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---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: final_V4_resized_balanced_Bert_balanced_dataset-after-adding-new-words-text-classification-model
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. -->
# final_V4_resized_balanced_Bert_balanced_dataset-after-adding-new-words-text-classification-model
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7809
- Accuracy: 0.5419
- F1: 0.5
- Precision: 0.5
- Recall: 0.5
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.8374 | 0.06 | 50 | 1.8580 | 0.1350 | 0.0735 | 0.0910 | 0.1771 |
| 1.1067 | 0.12 | 100 | 1.7265 | 0.4003 | 0.2554 | 0.3101 | 0.3385 |
| 0.5553 | 0.18 | 150 | 0.9466 | 0.7571 | 0.5029 | 0.5522 | 0.5495 |
| 0.2362 | 0.25 | 200 | 0.8960 | 0.8595 | 0.7592 | 0.7701 | 0.7770 |
| 0.2549 | 0.31 | 250 | 0.9180 | 0.8623 | 0.7585 | 0.7737 | 0.7785 |
| 0.1716 | 0.37 | 300 | 0.9975 | 0.8646 | 0.7807 | 0.7630 | 0.8138 |
| 0.2299 | 0.43 | 350 | 0.8119 | 0.8614 | 0.7843 | 0.7481 | 0.8354 |
| 0.1657 | 0.49 | 400 | 0.9501 | 0.8657 | 0.7878 | 0.7724 | 0.8215 |
| 0.1585 | 0.55 | 450 | 1.0274 | 0.8661 | 0.7962 | 0.7708 | 0.8367 |
| 0.1856 | 0.61 | 500 | 1.0357 | 0.8675 | 0.7948 | 0.7554 | 0.8510 |
| 0.1002 | 0.67 | 550 | 1.1383 | 0.8657 | 0.7978 | 0.7673 | 0.8423 |
| 0.1505 | 0.74 | 600 | 1.0459 | 0.8678 | 0.7981 | 0.7646 | 0.8477 |
| 0.1264 | 0.8 | 650 | 0.9859 | 0.8692 | 0.8048 | 0.7781 | 0.8467 |
| 0.13 | 0.86 | 700 | 1.0246 | 0.8678 | 0.7947 | 0.7569 | 0.8496 |
| 0.1151 | 0.92 | 750 | 0.5834 | 0.8764 | 0.8223 | 0.8837 | 0.8621 |
| 0.1776 | 0.98 | 800 | 1.0297 | 0.8675 | 0.7903 | 0.7773 | 0.8202 |
| 0.0488 | 1.04 | 850 | 1.0348 | 0.8700 | 0.8038 | 0.7724 | 0.8504 |
| 0.0752 | 1.1 | 900 | 1.1004 | 0.8675 | 0.7839 | 0.7725 | 0.8136 |
| 0.0696 | 1.17 | 950 | 1.1802 | 0.8701 | 0.8020 | 0.7756 | 0.8438 |
| 0.0743 | 1.23 | 1000 | 1.1167 | 0.8695 | 0.8065 | 0.7791 | 0.8490 |
| 0.0757 | 1.29 | 1050 | 1.1188 | 0.8704 | 0.8078 | 0.7795 | 0.8516 |
| 0.0757 | 1.35 | 1100 | 0.7870 | 0.8692 | 0.8062 | 0.7816 | 0.8460 |
| 0.0847 | 1.41 | 1150 | 1.0518 | 0.8698 | 0.8045 | 0.7791 | 0.8450 |
| 0.0502 | 1.47 | 1200 | 1.1333 | 0.8689 | 0.7993 | 0.7679 | 0.8469 |
| 0.0516 | 1.53 | 1250 | 1.2185 | 0.8626 | 0.7707 | 0.7217 | 0.8429 |
| 0.0841 | 1.6 | 1300 | 1.2722 | 0.8689 | 0.8026 | 0.7798 | 0.8404 |
| 0.1063 | 1.66 | 1350 | 1.2437 | 0.8690 | 0.8008 | 0.7808 | 0.8362 |
| 0.097 | 1.72 | 1400 | 1.1243 | 0.8684 | 0.7930 | 0.7836 | 0.8201 |
| 0.0746 | 1.78 | 1450 | 1.2221 | 0.8701 | 0.8072 | 0.7801 | 0.8498 |
| 0.0726 | 1.84 | 1500 | 0.7919 | 0.8676 | 0.8076 | 0.7799 | 0.8500 |
| 0.0779 | 1.9 | 1550 | 1.1613 | 0.8704 | 0.8092 | 0.7837 | 0.8500 |
| 0.0895 | 1.96 | 1600 | 1.0377 | 0.8704 | 0.8060 | 0.7788 | 0.8485 |
| 0.0481 | 2.02 | 1650 | 1.1583 | 0.8710 | 0.8087 | 0.7810 | 0.8520 |
| 0.0266 | 2.09 | 1700 | 1.1655 | 0.8687 | 0.8020 | 0.7734 | 0.8452 |
| 0.0403 | 2.15 | 1750 | 1.2421 | 0.8707 | 0.8066 | 0.7777 | 0.8509 |
| 0.0116 | 2.21 | 1800 | 1.2306 | 0.8701 | 0.8048 | 0.7778 | 0.8474 |
| 0.0287 | 2.27 | 1850 | 1.2461 | 0.8700 | 0.8057 | 0.7784 | 0.8487 |
| 0.0197 | 2.33 | 1900 | 1.2199 | 0.8612 | 0.7937 | 0.7568 | 0.8451 |
