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flanT5_large_Fact_U

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metadata
library_name: transformers
license: apache-2.0
base_model: google/flan-t5-large
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
model-index:
  - name: flanT5_large_Fact_U
    results: []

flanT5_large_Fact_U

This model is a fine-tuned version of google/flan-t5-large on the None dataset. It achieves the following results on the evaluation set:

  • Accuracy: 0.7812
  • F1 score: 0.7842
  • Precision: 0.8048
  • Recall: 0.7647
  • Loss: 1.5959

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: 0.0001
  • train_batch_size: 1
  • eval_batch_size: 1
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 5

Training results

Training Loss Epoch Step Accuracy F1 score Precision Recall Validation Loss
1.2434 0.0314 200 0.6059 0.6974 0.5805 0.8733 0.7545
1.4323 0.0628 400 0.6 0.6970 0.575 0.8846 1.7199
1.253 0.0941 600 0.6059 0.6957 0.5812 0.8665 1.3828
1.2986 0.1255 800 0.48 0.0 0.0 0.0 2.4072
1.2843 0.1569 1000 0.6553 0.5925 0.7690 0.4819 1.2474
1.2245 0.1883 1200 0.5435 0.2271 0.95 0.1290 1.3841
1.2389 0.2197 1400 0.6706 0.6923 0.6731 0.7127 0.8993
1.132 0.2511 1600 0.6271 0.6846 0.6110 0.7783 0.6845
1.0836 0.2824 1800 0.6824 0.6574 0.7486 0.5860 1.1694
1.2434 0.3138 2000 0.6788 0.6422 0.7632 0.5543 1.4787
1.1196 0.3452 2200 0.6694 0.6659 0.7018 0.6335 1.5004
1.5791 0.3766 2400 0.6376 0.5157 0.8454 0.3710 1.1289
1.3035 0.4080 2600 0.6859 0.6877 0.7119 0.6652 1.0136
1.1401 0.4394 2800 0.6753 0.6443 0.7485 0.5656 1.2340
0.9518 0.4707 3000 0.7024 0.6810 0.7692 0.6109 1.2197
1.1623 0.5021 3200 0.6788 0.6807 0.7046 0.6584 1.2827
1.1316 0.5335 3400 0.6659 0.7183 0.6396 0.8190 1.5077
1.2599 0.5649 3600 0.6341 0.5071 0.8466 0.3620 0.8272
0.9866 0.5963 3800 0.6647 0.6939 0.6605 0.7308 1.4574
1.147 0.6276 4000 0.6824 0.64 0.7792 0.5430 1.2933
1.0307 0.6590 4200 0.6482 0.5795 0.7658 0.4661 1.1586
1.0616 0.6904 4400 0.6976 0.6877 0.7428 0.6403 1.2668
1.0724 0.7218 4600 0.6447 0.6225 0.6955 0.5633 1.1130
0.9499 0.7532 4800 0.7188 0.7046 0.7766 0.6448 1.1635
1.1302 0.7846 5000 0.7118 0.7357 0.7031 0.7715 1.2608
1.1921 0.8159 5200 0.7094 0.6754 0.8056 0.5814 1.1742
0.9532 0.8473 5400 0.7071 0.6860 0.7749 0.6154 1.1589
0.783 0.8787 5600 0.7 0.6577 0.8086 0.5543 1.3256
0.9835 0.9101 5800 0.7282 0.7166 0.7828 0.6606 1.1383
0.9898 0.9415 6000 0.7141 0.7158 0.7409 0.6923 1.0662
0.9768 0.9729 6200 0.7059 0.6711 0.8019 0.5769 1.1941
1.043 1.0042 6400 0.6729 0.5838 0.8628 0.4412 1.2302
0.9531 1.0356 6600 0.7106 0.7 0.7593 0.6493 1.1304
