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---
license: apache-2.0
base_model: asapp/sew-d-tiny-100k-ft-ls100h
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sewd-classifier-aug-ref
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. -->
# sewd-classifier-aug-ref
This model is a fine-tuned version of [asapp/sew-d-tiny-100k-ft-ls100h](https://huggingface.co/asapp/sew-d-tiny-100k-ft-ls100h) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2210
- Accuracy: 0.6402
- Precision: 0.6291
- Recall: 0.6402
- F1: 0.6137
- Binary: 0.7478
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Binary |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| No log | 0.13 | 50 | 4.3839 | 0.0337 | 0.0029 | 0.0337 | 0.0052 | 0.2004 |
| No log | 0.27 | 100 | 4.1179 | 0.0687 | 0.0353 | 0.0687 | 0.0281 | 0.3295 |
| No log | 0.4 | 150 | 3.8682 | 0.0889 | 0.0335 | 0.0889 | 0.0338 | 0.3523 |
| No log | 0.54 | 200 | 3.6594 | 0.0997 | 0.0311 | 0.0997 | 0.0366 | 0.3635 |
| No log | 0.67 | 250 | 3.5084 | 0.1280 | 0.0534 | 0.1280 | 0.0494 | 0.3838 |
| No log | 0.81 | 300 | 3.3946 | 0.1469 | 0.0476 | 0.1469 | 0.0628 | 0.3964 |
| No log | 0.94 | 350 | 3.2570 | 0.1604 | 0.0699 | 0.1604 | 0.0830 | 0.4082 |
| 3.9151 | 1.08 | 400 | 3.1540 | 0.1806 | 0.0844 | 0.1806 | 0.0987 | 0.4224 |
| 3.9151 | 1.21 | 450 | 3.0449 | 0.1846 | 0.1000 | 0.1846 | 0.1005 | 0.4260 |
| 3.9151 | 1.35 | 500 | 2.9543 | 0.2237 | 0.1403 | 0.2237 | 0.1376 | 0.4534 |
| 3.9151 | 1.48 | 550 | 2.8691 | 0.2507 | 0.1621 | 0.2507 | 0.1606 | 0.4706 |
| 3.9151 | 1.62 | 600 | 2.7812 | 0.2493 | 0.1520 | 0.2493 | 0.1592 | 0.4718 |
| 3.9151 | 1.75 | 650 | 2.6598 | 0.2871 | 0.1855 | 0.2871 | 0.1856 | 0.4981 |
| 3.9151 | 1.89 | 700 | 2.6099 | 0.2951 | 0.2123 | 0.2951 | 0.2019 | 0.5047 |
| 3.1406 | 2.02 | 750 | 2.5039 | 0.3235 | 0.2106 | 0.3235 | 0.2230 | 0.5236 |
| 3.1406 | 2.16 | 800 | 2.4359 | 0.3383 | 0.2454 | 0.3383 | 0.2501 | 0.5358 |
| 3.1406 | 2.29 | 850 | 2.3869 | 0.3154 | 0.2329 | 0.3154 | 0.2324 | 0.5179 |
| 3.1406 | 2.43 | 900 | 2.3144 | 0.3612 | 0.2937 | 0.3612 | 0.2798 | 0.5513 |
| 3.1406 | 2.56 | 950 | 2.2470 | 0.3720 | 0.3122 | 0.3720 | 0.2908 | 0.5584 |
| 3.1406 | 2.7 | 1000 | 2.1944 | 0.3774 | 0.3099 | 0.3774 | 0.2992 | 0.5632 |
| 3.1406 | 2.83 | 1050 | 2.1421 | 0.4030 | 0.3250 | 0.4030 | 0.3226 | 0.5819 |
| 3.1406 | 2.97 | 1100 | 2.0630 | 0.4137 | 0.3442 | 0.4137 | 0.3336 | 0.5899 |
| 2.6974 | 3.1 | 1150 | 2.0115 | 0.4245 | 0.3679 | 0.4245 | 0.3510 | 0.5974 |
| 2.6974 | 3.24 | 1200 | 1.9716 | 0.4434 | 0.3964 | 0.4434 | 0.3729 | 0.6093 |
| 2.6974 | 3.37 | 1250 | 1.9255 | 0.4488 | 0.3972 | 0.4488 | 0.3883 | 0.6150 |
| 2.6974 | 3.51 | 1300 | 1.8715 | 0.4623 | 0.4112 | 0.4623 | 0.3969 | 0.6228 |
| 2.6974 | 3.64 | 1350 | 1.8223 | 0.4825 | 0.4534 | 0.4825 | 0.4222 | 0.6369 |
| 2.6974 | 3.78 | 1400 | 1.7951 | 0.5013 | 0.4728 | 0.5013 | 0.4500 | 0.6511 |
