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---
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: []
---
<!-- 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. -->
# flanT5_large_Fact_U
This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0731
- Accuracy: 0.7788
- Precision: 0.8159
- Recall: 0.7421
- F1 score: 0.7773
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 score |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| 1.2434 | 0.0314 | 200 | 0.7545 | 0.6059 | 0.5805 | 0.8733 | 0.6974 |
| 1.4323 | 0.0628 | 400 | 1.7199 | 0.6 | 0.575 | 0.8846 | 0.6970 |
| 1.253 | 0.0941 | 600 | 1.3828 | 0.6059 | 0.5812 | 0.8665 | 0.6957 |
| 1.2986 | 0.1255 | 800 | 2.4072 | 0.48 | 0.0 | 0.0 | 0.0 |
| 1.2843 | 0.1569 | 1000 | 1.2474 | 0.6553 | 0.7690 | 0.4819 | 0.5925 |
| 1.2245 | 0.1883 | 1200 | 1.3841 | 0.5435 | 0.95 | 0.1290 | 0.2271 |
| 1.2389 | 0.2197 | 1400 | 0.8993 | 0.6706 | 0.6731 | 0.7127 | 0.6923 |
| 1.132 | 0.2511 | 1600 | 0.6845 | 0.6271 | 0.6110 | 0.7783 | 0.6846 |
| 1.0836 | 0.2824 | 1800 | 1.1694 | 0.6824 | 0.7486 | 0.5860 | 0.6574 |
| 1.2434 | 0.3138 | 2000 | 1.4787 | 0.6788 | 0.7632 | 0.5543 | 0.6422 |
| 1.1196 | 0.3452 | 2200 | 1.5004 | 0.6694 | 0.7018 | 0.6335 | 0.6659 |
| 1.5791 | 0.3766 | 2400 | 1.1289 | 0.6376 | 0.8454 | 0.3710 | 0.5157 |
| 1.3035 | 0.4080 | 2600 | 1.0136 | 0.6859 | 0.7119 | 0.6652 | 0.6877 |
| 1.1401 | 0.4394 | 2800 | 1.2340 | 0.6753 | 0.7485 | 0.5656 | 0.6443 |
| 0.9518 | 0.4707 | 3000 | 1.2197 | 0.7024 | 0.7692 | 0.6109 | 0.6810 |
| 1.1623 | 0.5021 | 3200 | 1.2827 | 0.6788 | 0.7046 | 0.6584 | 0.6807 |
| 1.1316 | 0.5335 | 3400 | 1.5077 | 0.6659 | 0.6396 | 0.8190 | 0.7183 |
| 1.2599 | 0.5649 | 3600 | 0.8272 | 0.6341 | 0.8466 | 0.3620 | 0.5071 |
| 0.9866 | 0.5963 | 3800 | 1.4574 | 0.6647 | 0.6605 | 0.7308 | 0.6939 |
| 1.147 | 0.6276 | 4000 | 1.2933 | 0.6824 | 0.7792 | 0.5430 | 0.64 |
| 1.0307 | 0.6590 | 4200 | 1.1586 | 0.6482 | 0.7658 | 0.4661 | 0.5795 |
| 1.0616 | 0.6904 | 4400 | 1.2668 | 0.6976 | 0.7428 | 0.6403 | 0.6877 |
| 1.0724 | 0.7218 | 4600 | 1.1130 | 0.6447 | 0.6955 | 0.5633 | 0.6225 |
| 0.9499 | 0.7532 | 4800 | 1.1635 | 0.7188 | 0.7766 | 0.6448 | 0.7046 |
| 1.1302 | 0.7846 | 5000 | 1.2608 | 0.7118 | 0.7031 | 0.7715 | 0.7357 |
| 1.1921 | 0.8159 | 5200 | 1.1742 | 0.7094 | 0.8056 | 0.5814 | 0.6754 |
| 0.9532 | 0.8473 | 5400 | 1.1589 | 0.7071 | 0.7749 | 0.6154 | 0.6860 |
| 0.783 | 0.8787 | 5600 | 1.3256 | 0.7 | 0.8086 | 0.5543 | 0.6577 |