1.0585 1.0670 6800 0.7294 0.7132 0.7944 0.6471 1.0234
0.8862 1.0984 7000 0.6953 0.6230 0.8735 0.4842 1.1941
0.8721 1.1298 7200 0.7376 0.7271 0.792 0.6719 0.9352
0.8678 1.1611 7400 0.7388 0.7533 0.7402 0.7670 1.0473
0.7617 1.1925 7600 0.7294 0.7522 0.7181 0.7896 1.3020
1.0394 1.2239 7800 0.7212 0.7019 0.7904 0.6312 1.0322
0.822 1.2553 8000 0.7388 0.7266 0.7973 0.6674 1.0980
0.8406 1.2867 8200 0.7118 0.7357 0.7031 0.7715 1.4589
0.7059 1.3181 8400 0.7306 0.6999 0.8318 0.6041 1.0655
0.8649 1.3494 8600 0.7424 0.7266 0.8106 0.6584 0.9708
0.7142 1.3808 8800 0.7553 0.7419 0.8214 0.6765 1.1603
0.9057 1.4122 9000 0.76 0.7431 0.8381 0.6674 0.9389
0.9312 1.4436 9200 0.7553 0.7615 0.7721 0.7511 1.0568
0.8459 1.4750 9400 0.7459 0.7372 0.7974 0.6855 1.1646
0.8427 1.5064 9600 0.7459 0.7293 0.8174 0.6584 1.0133
0.7245 1.5377 9800 0.7341 0.6861 0.8885 0.5588 1.1397
0.6386 1.5691 10000 0.7294 0.6742 0.9015 0.5385 1.1112
0.7513 1.6005 10200 0.7671 0.7648 0.805 0.7285 0.9403
0.828 1.6319 10400 0.76 0.7639 0.7820 0.7466 0.9412
0.8393 1.6633 10600 0.7553 0.7219 0.8824 0.6109 0.9359
0.8679 1.6946 10800 0.7588 0.7402 0.8415 0.6606 0.8979
0.6735 1.7260 11000 0.7588 0.7285 0.8786 0.6222 1.0666
0.8702 1.7574 11200 0.7576 0.7553 0.795 0.7195 0.9554
0.7435 1.7888 11400 0.7588 0.7497 0.8143 0.6946 1.0937
0.8796 1.8202 11600 0.7824 0.7768 0.8320 0.7285 0.9257
0.6257 1.8516 11800 0.7659 0.7588 0.8172 0.7081 0.9606
0.8589 1.8829 12000 0.7659 0.7484 0.8481 0.6697 0.9013
0.865 1.9143 12200 0.7612 0.7717 0.7673 0.7760 1.0734
0.8068 1.9457 12400 0.76 0.7431 0.8381 0.6674 0.9214
0.6212 1.9771 12600 0.7706 0.7535 0.8539 0.6742 1.0116
0.7657 2.0085 12800 0.7718 0.7532 0.8605 0.6697 0.9830
0.6631 2.0399 13000 0.7776 0.7810 0.8005 0.7624 1.0075
0.3003 2.0712 13200 0.7812 0.7748 0.8333 0.7240 1.1456
0.5982 2.1026 13400 0.7753 0.7633 0.8438 0.6968 1.0728
0.4828 2.1340 13600 0.7753 0.7718 0.8177 0.7308 1.0474
0.5463 2.1654 13800 0.7776 0.7726 0.8252 0.7262 1.0521
0.5429 2.1968 14000 0.7706 0.7590 0.8365 0.6946 1.0990
0.7112 2.2282 14200 0.7729 0.7578 0.8507 0.6833 1.1072
0.4816 2.2595 14400 0.7753 0.7685 0.8277 0.7172 1.1528
0.7882 2.2909 14600 0.7765 0.7722 0.8214 0.7285 0.9670
0.5265 2.3223 14800 0.7765 0.7694 0.8298 0.7172 1.0724
0.6116 2.3537 15000 0.7776 0.7742 0.8203 0.7330 1.0316
0.575 2.3851 15200 0.7741 0.7624 0.8415 0.6968 1.1125
0.5599 2.4164 15400 0.7765 0.7754 0.8119 0.7421 1.0327
0.5821 2.4478 15600 0.7776 0.7784 0.8078 0.7511 1.0655
0.4777 2.4792 15800 0.7835 0.7880 0.8028 0.7738 1.1187
0.432 2.5106 16000 0.7788 0.7740 0.8256 0.7285 1.1973
0.4385 2.5420 16200 0.7729 0.7737 0.8029 0.7466 1.2155
