chemical-bert-uncased-finetuned-cust-c2
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: 0.5768
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 |
|---|---|---|---|
| 1.9422 | 1.0 | 63 | 1.6236 |
| 1.6662 | 2.0 | 126 | 1.5136 |
| 1.5299 | 3.0 | 189 | 1.4435 |
| 1.4542 | 4.0 | 252 | 1.2997 |
| 1.374 | 5.0 | 315 | 1.2431 |
| 1.2944 | 6.0 | 378 | 1.1990 |
| 1.2439 | 7.0 | 441 | 1.1733 |
| 1.2304 | 8.0 | 504 | 1.1494 |
| 1.1495 | 9.0 | 567 | 1.1410 |
| 1.1325 | 10.0 | 630 | 1.1208 |
| 1.0798 | 11.0 | 693 | 1.0691 |
| 1.074 | 12.0 | 756 | 1.0918 |
| 1.0422 | 13.0 | 819 | 1.0823 |
| 1.0124 | 14.0 | 882 | 1.0101 |
| 1.0172 | 15.0 | 945 | 0.9742 |
| 0.9821 | 16.0 | 1008 | 0.9740 |
| 0.9347 | 17.0 | 1071 | 0.9711 |
| 0.9193 | 18.0 | 1134 | 0.9291 |
| 0.9229 | 19.0 | 1197 | 0.9317 |
| 0.8751 | 20.0 | 1260 | 0.9331 |
| 0.8914 | 21.0 | 1323 | 0.9137 |
| 0.8686 | 22.0 | 1386 | 0.9209 |
| 0.8482 | 23.0 | 1449 | 0.8724 |
| 0.8201 | 24.0 | 1512 | 0.8512 |
| 0.8131 | 25.0 | 1575 | 0.8753 |
| 0.8123 | 26.0 | 1638 | 0.8651 |
| 0.8046 | 27.0 | 1701 | 0.8374 |
| 0.7668 | 28.0 | 1764 | 0.8981 |
| 0.7732 | 29.0 | 1827 | 0.8691 |
| 0.7567 | 30.0 | 1890 | 0.7845 |
| 0.7465 | 31.0 | 1953 | 0.8493 |
| 0.7451 | 32.0 | 2016 | 0.8270 |
| 0.7211 | 33.0 | 2079 | 0.8148 |
| 0.7006 | 34.0 | 2142 | 0.8163 |
| 0.7107 | 35.0 | 2205 | 0.7866 |
| 0.6889 | 36.0 | 2268 | 0.7712 |
| 0.674 | 37.0 | 2331 | 0.7762 |
| 0.6847 | 38.0 | 2394 | 0.7583 |
| 0.6639 | 39.0 | 2457 | 0.7800 |
| 0.6615 | 40.0 | 2520 | 0.8270 |
| 0.6566 | 41.0 | 2583 | 0.7851 |
| 0.6364 | 42.0 | 2646 | 0.7645 |
| 0.6261 | 43.0 | 2709 | 0.7044 |
| 0.6338 | 44.0 | 2772 | 0.7952 |
| 0.6315 | 45.0 | 2835 | 0.7439 |
| 0.6122 | 46.0 | 2898 | 0.7566 |
| 0.5941 | 47.0 | 2961 | 0.7124 |
| 0.6076 | 48.0 | 3024 | 0.7591 |
| 0.59 | 49.0 | 3087 | 0.7473 |
| 0.5838 | 50.0 | 3150 | 0.6961 |
| 0.5931 | 51.0 | 3213 | 0.7604 |
| 0.5847 | 52.0 | 3276 | 0.7260 |
| 0.5691 | 53.0 | 3339 | 0.7309 |
| 0.5778 | 54.0 | 3402 | 0.7200 |
| 0.5464 | 55.0 | 3465 | 0.7014 |
| 0.5592 | 56.0 | 3528 | 0.7567 |
| 0.555 | 57.0 | 3591 | 0.7062 |
| 0.5436 | 58.0 | 3654 | 0.7284 |
| 0.5328 | 59.0 | 3717 | 0.6896 |
| 0.5397 | 60.0 | 3780 | 0.7041 |
| 0.5263 | 61.0 | 3843 | 0.7029 |
| 0.5181 | 62.0 | 3906 | 0.7223 |
| 0.5166 | 63.0 | 3969 | 0.7043 |
| 0.5066 | 64.0 | 4032 | 0.6723 |
| 0.5115 | 65.0 | 4095 | 0.6871 |
| 0.4956 | 66.0 | 4158 | 0.6818 |
