File size: 6,894 Bytes
036bf18 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 |
---
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: 20240402140914_big_aristotle
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. -->
# 20240402140914_big_aristotle
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0293
- Precision: 0.9731
- Recall: 0.9711
- F1: 0.9721
- Accuracy: 0.9891
## 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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 69
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 350
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0654 | 0.09 | 300 | 0.0495 | 0.9535 | 0.9462 | 0.9498 | 0.9808 |
| 0.0647 | 0.18 | 600 | 0.0489 | 0.9529 | 0.9470 | 0.9499 | 0.9807 |
| 0.0626 | 0.26 | 900 | 0.0486 | 0.9542 | 0.9448 | 0.9495 | 0.9807 |
| 0.0613 | 0.35 | 1200 | 0.0460 | 0.9544 | 0.9501 | 0.9522 | 0.9815 |
| 0.0623 | 0.44 | 1500 | 0.0469 | 0.9538 | 0.9493 | 0.9515 | 0.9814 |
| 0.0573 | 0.53 | 1800 | 0.0455 | 0.9552 | 0.9488 | 0.9520 | 0.9816 |
| 0.0569 | 0.61 | 2100 | 0.0447 | 0.9528 | 0.9540 | 0.9534 | 0.9819 |
| 0.0585 | 0.7 | 2400 | 0.0464 | 0.9566 | 0.9468 | 0.9516 | 0.9815 |
| 0.056 | 0.79 | 2700 | 0.0452 | 0.9555 | 0.9517 | 0.9536 | 0.9822 |
| 0.0564 | 0.88 | 3000 | 0.0430 | 0.9591 | 0.9512 | 0.9551 | 0.9828 |
| 0.0552 | 0.96 | 3300 | 0.0433 | 0.9548 | 0.9532 | 0.9540 | 0.9825 |
| 0.048 | 1.05 | 3600 | 0.0444 | 0.9579 | 0.9529 | 0.9554 | 0.9828 |
| 0.0483 | 1.14 | 3900 | 0.0415 | 0.9582 | 0.9553 | 0.9568 | 0.9831 |
| 0.0465 | 1.23 | 4200 | 0.0424 | 0.9622 | 0.9495 | 0.9558 | 0.9831 |
| 0.0465 | 1.31 | 4500 | 0.0415 | 0.9616 | 0.9514 | 0.9565 | 0.9835 |
| 0.0462 | 1.4 | 4800 | 0.0407 | 0.9588 | 0.9534 | 0.9561 | 0.9835 |
| 0.0467 | 1.49 | 5100 | 0.0403 | 0.9582 | 0.9581 | 0.9581 | 0.9836 |
| 0.0453 | 1.58 | 5400 | 0.0405 | 0.9636 | 0.9513 | 0.9574 | 0.9839 |
| 0.0446 | 1.66 | 5700 | 0.0383 | 0.9637 | 0.9555 | 0.9596 | 0.9847 |
| 0.0443 | 1.75 | 6000 | 0.0382 | 0.9596 | 0.9572 | 0.9584 | 0.9844 |
| 0.0432 | 1.84 | 6300 | 0.0373 | 0.9637 | 0.9573 | 0.9605 | 0.9847 |
| 0.0424 | 1.93 | 6600 | 0.0368 | 0.9674 | 0.9516 | 0.9594 | 0.9850 |
| 0.0364 | 2.01 | 6900 | 0.0361 | 0.9633 | 0.9570 | 0.9601 | 0.9851 |
| 0.0358 | 2.1 | 7200 | 0.0366 | 0.9618 | 0.9613 | 0.9615 | 0.9853 |
| 0.0359 | 2.19 | 7500 | 0.0370 | 0.9665 | 0.9561 | 0.9613 | 0.9852 |
