metadata
tags:
- generated_from_trainer
model-index:
- name: digikala_products_parsbert_model
results: []
digikala_products_parsbert_model
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.7245
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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 25 | 7.8477 |
| No log | 2.0 | 50 | 7.0014 |
| No log | 3.0 | 75 | 6.3235 |
| No log | 4.0 | 100 | 5.6651 |
| No log | 5.0 | 125 | 4.9101 |
| No log | 6.0 | 150 | 4.2448 |
| No log | 7.0 | 175 | 3.8656 |
| No log | 8.0 | 200 | 3.4329 |
| No log | 9.0 | 225 | 3.3204 |
| No log | 10.0 | 250 | 3.0740 |
| No log | 11.0 | 275 | 2.9556 |
| No log | 12.0 | 300 | 2.9938 |
| No log | 13.0 | 325 | 2.8620 |
| No log | 14.0 | 350 | 2.7879 |
| No log | 15.0 | 375 | 2.8619 |
| No log | 16.0 | 400 | 2.8521 |
| No log | 17.0 | 425 | 2.7920 |
| No log | 18.0 | 450 | 2.8494 |
| No log | 19.0 | 475 | 2.8209 |
| 4.1477 | 20.0 | 500 | 2.8471 |
| 4.1477 | 21.0 | 525 | 2.8478 |
| 4.1477 | 22.0 | 550 | 2.7904 |
| 4.1477 | 23.0 | 575 | 2.7961 |
| 4.1477 | 24.0 | 600 | 2.7494 |
| 4.1477 | 25.0 | 625 | 2.8250 |
| 4.1477 | 26.0 | 650 | 2.7439 |
| 4.1477 | 27.0 | 675 | 2.7539 |
| 4.1477 | 28.0 | 700 | 2.7635 |
| 4.1477 | 29.0 | 725 | 2.7742 |
| 4.1477 | 30.0 | 750 | 2.7711 |
| 4.1477 | 31.0 | 775 | 2.8243 |
| 4.1477 | 32.0 | 800 | 2.7547 |
| 4.1477 | 33.0 | 825 | 2.7690 |
| 4.1477 | 34.0 | 850 | 2.7178 |
| 4.1477 | 35.0 | 875 | 2.7554 |
| 4.1477 | 36.0 | 900 | 2.7701 |
| 4.1477 | 37.0 | 925 | 2.7953 |
| 4.1477 | 38.0 | 950 | 2.8062 |
| 4.1477 | 39.0 | 975 | 2.7637 |
| 2.772 | 40.0 | 1000 | 2.7675 |
| 2.772 | 41.0 | 1025 | 2.7953 |
| 2.772 | 42.0 | 1050 | 2.8003 |
| 2.772 | 43.0 | 1075 | 2.7484 |
| 2.772 | 44.0 | 1100 | 2.7292 |
| 2.772 | 45.0 | 1125 | 2.7287 |
| 2.772 | 46.0 | 1150 | 2.6998 |
| 2.772 | 47.0 | 1175 | 2.7381 |
| 2.772 | 48.0 | 1200 | 2.7196 |
| 2.772 | 49.0 | 1225 | 2.7450 |
| 2.772 | 50.0 | 1250 | 2.7293 |
| 2.772 | 51.0 | 1275 | 2.7216 |
| 2.772 | 52.0 | 1300 | 2.7981 |
| 2.772 | 53.0 | 1325 | 2.7405 |
| 2.772 | 54.0 | 1350 | 2.7895 |
| 2.772 | 55.0 | 1375 | 2.7092 |
| 2.772 | 56.0 | 1400 | 2.7977 |
| 2.772 | 57.0 | 1425 | 2.7012 |
| 2.772 | 58.0 | 1450 | 2.7752 |
| 2.772 | 59.0 | 1475 | 2.7469 |
| 2.742 | 60.0 | 1500 | 2.7205 |
| 2.742 | 61.0 | 1525 | 2.7752 |
| 2.742 | 62.0 | 1550 | 2.6942 |
| 2.742 | 63.0 | 1575 | 2.6916 |
| 2.742 | 64.0 | 1600 | 2.8169 |
| 2.742 | 65.0 | 1625 | 2.7256 |
| 2.742 | 66.0 | 1650 | 2.6844 |
| 2.742 | 67.0 | 1675 | 2.7544 |
| 2.742 | 68.0 | 1700 | 2.7083 |
| 2.742 | 69.0 | 1725 | 2.7286 |
| 2.742 | 70.0 | 1750 | 2.7492 |
| 2.742 | 71.0 | 1775 | 2.6946 |
| 2.742 | 72.0 | 1800 | 2.7395 |
| 2.742 | 73.0 | 1825 | 2.7597 |
| 2.742 | 74.0 | 1850 | 2.7953 |
| 2.742 | 75.0 | 1875 | 2.7468 |
| 2.742 | 76.0 | 1900 | 2.7274 |
| 2.742 | 77.0 | 1925 | 2.7507 |
| 2.742 | 78.0 | 1950 | 2.7174 |
| 2.742 | 79.0 | 1975 | 2.7233 |
| 2.7185 | 80.0 | 2000 | 2.7405 |
| 2.7185 | 81.0 | 2025 | 2.7781 |
| 2.7185 | 82.0 | 2050 | 2.7534 |
| 2.7185 | 83.0 | 2075 | 2.7588 |
| 2.7185 | 84.0 | 2100 | 2.7469 |
| 2.7185 | 85.0 | 2125 | 2.6929 |
| 2.7185 | 86.0 | 2150 | 2.6785 |
| 2.7185 | 87.0 | 2175 | 2.7098 |
| 2.7185 | 88.0 | 2200 | 2.7622 |
| 2.7185 | 89.0 | 2225 | 2.7726 |
| 2.7185 | 90.0 | 2250 | 2.7144 |
| 2.7185 | 91.0 | 2275 | 2.7877 |
| 2.7185 | 92.0 | 2300 | 2.7665 |
| 2.7185 | 93.0 | 2325 | 2.7794 |
| 2.7185 | 94.0 | 2350 | 2.6788 |
| 2.7185 | 95.0 | 2375 | 2.7398 |
| 2.7185 | 96.0 | 2400 | 2.7277 |
| 2.7185 | 97.0 | 2425 | 2.8053 |
| 2.7185 | 98.0 | 2450 | 2.7537 |
| 2.7185 | 99.0 | 2475 | 2.7467 |
| 2.7057 | 100.0 | 2500 | 2.7191 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2