| 0.5006 | 67.0 | 4221 | 0.7075 |
| 0.4837 | 68.0 | 4284 | 0.6686 |
| 0.4874 | 69.0 | 4347 | 0.6943 |
| 0.4808 | 70.0 | 4410 | 0.6584 |
| 0.4775 | 71.0 | 4473 | 0.6954 |
| 0.4776 | 72.0 | 4536 | 0.6741 |
| 0.4773 | 73.0 | 4599 | 0.6591 |
| 0.4699 | 74.0 | 4662 | 0.7000 |
| 0.4779 | 75.0 | 4725 | 0.6829 |
| 0.4543 | 76.0 | 4788 | 0.6839 |
| 0.4641 | 77.0 | 4851 | 0.6444 |
| 0.4495 | 78.0 | 4914 | 0.6604 |
| 0.4489 | 79.0 | 4977 | 0.6713 |
| 0.4394 | 80.0 | 5040 | 0.6905 |
| 0.4461 | 81.0 | 5103 | 0.6879 |
| 0.4386 | 82.0 | 5166 | 0.6458 |
| 0.4529 | 83.0 | 5229 | 0.6306 |
| 0.4261 | 84.0 | 5292 | 0.6291 |
| 0.4306 | 85.0 | 5355 | 0.6518 |
| 0.4428 | 86.0 | 5418 | 0.6456 |
| 0.4336 | 87.0 | 5481 | 0.6686 |
| 0.4105 | 88.0 | 5544 | 0.6735 |
| 0.4281 | 89.0 | 5607 | 0.6645 |
| 0.4172 | 90.0 | 5670 | 0.6527 |
| 0.4037 | 91.0 | 5733 | 0.6004 |
| 0.4137 | 92.0 | 5796 | 0.6643 |
| 0.4135 | 93.0 | 5859 | 0.6783 |
| 0.3988 | 94.0 | 5922 | 0.6687 |
| 0.4172 | 95.0 | 5985 | 0.6486 |
| 0.3819 | 96.0 | 6048 | 0.6466 |
| 0.3938 | 97.0 | 6111 | 0.5946 |
| 0.4053 | 98.0 | 6174 | 0.6146 |
| 0.3988 | 99.0 | 6237 | 0.6166 |
| 0.3798 | 100.0 | 6300 | 0.6383 |
| 0.386 | 101.0 | 6363 | 0.6631 |
| 0.3962 | 102.0 | 6426 | 0.6298 |
| 0.399 | 103.0 | 6489 | 0.6251 |
| 0.3851 | 104.0 | 6552 | 0.6339 |
| 0.3767 | 105.0 | 6615 | 0.6610 |
| 0.3756 | 106.0 | 6678 | 0.6292 |
| 0.375 | 107.0 | 6741 | 0.6201 |
| 0.3648 | 108.0 | 6804 | 0.6384 |
| 0.3664 | 109.0 | 6867 | 0.6046 |
| 0.3679 | 110.0 | 6930 | 0.6169 |
| 0.368 | 111.0 | 6993 | 0.6450 |
| 0.3605 | 112.0 | 7056 | 0.6518 |
| 0.3675 | 113.0 | 7119 | 0.6082 |
| 0.3559 | 114.0 | 7182 | 0.6232 |
| 0.3563 | 115.0 | 7245 | 0.6438 |
| 0.3664 | 116.0 | 7308 | 0.6381 |
| 0.3662 | 117.0 | 7371 | 0.6412 |
| 0.3596 | 118.0 | 7434 | 0.6631 |
| 0.3447 | 119.0 | 7497 | 0.6065 |
| 0.3421 | 120.0 | 7560 | 0.6072 |
| 0.347 | 121.0 | 7623 | 0.5787 |
| 0.3474 | 122.0 | 7686 | 0.6343 |
| 0.3426 | 123.0 | 7749 | 0.6114 |
| 0.3418 | 124.0 | 7812 | 0.6084 |
| 0.3485 | 125.0 | 7875 | 0.6188 |
| 0.3411 | 126.0 | 7938 | 0.6112 |
| 0.3371 | 127.0 | 8001 | 0.5991 |
| 0.3353 | 128.0 | 8064 | 0.5861 |
| 0.3318 | 129.0 | 8127 | 0.6419 |
| 0.3417 | 130.0 | 8190 | 0.6272 |
| 0.3235 | 131.0 | 8253 | 0.6293 |
| 0.3363 | 132.0 | 8316 | 0.6017 |
| 0.3358 | 133.0 | 8379 | 0.5816 |
| 0.3273 | 134.0 | 8442 | 0.6384 |
| 0.3277 | 135.0 | 8505 | 0.6063 |
| 0.3336 | 136.0 | 8568 | 0.6482 |