| 0.9835 | 0.9101 | 5800 | 1.1383 | 0.7282 | 0.7828 | 0.6606 | 0.7166 |
| 0.9898 | 0.9415 | 6000 | 1.0662 | 0.7141 | 0.7409 | 0.6923 | 0.7158 |
| 0.9768 | 0.9729 | 6200 | 1.1941 | 0.7059 | 0.8019 | 0.5769 | 0.6711 |
| 1.043 | 1.0042 | 6400 | 1.2302 | 0.6729 | 0.8628 | 0.4412 | 0.5838 |
| 0.9531 | 1.0356 | 6600 | 1.1304 | 0.7106 | 0.7593 | 0.6493 | 0.7 |
| 1.0585 | 1.0670 | 6800 | 1.0234 | 0.7294 | 0.7944 | 0.6471 | 0.7132 |
| 0.8862 | 1.0984 | 7000 | 1.1941 | 0.6953 | 0.8735 | 0.4842 | 0.6230 |
| 0.8721 | 1.1298 | 7200 | 0.9352 | 0.7376 | 0.792 | 0.6719 | 0.7271 |
| 0.8678 | 1.1611 | 7400 | 1.0473 | 0.7388 | 0.7402 | 0.7670 | 0.7533 |
| 0.7617 | 1.1925 | 7600 | 1.3020 | 0.7294 | 0.7181 | 0.7896 | 0.7522 |
| 1.0394 | 1.2239 | 7800 | 1.0322 | 0.7212 | 0.7904 | 0.6312 | 0.7019 |
| 0.822 | 1.2553 | 8000 | 1.0980 | 0.7388 | 0.7973 | 0.6674 | 0.7266 |
| 0.8406 | 1.2867 | 8200 | 1.4589 | 0.7118 | 0.7031 | 0.7715 | 0.7357 |
| 0.7059 | 1.3181 | 8400 | 1.0655 | 0.7306 | 0.8318 | 0.6041 | 0.6999 |
| 0.8649 | 1.3494 | 8600 | 0.9708 | 0.7424 | 0.8106 | 0.6584 | 0.7266 |
| 0.7142 | 1.3808 | 8800 | 1.1603 | 0.7553 | 0.8214 | 0.6765 | 0.7419 |
| 0.9057 | 1.4122 | 9000 | 0.9389 | 0.76 | 0.8381 | 0.6674 | 0.7431 |
| 0.9312 | 1.4436 | 9200 | 1.0568 | 0.7553 | 0.7721 | 0.7511 | 0.7615 |
| 0.8459 | 1.4750 | 9400 | 1.1646 | 0.7459 | 0.7974 | 0.6855 | 0.7372 |
| 0.8427 | 1.5064 | 9600 | 1.0133 | 0.7459 | 0.8174 | 0.6584 | 0.7293 |
| 0.7245 | 1.5377 | 9800 | 1.1397 | 0.7341 | 0.8885 | 0.5588 | 0.6861 |
| 0.6386 | 1.5691 | 10000 | 1.1112 | 0.7294 | 0.9015 | 0.5385 | 0.6742 |
| 0.7513 | 1.6005 | 10200 | 0.9403 | 0.7671 | 0.805 | 0.7285 | 0.7648 |
| 0.828 | 1.6319 | 10400 | 0.9412 | 0.76 | 0.7820 | 0.7466 | 0.7639 |
| 0.8393 | 1.6633 | 10600 | 0.9359 | 0.7553 | 0.8824 | 0.6109 | 0.7219 |
| 0.8679 | 1.6946 | 10800 | 0.8979 | 0.7588 | 0.8415 | 0.6606 | 0.7402 |
| 0.6735 | 1.7260 | 11000 | 1.0666 | 0.7588 | 0.8786 | 0.6222 | 0.7285 |
| 0.8702 | 1.7574 | 11200 | 0.9554 | 0.7576 | 0.795 | 0.7195 | 0.7553 |
| 0.7435 | 1.7888 | 11400 | 1.0937 | 0.7588 | 0.8143 | 0.6946 | 0.7497 |
| 0.8796 | 1.8202 | 11600 | 0.9257 | 0.7824 | 0.8320 | 0.7285 | 0.7768 |
| 0.6257 | 1.8516 | 11800 | 0.9606 | 0.7659 | 0.8172 | 0.7081 | 0.7588 |
| 0.8589 | 1.8829 | 12000 | 0.9013 | 0.7659 | 0.8481 | 0.6697 | 0.7484 |
| 0.865 | 1.9143 | 12200 | 1.0734 | 0.7612 | 0.7673 | 0.7760 | 0.7717 |
| 0.8068 | 1.9457 | 12400 | 0.9214 | 0.76 | 0.8381 | 0.6674 | 0.7431 |
| 0.6212 | 1.9771 | 12600 | 1.0116 | 0.7706 | 0.8539 | 0.6742 | 0.7535 |
