metadata
tags:
- generated_from_trainer
model-index:
- name: chemical-bert-uncased-finetuned-cust-c1-cust
results: []
chemical-bert-uncased-finetuned-cust-c1-cust
This model is a fine-tuned version of shafin/chemical-bert-uncased-finetuned-cust on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5420
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.96 | 1.0 | 63 | 1.6719 |
| 1.7095 | 2.0 | 126 | 1.5305 |
| 1.5634 | 3.0 | 189 | 1.2972 |
| 1.4785 | 4.0 | 252 | 1.3354 |
| 1.3991 | 5.0 | 315 | 1.2542 |
| 1.3482 | 6.0 | 378 | 1.1870 |
| 1.2984 | 7.0 | 441 | 1.1844 |
| 1.2589 | 8.0 | 504 | 1.1262 |
| 1.1762 | 9.0 | 567 | 1.1176 |
| 1.1724 | 10.0 | 630 | 1.0312 |
| 1.1222 | 11.0 | 693 | 1.0113 |
| 1.1021 | 12.0 | 756 | 1.0518 |
| 1.0646 | 13.0 | 819 | 1.0433 |
| 1.0273 | 14.0 | 882 | 0.9634 |
| 1.0187 | 15.0 | 945 | 0.9299 |
| 0.9854 | 16.0 | 1008 | 0.9458 |
| 0.9799 | 17.0 | 1071 | 0.9733 |
| 0.95 | 18.0 | 1134 | 0.9169 |
| 0.934 | 19.0 | 1197 | 0.9246 |
| 0.907 | 20.0 | 1260 | 0.8939 |
| 0.8974 | 21.0 | 1323 | 0.8575 |
| 0.8749 | 22.0 | 1386 | 0.8513 |
| 0.8526 | 23.0 | 1449 | 0.8089 |
| 0.8359 | 24.0 | 1512 | 0.8600 |
| 0.8292 | 25.0 | 1575 | 0.8517 |
| 0.8263 | 26.0 | 1638 | 0.8293 |
| 0.8033 | 27.0 | 1701 | 0.7747 |
| 0.7999 | 28.0 | 1764 | 0.8169 |
| 0.7778 | 29.0 | 1827 | 0.7981 |
| 0.7574 | 30.0 | 1890 | 0.7457 |
| 0.7581 | 31.0 | 1953 | 0.7504 |
| 0.7404 | 32.0 | 2016 | 0.7637 |
| 0.7332 | 33.0 | 2079 | 0.7902 |
| 0.7314 | 34.0 | 2142 | 0.7185 |
| 0.7209 | 35.0 | 2205 | 0.7534 |
| 0.6902 | 36.0 | 2268 | 0.7334 |
| 0.6973 | 37.0 | 2331 | 0.7069 |
| 0.687 | 38.0 | 2394 | 0.6820 |
| 0.6658 | 39.0 | 2457 | 0.7155 |
| 0.6697 | 40.0 | 2520 | 0.7149 |
| 0.6584 | 41.0 | 2583 | 0.7413 |
| 0.6638 | 42.0 | 2646 | 0.7245 |
| 0.6282 | 43.0 | 2709 | 0.7177 |
| 0.6418 | 44.0 | 2772 | 0.6653 |
| 0.6323 | 45.0 | 2835 | 0.7715 |
| 0.6256 | 46.0 | 2898 | 0.7269 |
| 0.6109 | 47.0 | 2961 | 0.6744 |
| 0.6133 | 48.0 | 3024 | 0.6816 |
| 0.595 | 49.0 | 3087 | 0.6969 |
| 0.6058 | 50.0 | 3150 | 0.6965 |
| 0.5961 | 51.0 | 3213 | 0.6988 |
| 0.587 | 52.0 | 3276 | 0.6727 |
| 0.5861 | 53.0 | 3339 | 0.6327 |
| 0.5758 | 54.0 | 3402 | 0.6538 |
| 0.5692 | 55.0 | 3465 | 0.6612 |
| 0.567 | 56.0 | 3528 | 0.5989 |
| 0.5514 | 57.0 | 3591 | 0.6776 |
| 0.5526 | 58.0 | 3654 | 0.6440 |
| 0.556 | 59.0 | 3717 | 0.6682 |
| 0.5476 | 60.0 | 3780 | 0.6254 |
| 0.536 | 61.0 | 3843 | 0.6239 |
| 0.526 | 62.0 | 3906 | 0.6606 |