| 2.6974 | 3.91 | 1450 | 1.7427 | 0.5270 | 0.4855 | 0.5270 | 0.4804 | 0.6686 |
| 2.3963 | 4.05 | 1500 | 1.7319 | 0.5 | 0.4618 | 0.5 | 0.4452 | 0.6493 |
| 2.3963 | 4.18 | 1550 | 1.7098 | 0.4960 | 0.4588 | 0.4960 | 0.4454 | 0.6473 |
| 2.3963 | 4.32 | 1600 | 1.6518 | 0.5310 | 0.5051 | 0.5310 | 0.4855 | 0.6709 |
| 2.3963 | 4.45 | 1650 | 1.6535 | 0.5067 | 0.4838 | 0.5067 | 0.4552 | 0.6539 |
| 2.3963 | 4.59 | 1700 | 1.6011 | 0.5539 | 0.5106 | 0.5539 | 0.5061 | 0.6865 |
| 2.3963 | 4.72 | 1750 | 1.5894 | 0.5404 | 0.4940 | 0.5404 | 0.4923 | 0.6767 |
| 2.3963 | 4.86 | 1800 | 1.5580 | 0.5660 | 0.5371 | 0.5660 | 0.5285 | 0.6964 |
| 2.3963 | 4.99 | 1850 | 1.5375 | 0.5431 | 0.5032 | 0.5431 | 0.4968 | 0.6803 |
| 2.1926 | 5.12 | 1900 | 1.5166 | 0.5620 | 0.5237 | 0.5620 | 0.5193 | 0.6941 |
| 2.1926 | 5.26 | 1950 | 1.5168 | 0.5526 | 0.5198 | 0.5526 | 0.5085 | 0.6860 |
| 2.1926 | 5.39 | 2000 | 1.4773 | 0.5836 | 0.5615 | 0.5836 | 0.5455 | 0.7073 |
| 2.1926 | 5.53 | 2050 | 1.4488 | 0.5782 | 0.5564 | 0.5782 | 0.5396 | 0.7054 |
| 2.1926 | 5.66 | 2100 | 1.4335 | 0.5916 | 0.5691 | 0.5916 | 0.5560 | 0.7143 |
| 2.1926 | 5.8 | 2150 | 1.4078 | 0.5957 | 0.5782 | 0.5957 | 0.5641 | 0.7177 |
| 2.1926 | 5.93 | 2200 | 1.4092 | 0.5863 | 0.5691 | 0.5863 | 0.5506 | 0.7105 |
| 2.0446 | 6.07 | 2250 | 1.3942 | 0.5755 | 0.5405 | 0.5755 | 0.5334 | 0.7026 |
| 2.0446 | 6.2 | 2300 | 1.3828 | 0.5930 | 0.5776 | 0.5930 | 0.5613 | 0.7148 |
| 2.0446 | 6.34 | 2350 | 1.3625 | 0.6065 | 0.5886 | 0.6065 | 0.5688 | 0.7247 |
| 2.0446 | 6.47 | 2400 | 1.3444 | 0.6119 | 0.6008 | 0.6119 | 0.5754 | 0.7284 |
| 2.0446 | 6.61 | 2450 | 1.3088 | 0.6267 | 0.6134 | 0.6267 | 0.5914 | 0.7388 |
| 2.0446 | 6.74 | 2500 | 1.3183 | 0.6038 | 0.5869 | 0.6038 | 0.5729 | 0.7228 |
| 2.0446 | 6.88 | 2550 | 1.3000 | 0.6173 | 0.5886 | 0.6173 | 0.5810 | 0.7322 |
| 1.9441 | 7.01 | 2600 | 1.2930 | 0.6213 | 0.6048 | 0.6213 | 0.5900 | 0.7341 |
| 1.9441 | 7.15 | 2650 | 1.2757 | 0.6226 | 0.6097 | 0.6226 | 0.5959 | 0.7361 |
| 1.9441 | 7.28 | 2700 | 1.2787 | 0.6226 | 0.6091 | 0.6226 | 0.5963 | 0.7361 |
| 1.9441 | 7.42 | 2750 | 1.2566 | 0.6240 | 0.6204 | 0.6240 | 0.5983 | 0.7375 |
| 1.9441 | 7.55 | 2800 | 1.2549 | 0.6253 | 0.6055 | 0.6253 | 0.5970 | 0.7380 |
| 1.9441 | 7.69 | 2850 | 1.2396 | 0.6240 | 0.6255 | 0.6240 | 0.5954 | 0.7371 |
| 1.9441 | 7.82 | 2900 | 1.2400 | 0.6388 | 0.6361 | 0.6388 | 0.6128 | 0.7478 |
| 1.9441 | 7.96 | 2950 | 1.2369 | 0.6294 | 0.6188 | 0.6294 | 0.5996 | 0.7407 |
| 1.8636 | 8.09 | 3000 | 1.2235 | 0.6375 | 0.6363 | 0.6375 | 0.6151 | 0.7460 |
| 1.8636 | 8.23 | 3050 | 1.2178 | 0.6456 | 0.6415 | 0.6456 | 0.6196 | 0.7520 |
| 1.8636 | 8.36 | 3100 | 1.2093 | 0.6402 | 0.6346 | 0.6402 | 0.6149 | 0.7482 |
| 1.8636 | 8.5 | 3150 | 1.2210 | 0.6402 | 0.6291 | 0.6402 | 0.6137 | 0.7478 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.15.1
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