| 0.0325 | 2.39 | 1950 | 1.3021 | 0.8703 | 0.8051 | 0.7785 | 0.8472 |
| 0.0443 | 2.45 | 2000 | 1.2395 | 0.8703 | 0.8061 | 0.7771 | 0.8503 |
| 0.0189 | 2.52 | 2050 | 1.2496 | 0.8704 | 0.8052 | 0.7812 | 0.8449 |
| 0.0056 | 2.58 | 2100 | 1.2561 | 0.8706 | 0.8073 | 0.7772 | 0.8527 |
| 0.0188 | 2.64 | 2150 | 1.2711 | 0.8706 | 0.8053 | 0.7818 | 0.8443 |
| 0.0287 | 2.7 | 2200 | 1.2728 | 0.8706 | 0.8068 | 0.7781 | 0.8504 |
| 0.0487 | 2.76 | 2250 | 1.1602 | 0.8710 | 0.8074 | 0.7802 | 0.8499 |
| 0.0409 | 2.82 | 2300 | 1.0628 | 0.8706 | 0.8061 | 0.7760 | 0.8510 |
| 0.053 | 2.88 | 2350 | 1.1891 | 0.8707 | 0.8076 | 0.7779 | 0.8526 |
| 0.0109 | 2.94 | 2400 | 1.2429 | 0.8700 | 0.8065 | 0.7811 | 0.8476 |
| 0.0392 | 3.01 | 2450 | 1.2635 | 0.8709 | 0.8058 | 0.7759 | 0.8509 |
| 0.0237 | 3.07 | 2500 | 1.2678 | 0.8703 | 0.8023 | 0.7817 | 0.8386 |
| 0.007 | 3.13 | 2550 | 1.2495 | 0.8709 | 0.8077 | 0.7812 | 0.8497 |
| 0.009 | 3.19 | 2600 | 1.2368 | 0.8715 | 0.8091 | 0.7807 | 0.8529 |
| 0.0022 | 3.25 | 2650 | 1.2436 | 0.8707 | 0.8055 | 0.7829 | 0.8435 |
| 0.0175 | 3.31 | 2700 | 1.2469 | 0.8712 | 0.8079 | 0.7818 | 0.8493 |
| 0.0037 | 3.37 | 2750 | 1.2342 | 0.8710 | 0.8068 | 0.7781 | 0.8510 |
| 0.0091 | 3.44 | 2800 | 1.2489 | 0.8701 | 0.8041 | 0.7780 | 0.8459 |
| 0.0008 | 3.5 | 2850 | 1.2252 | 0.8709 | 0.8059 | 0.7742 | 0.8532 |
| 0.005 | 3.56 | 2900 | 1.2281 | 0.8696 | 0.8030 | 0.7693 | 0.8522 |
| 0.0004 | 3.62 | 2950 | 1.2746 | 0.8704 | 0.8052 | 0.7760 | 0.8501 |
| 0.0009 | 3.68 | 3000 | 1.2903 | 0.8706 | 0.8054 | 0.7760 | 0.8504 |
| 0.001 | 3.74 | 3050 | 1.2960 | 0.8712 | 0.8060 | 0.7780 | 0.8492 |
| 0.0002 | 3.8 | 3100 | 1.3036 | 0.8712 | 0.8060 | 0.7780 | 0.8492 |
| 0.0185 | 3.87 | 3150 | 1.3224 | 0.8701 | 0.8048 | 0.7797 | 0.8453 |
| 0.0013 | 3.93 | 3200 | 1.3236 | 0.8707 | 0.8064 | 0.7780 | 0.8503 |
| 0.0003 | 3.99 | 3250 | 1.3241 | 0.8710 | 0.8068 | 0.7780 | 0.8509 |
| 0.0128 | 4.05 | 3300 | 1.3175 | 0.8704 | 0.8075 | 0.7794 | 0.8504 |
| 0.0005 | 4.11 | 3350 | 1.3160 | 0.8709 | 0.8078 | 0.7797 | 0.8508 |
| 0.0147 | 4.17 | 3400 | 1.3180 | 0.8712 | 0.8078 | 0.7803 | 0.8505 |
| 0.0064 | 4.23 | 3450 | 1.3197 | 0.8710 | 0.8076 | 0.7793 | 0.8510 |
| 0.0009 | 4.29 | 3500 | 1.3245 | 0.8712 | 0.8080 | 0.7800 | 0.8512 |
| 0.0002 | 4.36 | 3550 | 1.3336 | 0.8712 | 0.8079 | 0.7820 | 0.8491 |
| 0.0131 | 4.42 | 3600 | 1.3113 | 0.8710 | 0.8076 | 0.7794 | 0.8511 |
| 0.0003 | 4.48 | 3650 | 1.3200 | 0.8712 | 0.8079 | 0.7820 | 0.8491 |
| 0.0005 | 4.54 | 3700 | 1.3258 | 0.8712 | 0.8080 | 0.7841 | 0.8472 |
| 0.0102 | 4.6 | 3750 | 1.3177 | 0.8712 | 0.8079 | 0.7797 | 0.8512 |
| 0.0161 | 4.66 | 3800 | 1.3042 | 0.8712 | 0.8077 | 0.7794 | 0.8512 |
| 0.0178 | 4.72 | 3850 | 1.3133 | 0.8710 | 0.8076 | 0.7794 | 0.8511 |
| 0.0067 | 4.79 | 3900 | 1.3154 | 0.8709 | 0.8076 | 0.7793 | 0.8510 |
| 0.0191 | 4.85 | 3950 | 1.3187 | 0.8715 | 0.8103 | 0.7843 | 0.8516 |
| 0.0048 | 4.91 | 4000 | 1.3218 | 0.8713 | 0.8091 | 0.7842 | 0.8492 |
| 0.0046 | 4.97 | 4050 | 1.3220 | 0.8715 | 0.8103 | 0.7843 | 0.8516 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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