--- tags: - generated_from_trainer model-index: - name: digikala_products_parsbert_model results: [] --- # digikala_products_parsbert_model This model is a fine-tuned version of [](https://huggingface.co/) 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