| 0.7657 | 2.0085 | 12800 | 0.9830 | 0.7718 | 0.8605 | 0.6697 | 0.7532 |
| 0.6631 | 2.0399 | 13000 | 1.0075 | 0.7776 | 0.8005 | 0.7624 | 0.7810 |
| 0.3003 | 2.0712 | 13200 | 1.1456 | 0.7812 | 0.8333 | 0.7240 | 0.7748 |
| 0.5982 | 2.1026 | 13400 | 1.0728 | 0.7753 | 0.8438 | 0.6968 | 0.7633 |
| 0.4828 | 2.1340 | 13600 | 1.0474 | 0.7753 | 0.8177 | 0.7308 | 0.7718 |
| 0.5463 | 2.1654 | 13800 | 1.0521 | 0.7776 | 0.8252 | 0.7262 | 0.7726 |
| 0.5429 | 2.1968 | 14000 | 1.0990 | 0.7706 | 0.8365 | 0.6946 | 0.7590 |
| 0.7112 | 2.2282 | 14200 | 1.1072 | 0.7729 | 0.8507 | 0.6833 | 0.7578 |
| 0.4816 | 2.2595 | 14400 | 1.1528 | 0.7753 | 0.8277 | 0.7172 | 0.7685 |
| 0.7882 | 2.2909 | 14600 | 0.9670 | 0.7765 | 0.8214 | 0.7285 | 0.7722 |
| 0.5265 | 2.3223 | 14800 | 1.0724 | 0.7765 | 0.8298 | 0.7172 | 0.7694 |
| 0.6116 | 2.3537 | 15000 | 1.0316 | 0.7776 | 0.8203 | 0.7330 | 0.7742 |
| 0.575 | 2.3851 | 15200 | 1.1125 | 0.7741 | 0.8415 | 0.6968 | 0.7624 |
| 0.5599 | 2.4164 | 15400 | 1.0327 | 0.7765 | 0.8119 | 0.7421 | 0.7754 |
| 0.5821 | 2.4478 | 15600 | 1.0655 | 0.7776 | 0.8078 | 0.7511 | 0.7784 |
| 0.4777 | 2.4792 | 15800 | 1.1187 | 0.7835 | 0.8028 | 0.7738 | 0.7880 |
| 0.432 | 2.5106 | 16000 | 1.1973 | 0.7788 | 0.8256 | 0.7285 | 0.7740 |
| 0.4385 | 2.5420 | 16200 | 1.2155 | 0.7729 | 0.8029 | 0.7466 | 0.7737 |
| 0.6103 | 2.5734 | 16400 | 1.0527 | 0.78 | 0.8212 | 0.7376 | 0.7771 |
| 0.4618 | 2.6047 | 16600 | 1.1377 | 0.78 | 0.8164 | 0.7443 | 0.7787 |
| 0.471 | 2.6361 | 16800 | 1.1468 | 0.7788 | 0.8038 | 0.7602 | 0.7814 |
| 0.6206 | 2.6675 | 17000 | 1.1048 | 0.7765 | 0.8014 | 0.7579 | 0.7791 |
| 0.5869 | 2.6989 | 17200 | 1.1343 | 0.7776 | 0.7895 | 0.7805 | 0.7850 |
| 0.5647 | 2.7303 | 17400 | 1.0843 | 0.7859 | 0.8218 | 0.7511 | 0.7849 |
| 0.5527 | 2.7617 | 17600 | 1.0834 | 0.7847 | 0.8091 | 0.7670 | 0.7875 |
| 0.8013 | 2.7930 | 17800 | 0.9898 | 0.7894 | 0.8124 | 0.7738 | 0.7926 |
| 0.5232 | 2.8244 | 18000 | 1.0052 | 0.7859 | 0.8110 | 0.7670 | 0.7884 |
| 0.617 | 2.8558 | 18200 | 1.0083 | 0.7824 | 0.8157 | 0.7511 | 0.7821 |
| 0.5093 | 2.8872 | 18400 | 1.0510 | 0.7835 | 0.8241 | 0.7421 | 0.7810 |
| 0.5099 | 2.9186 | 18600 | 1.0758 | 0.78 | 0.8133 | 0.7489 | 0.7797 |
| 0.6239 | 2.9499 | 18800 | 1.0726 | 0.7812 | 0.8168 | 0.7466 | 0.7801 |
| 0.6592 | 2.9813 | 19000 | 1.0731 | 0.7788 | 0.8159 | 0.7421 | 0.7773 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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