| 0.532 | 63.0 | 3969 | 0.6565 |
| 0.5189 | 64.0 | 4032 | 0.6586 |
| 0.5075 | 65.0 | 4095 | 0.6286 |
| 0.5131 | 66.0 | 4158 | 0.6646 |
| 0.498 | 67.0 | 4221 | 0.6486 |
| 0.4979 | 68.0 | 4284 | 0.6313 |
| 0.4885 | 69.0 | 4347 | 0.6419 |
| 0.4875 | 70.0 | 4410 | 0.6313 |
| 0.4904 | 71.0 | 4473 | 0.6602 |
| 0.4712 | 72.0 | 4536 | 0.6200 |
| 0.4798 | 73.0 | 4599 | 0.5912 |
| 0.4802 | 74.0 | 4662 | 0.6001 |
| 0.4704 | 75.0 | 4725 | 0.6303 |
| 0.4709 | 76.0 | 4788 | 0.5871 |
| 0.465 | 77.0 | 4851 | 0.6344 |
| 0.4651 | 78.0 | 4914 | 0.6030 |
| 0.4501 | 79.0 | 4977 | 0.5998 |
| 0.4584 | 80.0 | 5040 | 0.5926 |
| 0.4651 | 81.0 | 5103 | 0.6134 |
| 0.438 | 82.0 | 5166 | 0.6254 |
| 0.448 | 83.0 | 5229 | 0.6260 |
| 0.4295 | 84.0 | 5292 | 0.5866 |
| 0.434 | 85.0 | 5355 | 0.5740 |
| 0.4261 | 86.0 | 5418 | 0.5691 |
| 0.4312 | 87.0 | 5481 | 0.6243 |
| 0.4289 | 88.0 | 5544 | 0.5781 |
| 0.4255 | 89.0 | 5607 | 0.6226 |
| 0.4254 | 90.0 | 5670 | 0.5538 |
| 0.4231 | 91.0 | 5733 | 0.5874 |
| 0.4107 | 92.0 | 5796 | 0.6054 |
| 0.4082 | 93.0 | 5859 | 0.5898 |
| 0.4144 | 94.0 | 5922 | 0.5826 |
| 0.4225 | 95.0 | 5985 | 0.5501 |
| 0.3964 | 96.0 | 6048 | 0.5886 |
| 0.3972 | 97.0 | 6111 | 0.5831 |
| 0.4165 | 98.0 | 6174 | 0.5164 |
| 0.4024 | 99.0 | 6237 | 0.5714 |
| 0.4013 | 100.0 | 6300 | 0.5734 |
| 0.3933 | 101.0 | 6363 | 0.5727 |
| 0.3821 | 102.0 | 6426 | 0.5985 |
| 0.3904 | 103.0 | 6489 | 0.5571 |
| 0.3965 | 104.0 | 6552 | 0.5837 |
| 0.3789 | 105.0 | 6615 | 0.5989 |
| 0.3733 | 106.0 | 6678 | 0.5405 |
| 0.3907 | 107.0 | 6741 | 0.6059 |
| 0.3794 | 108.0 | 6804 | 0.5602 |
| 0.3689 | 109.0 | 6867 | 0.5590 |
| 0.3603 | 110.0 | 6930 | 0.5886 |
| 0.3747 | 111.0 | 6993 | 0.5294 |
| 0.3667 | 112.0 | 7056 | 0.5759 |
| 0.3754 | 113.0 | 7119 | 0.5821 |
| 0.3676 | 114.0 | 7182 | 0.5653 |
| 0.3524 | 115.0 | 7245 | 0.5537 |
| 0.3624 | 116.0 | 7308 | 0.5523 |
| 0.3527 | 117.0 | 7371 | 0.5799 |
| 0.3588 | 118.0 | 7434 | 0.6346 |
| 0.3539 | 119.0 | 7497 | 0.5116 |
| 0.3553 | 120.0 | 7560 | 0.5716 |
| 0.3483 | 121.0 | 7623 | 0.5721 |
| 0.3625 | 122.0 | 7686 | 0.5393 |
| 0.3354 | 123.0 | 7749 | 0.5800 |
| 0.3392 | 124.0 | 7812 | 0.5389 |
| 0.344 | 125.0 | 7875 | 0.5455 |
| 0.3451 | 126.0 | 7938 | 0.5428 |
| 0.3374 | 127.0 | 8001 | 0.5580 |
| 0.3428 | 128.0 | 8064 | 0.5339 |
| 0.3386 | 129.0 | 8127 | 0.5447 |
| 0.3318 | 130.0 | 8190 | 0.5738 |
| 0.3388 | 131.0 | 8253 | 0.5667 |
| 0.3335 | 132.0 | 8316 | 0.5407 |
| 0.3383 | 133.0 | 8379 | 0.5679 |