| 0.3205 | 137.0 | 8631 | 0.6428 |
| 0.3136 | 138.0 | 8694 | 0.6322 |
| 0.3212 | 139.0 | 8757 | 0.6218 |
| 0.3275 | 140.0 | 8820 | 0.6328 |
| 0.3227 | 141.0 | 8883 | 0.6406 |
| 0.3166 | 142.0 | 8946 | 0.6317 |
| 0.3111 | 143.0 | 9009 | 0.6308 |
| 0.309 | 144.0 | 9072 | 0.5972 |
| 0.316 | 145.0 | 9135 | 0.6229 |
| 0.3163 | 146.0 | 9198 | 0.6244 |
| 0.3125 | 147.0 | 9261 | 0.6195 |
| 0.3164 | 148.0 | 9324 | 0.5676 |
| 0.3151 | 149.0 | 9387 | 0.6225 |
| 0.3014 | 150.0 | 9450 | 0.6044 |
| 0.3106 | 151.0 | 9513 | 0.6262 |
| 0.3065 | 152.0 | 9576 | 0.5927 |
| 0.2982 | 153.0 | 9639 | 0.6402 |
| 0.3054 | 154.0 | 9702 | 0.6329 |
| 0.3172 | 155.0 | 9765 | 0.6227 |
| 0.3005 | 156.0 | 9828 | 0.5882 |
| 0.3174 | 157.0 | 9891 | 0.6049 |
| 0.3023 | 158.0 | 9954 | 0.5990 |
| 0.3013 | 159.0 | 10017 | 0.5909 |
| 0.3044 | 160.0 | 10080 | 0.6317 |
| 0.298 | 161.0 | 10143 | 0.6237 |
| 0.2984 | 162.0 | 10206 | 0.6074 |
| 0.3075 | 163.0 | 10269 | 0.5746 |
| 0.2921 | 164.0 | 10332 | 0.5633 |
| 0.3014 | 165.0 | 10395 | 0.6034 |
| 0.297 | 166.0 | 10458 | 0.6420 |
| 0.2936 | 167.0 | 10521 | 0.6206 |
| 0.2946 | 168.0 | 10584 | 0.5869 |
| 0.2923 | 169.0 | 10647 | 0.5898 |
| 0.2936 | 170.0 | 10710 | 0.5810 |
| 0.2968 | 171.0 | 10773 | 0.5888 |
| 0.2863 | 172.0 | 10836 | 0.6124 |
| 0.3038 | 173.0 | 10899 | 0.5823 |
| 0.2845 | 174.0 | 10962 | 0.6187 |
| 0.2847 | 175.0 | 11025 | 0.5749 |
| 0.2984 | 176.0 | 11088 | 0.5900 |
| 0.297 | 177.0 | 11151 | 0.6243 |
| 0.2914 | 178.0 | 11214 | 0.5839 |
| 0.2904 | 179.0 | 11277 | 0.6085 |
| 0.2946 | 180.0 | 11340 | 0.6257 |
| 0.2934 | 181.0 | 11403 | 0.5918 |
| 0.2858 | 182.0 | 11466 | 0.6072 |
| 0.2912 | 183.0 | 11529 | 0.6394 |
| 0.2771 | 184.0 | 11592 | 0.5962 |
| 0.289 | 185.0 | 11655 | 0.6039 |
| 0.2801 | 186.0 | 11718 | 0.5819 |
| 0.2875 | 187.0 | 11781 | 0.6264 |
| 0.2875 | 188.0 | 11844 | 0.6156 |
| 0.2853 | 189.0 | 11907 | 0.5968 |
| 0.2874 | 190.0 | 11970 | 0.6028 |
| 0.2844 | 191.0 | 12033 | 0.5767 |
| 0.2855 | 192.0 | 12096 | 0.6124 |
| 0.2879 | 193.0 | 12159 | 0.5856 |
| 0.2801 | 194.0 | 12222 | 0.6163 |
| 0.2902 | 195.0 | 12285 | 0.5939 |
| 0.2879 | 196.0 | 12348 | 0.5780 |
| 0.2946 | 197.0 | 12411 | 0.6052 |
| 0.2801 | 198.0 | 12474 | 0.6251 |
| 0.287 | 199.0 | 12537 | 0.5839 |
| 0.2864 | 200.0 | 12600 | 0.5768 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2
- Downloads last month
- 8
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.