 1350 | 0.1436 | 0.96 | 0.9602 |
| 0.2446 | 0.14 | 1400 | 0.0896 | 0.98 | 0.9800 |
| 0.2446 | 0.14 | 1450 | 0.1045 | 0.96 | 0.9602 |
| 0.1824 | 0.15 | 1500 | 0.1269 | 0.96 | 0.9602 |
| 0.1824 | 0.15 | 1550 | 0.1449 | 0.96 | 0.9602 |
| 0.1824 | 0.16 | 1600 | 0.1311 | 0.96 | 0.9602 |
| 0.1824 | 0.16 | 1650 | 0.1380 | 0.96 | 0.9602 |
| 0.1824 | 0.17 | 1700 | 0.1466 | 0.96 | 0.9602 |
| 0.1824 | 0.17 | 1750 | 0.0861 | 0.98 | 0.9800 |
| 0.1824 | 0.18 | 1800 | 0.1323 | 0.96 | 0.9602 |
| 0.1824 | 0.18 | 1850 | 0.1375 | 0.96 | 0.9602 |
| 0.1824 | 0.19 | 1900 | 0.1719 | 0.96 | 0.9602 |
| 0.1824 | 0.19 | 1950 | 0.0837 | 0.98 | 0.9800 |
| 0.1252 | 0.2 | 2000 | 0.1661 | 0.96 | 0.9602 |
| 0.1252 | 0.2 | 2050 | 0.1129 | 0.96 | 0.9602 |
| 0.1252 | 0.21 | 2100 | 0.0603 | 0.98 | 0.9800 |
| 0.1252 | 0.21 | 2150 | 0.1231 | 0.96 | 0.9602 |
| 0.1252 | 0.22 | 2200 | 0.1363 | 0.96 | 0.9602 |
| 0.1252 | 0.22 | 2250 | 0.1330 | 0.96 | 0.9602 |
| 0.1252 | 0.23 | 2300 | 0.0795 | 0.96 | 0.9602 |
| 0.1252 | 0.23 | 2350 | 0.0856 | 0.96 | 0.9602 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
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