| 0.036 | 2.28 | 7800 | 0.0360 | 0.9660 | 0.9564 | 0.9611 | 0.9853 |
| 0.0352 | 2.36 | 8100 | 0.0355 | 0.9658 | 0.9602 | 0.9630 | 0.9856 |
| 0.0354 | 2.45 | 8400 | 0.0354 | 0.9682 | 0.9579 | 0.9630 | 0.9860 |
| 0.0347 | 2.54 | 8700 | 0.0347 | 0.9694 | 0.9566 | 0.9630 | 0.9861 |
| 0.0347 | 2.63 | 9000 | 0.0340 | 0.9676 | 0.9597 | 0.9636 | 0.9864 |
| 0.0338 | 2.71 | 9300 | 0.0327 | 0.9682 | 0.9626 | 0.9654 | 0.9867 |
| 0.0338 | 2.8 | 9600 | 0.0334 | 0.9681 | 0.9627 | 0.9654 | 0.9865 |
| 0.033 | 2.89 | 9900 | 0.0325 | 0.9705 | 0.9613 | 0.9659 | 0.9870 |
| 0.0326 | 2.98 | 10200 | 0.0331 | 0.9686 | 0.9640 | 0.9663 | 0.9870 |
| 0.0264 | 3.06 | 10500 | 0.0352 | 0.9689 | 0.9651 | 0.9670 | 0.9871 |
| 0.0261 | 3.15 | 10800 | 0.0329 | 0.9698 | 0.9633 | 0.9666 | 0.9871 |
| 0.026 | 3.24 | 11100 | 0.0328 | 0.9672 | 0.9662 | 0.9667 | 0.9872 |
| 0.0261 | 3.33 | 11400 | 0.0333 | 0.9678 | 0.9681 | 0.9680 | 0.9872 |
| 0.0264 | 3.41 | 11700 | 0.0326 | 0.9689 | 0.9676 | 0.9682 | 0.9875 |
| 0.0258 | 3.5 | 12000 | 0.0313 | 0.9717 | 0.9643 | 0.9680 | 0.9877 |
| 0.0254 | 3.59 | 12300 | 0.0307 | 0.9691 | 0.9675 | 0.9683 | 0.9880 |
| 0.0249 | 3.68 | 12600 | 0.0304 | 0.9720 | 0.9666 | 0.9693 | 0.9881 |
| 0.025 | 3.76 | 12900 | 0.0300 | 0.9686 | 0.9680 | 0.9683 | 0.9882 |
| 0.0242 | 3.85 | 13200 | 0.0297 | 0.9682 | 0.9682 | 0.9682 | 0.9881 |
| 0.0246 | 3.94 | 13500 | 0.0291 | 0.9725 | 0.9655 | 0.9690 | 0.9883 |
| 0.0184 | 4.03 | 13800 | 0.0320 | 0.9712 | 0.9678 | 0.9695 | 0.9882 |
| 0.0186 | 4.11 | 14100 | 0.0311 | 0.9703 | 0.9688 | 0.9696 | 0.9883 |
| 0.0183 | 4.2 | 14400 | 0.0319 | 0.9718 | 0.9696 | 0.9707 | 0.9886 |
| 0.0181 | 4.29 | 14700 | 0.0312 | 0.9730 | 0.9673 | 0.9702 | 0.9885 |
| 0.0181 | 4.38 | 15000 | 0.0308 | 0.9694 | 0.9698 | 0.9696 | 0.9885 |
| 0.0178 | 4.47 | 15300 | 0.0302 | 0.9727 | 0.9698 | 0.9712 | 0.9888 |
| 0.0175 | 4.55 | 15600 | 0.0300 | 0.9729 | 0.9705 | 0.9717 | 0.9889 |
| 0.0171 | 4.64 | 15900 | 0.0300 | 0.9725 | 0.9713 | 0.9719 | 0.9890 |
| 0.017 | 4.73 | 16200 | 0.0296 | 0.9712 | 0.9710 | 0.9711 | 0.9888 |
| 0.017 | 4.82 | 16500 | 0.0295 | 0.9726 | 0.9707 | 0.9717 | 0.9890 |
| 0.0168 | 4.9 | 16800 | 0.0297 | 0.9730 | 0.9711 | 0.9721 | 0.9891 |
| 0.0166 | 4.99 | 17100 | 0.0293 | 0.9731 | 0.9711 | 0.9721 | 0.9891 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.0a0+6a974be
- Datasets 2.18.0
- Tokenizers 0.15.2
|