0.6103 2.5734 16400 0.78 0.7771 0.8212 0.7376 1.0527
0.4618 2.6047 16600 0.78 0.7787 0.8164 0.7443 1.1377
0.471 2.6361 16800 0.7788 0.7814 0.8038 0.7602 1.1468
0.6206 2.6675 17000 0.7765 0.7791 0.8014 0.7579 1.1048
0.5869 2.6989 17200 0.7776 0.7850 0.7895 0.7805 1.1343
0.5647 2.7303 17400 0.7859 0.7849 0.8218 0.7511 1.0843
0.5527 2.7617 17600 0.7847 0.7875 0.8091 0.7670 1.0834
0.8013 2.7930 17800 0.7894 0.7926 0.8124 0.7738 0.9898
0.5232 2.8244 18000 0.7859 0.7884 0.8110 0.7670 1.0052
0.617 2.8558 18200 0.7824 0.7821 0.8157 0.7511 1.0083
0.5093 2.8872 18400 0.7835 0.7810 0.8241 0.7421 1.0510
0.5099 2.9186 18600 0.78 0.7797 0.8133 0.7489 1.0758
0.6239 2.9499 18800 0.7812 0.7801 0.8168 0.7466 1.0726
0.6592 2.9813 19000 0.7788 0.7773 0.8159 0.7421 1.0731
0.1354 3.0127 19200 0.7765 0.7791 0.8014 0.7579 1.3351
0.3893 3.0755 19600 0.7835 0.7933 0.7879 0.7986 1.1745
0.4753 3.1382 20000 0.7882 0.7783 0.8541 0.7149 1.2362
0.3696 3.2010 20400 0.7847 0.7850 0.8166 0.7557 1.2638
0.4476 3.2638 20800 0.7706 0.7522 0.8580 0.6697 1.3415
0.4819 3.3265 21200 0.7776 0.7731 0.8235 0.7285 1.2743
0.4361 3.3893 21600 0.7765 0.7727 0.8198 0.7308 1.1387
0.3522 3.4521 22000 0.7694 0.7773 0.7808 0.7738 1.4823
0.4655 3.5148 22400 0.7529 0.7789 0.7283 0.8371 1.4060
0.438 3.5776 22800 0.7659 0.7651 0.8 0.7330 1.2310
0.5766 3.6404 23200 0.7776 0.7869 0.7843 0.7896 1.2406
0.5009 3.7031 23600 0.7882 0.7950 0.8005 0.7896 1.1521
0.4282 3.7659 24000 0.7765 0.7860 0.7825 0.7896 1.2111
0.3733 3.8287 24400 0.7765 0.7780 0.8043 0.7534 1.3162
0.5248 3.8914 24800 0.7788 0.7814 0.8038 0.7602 1.2680
0.4236 3.9542 25200 0.7706 0.7802 0.7775 0.7828 1.3282
0.5013 4.0169 25600 0.7753 0.7792 0.7967 0.7624 1.2586
0.2414 4.0797 26000 0.7741 0.7773 0.7976 0.7579 1.2782
0.1564 4.1425 26400 0.78 0.7797 0.8133 0.7489 1.4687
0.2116 4.2052 26800 0.7694 0.7768 0.7821 0.7715 1.3950
0.288 4.2680 27200 0.7753 0.7766 0.8039 0.7511 1.3878
0.1134 4.3308 27600 0.7729 0.7721 0.8074 0.7398 1.6678
0.1839 4.3935 28000 0.7729 0.7705 0.8120 0.7330 1.6949
0.1831 4.4563 28400 0.78 0.7808 0.8102 0.7534 1.5547
0.2417 4.5191 28800 0.7847 0.7824 0.8246 0.7443 1.4637
0.1922 4.5818 29200 0.7824 0.7821 0.8157 0.7511 1.5513
0.252 4.6446 29600 0.78 0.7808 0.8102 0.7534 1.5873
0.2172 4.7074 30000 0.7882 0.7867 0.8259 0.7511 1.5172
0.1177 4.7701 30400 0.7824 0.7866 0.8024 0.7715 1.5945
0.1921 4.8329 30800 0.7812 0.7842 0.8048 0.7647 1.6031
0.1833 4.8957 31200 0.7765 0.7831 0.7903 0.7760 1.5854
0.243 4.9584 31600 0.7812 0.7842 0.8048 0.7647 1.5959

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1