| 0.3299 | 134.0 | 8442 | 0.5846 |
| 0.327 | 135.0 | 8505 | 0.5511 |
| 0.3354 | 136.0 | 8568 | 0.5649 |
| 0.32 | 137.0 | 8631 | 0.5358 |
| 0.3265 | 138.0 | 8694 | 0.5528 |
| 0.319 | 139.0 | 8757 | 0.5926 |
| 0.3304 | 140.0 | 8820 | 0.5531 |
| 0.3191 | 141.0 | 8883 | 0.5379 |
| 0.3298 | 142.0 | 8946 | 0.5468 |
| 0.3134 | 143.0 | 9009 | 0.5623 |
| 0.3186 | 144.0 | 9072 | 0.5162 |
| 0.3179 | 145.0 | 9135 | 0.5570 |
| 0.3175 | 146.0 | 9198 | 0.5379 |
| 0.3051 | 147.0 | 9261 | 0.5437 |
| 0.312 | 148.0 | 9324 | 0.5301 |
| 0.3093 | 149.0 | 9387 | 0.5393 |
| 0.3227 | 150.0 | 9450 | 0.5531 |
| 0.3125 | 151.0 | 9513 | 0.5794 |
| 0.3162 | 152.0 | 9576 | 0.5677 |
| 0.3006 | 153.0 | 9639 | 0.5668 |
| 0.3011 | 154.0 | 9702 | 0.5797 |
| 0.3208 | 155.0 | 9765 | 0.5450 |
| 0.3048 | 156.0 | 9828 | 0.5465 |
| 0.3092 | 157.0 | 9891 | 0.5358 |
| 0.3125 | 158.0 | 9954 | 0.5043 |
| 0.3083 | 159.0 | 10017 | 0.5321 |
| 0.3 | 160.0 | 10080 | 0.5526 |
| 0.2968 | 161.0 | 10143 | 0.5324 |
| 0.3068 | 162.0 | 10206 | 0.5471 |
| 0.3129 | 163.0 | 10269 | 0.5575 |
| 0.3061 | 164.0 | 10332 | 0.5796 |
| 0.2943 | 165.0 | 10395 | 0.5544 |
| 0.2967 | 166.0 | 10458 | 0.5422 |
| 0.2959 | 167.0 | 10521 | 0.5149 |
| 0.2987 | 168.0 | 10584 | 0.5685 |
| 0.3045 | 169.0 | 10647 | 0.5176 |
| 0.2975 | 170.0 | 10710 | 0.5044 |
| 0.2948 | 171.0 | 10773 | 0.5264 |
| 0.3 | 172.0 | 10836 | 0.5174 |
| 0.2967 | 173.0 | 10899 | 0.5658 |
| 0.2873 | 174.0 | 10962 | 0.4988 |
| 0.2939 | 175.0 | 11025 | 0.5512 |
| 0.2954 | 176.0 | 11088 | 0.5139 |
| 0.301 | 177.0 | 11151 | 0.6007 |
| 0.2948 | 178.0 | 11214 | 0.5167 |
| 0.2898 | 179.0 | 11277 | 0.5443 |
| 0.2869 | 180.0 | 11340 | 0.5544 |
| 0.2973 | 181.0 | 11403 | 0.5644 |
| 0.2985 | 182.0 | 11466 | 0.5153 |
| 0.2904 | 183.0 | 11529 | 0.5561 |
| 0.2872 | 184.0 | 11592 | 0.5610 |
| 0.2894 | 185.0 | 11655 | 0.5511 |
| 0.297 | 186.0 | 11718 | 0.5408 |
| 0.2904 | 187.0 | 11781 | 0.5574 |
| 0.2818 | 188.0 | 11844 | 0.5182 |
| 0.2873 | 189.0 | 11907 | 0.5425 |
| 0.2973 | 190.0 | 11970 | 0.5198 |
| 0.2913 | 191.0 | 12033 | 0.5119 |
| 0.2931 | 192.0 | 12096 | 0.5585 |
| 0.2859 | 193.0 | 12159 | 0.5368 |
| 0.2853 | 194.0 | 12222 | 0.5274 |
| 0.294 | 195.0 | 12285 | 0.5685 |
| 0.2885 | 196.0 | 12348 | 0.5581 |
| 0.295 | 197.0 | 12411 | 0.4987 |
| 0.2807 | 198.0 | 12474 | 0.5168 |
| 0.289 | 199.0 | 12537 | 0.5284 |
| 0.2893 | 200.0 | 12600 | 0.5420 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2