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---
library_name: transformers
license: apache-2.0
base_model: T5-small
tags:
- generated_from_trainer
metrics:
- bleu
- rouge
model-index:
- name: T5-JSON-OM-IMP
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. -->
# T5-JSON-OM-IMP
This model is a fine-tuned version of [T5-small](https://huggingface.co/T5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0063
- Micro Precision: 0.4069
- Micro Recall: 0.4681
- Micro F1: 0.4353
- Macro Precision: 0.4072
- Macro Recall: 0.4742
- Macro F1: 0.4382
- Bleu: 75.6457
- Rouge1: 0.7665
- Rouge2: 0.5226
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Micro Precision | Micro Recall | Micro F1 | Macro Precision | Macro Recall | Macro F1 | Bleu | Rouge1 | Rouge2 |
|:-------------:|:------:|:----:|:---------------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:-------:|:------:|:------:|
| 17.7048 | 0.1068 | 50 | 8.2715 | 0 | 0.0 | 0 | 0.0 | 0.0 | 0 | 0.0213 | 0.0194 | 0.0 |
| 3.5821 | 0.2137 | 100 | 0.1261 | 0 | 0.0 | 0 | 0.0 | 0.0 | 0 | 0.0022 | 0.0022 | 0.0 |
| 0.5575 | 0.3205 | 150 | 0.0912 | 0 | 0.0 | 0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 |
| 0.1174 | 0.4274 | 200 | 0.0534 | 0 | 0.0 | 0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 |
| 0.067 | 0.5342 | 250 | 0.0329 | 0 | 0.0 | 0 | 0.0 | 0.0 | 0 | 0.0 | 0.0 | 0.0 |
| 0.047 | 0.6410 | 300 | 0.0223 | 0 | 0.0 | 0 | 0.0 | 0.0 | 0 | 0.0000 | 0.0026 | 0.0005 |
| 0.0367 | 0.7479 | 350 | 0.0176 | 0.1667 | 0.0007 | 0.0015 | 0.125 | 0.0008 | 0.0015 | 0.0588 | 0.0196 | 0.0085 |
| 0.0305 | 0.8547 | 400 | 0.0151 | 0.3333 | 0.0066 | 0.0129 | 0.2500 | 0.0069 | 0.0134 | 0.2728 | 0.0411 | 0.0211 |
| 0.0259 | 0.9615 | 450 | 0.0128 | 0.3904 | 0.0536 | 0.0942 | 0.3743 | 0.0562 | 0.0978 | 5.1808 | 0.1404 | 0.0812 |
| 0.023 | 1.0684 | 500 | 0.0109 | 0.3549 | 0.1570 | 0.2177 | 0.3537 | 0.1627 | 0.2228 | 22.2151 | 0.3401 | 0.2093 |
| 0.0208 | 1.1752 | 550 | 0.0097 | 0.3635 | 0.2678 | 0.3084 | 0.3621 | 0.2756 | 0.3130 | 44.1481 | 0.5171 | 0.3302 |
| 0.0191 | 1.2821 | 600 | 0.0084 | 0.3442 | 0.3478 | 0.3460 | 0.3446 | 0.3517 | 0.3481 | 61.5038 | 0.6523 | 0.4297 |
| 0.017 | 1.3889 | 650 | 0.0080 | 0.3555 | 0.3881 | 0.3711 | 0.3555 | 0.3921 | 0.3729 | 67.9000 | 0.7144 | 0.4766 |
| 0.0158 | 1.4957 | 700 | 0.0077 | 0.3907 | 0.4299 | 0.4094 | 0.3906 | 0.4342 | 0.4112 | 73.1590 | 0.7482 | 0.5047 |
| 0.0151 | 1.6026 | 750 | 0.0073 | 0.3906 | 0.4270 | 0.4080 | 0.3905 | 0.4311 | 0.4098 | 74.0266 | 0.7572 | 0.5115 |
| 0.0139 | 1.7094 | 800 | 0.0070 | 0.3962 | 0.4453 | 0.4193 | 0.3962 | 0.4504 | 0.4216 | 75.0071 | 0.7665 | 0.5226 |
| 0.0135 | 1.8162 | 850 | 0.0069 | 0.4101 | 0.4571 | 0.4323 | 0.4106 | 0.4626 | 0.4350 | 75.7473 | 0.7674 | 0.5221 |
| 0.0129 | 1.9231 | 900 | 0.0068 | 0.4065 | 0.4563 | 0.4300 | 0.4069 | 0.4619 | 0.4327 | 75.3869 | 0.7651 | 0.5159 |
| 0.0125 | 2.0299 | 950 | 0.0067 | 0.3994 | 0.4600 | 0.4275 | 0.3995 | 0.4655 | 0.4300 | 75.0995 | 0.7644 | 0.5174 |
| 0.0115 | 2.1368 | 1000 | 0.0066 | 0.4059 | 0.4622 | 0.4322 | 0.4062 | 0.4678 | 0.4348 | 75.5433 | 0.7666 | 0.5209 |
| 0.0115 | 2.2436 | 1050 | 0.0065 | 0.4064 | 0.4637 | 0.4332 | 0.4067 | 0.4698 | 0.4360 | 75.5672 | 0.7658 | 0.5213 |
| 0.0114 | 2.3504 | 1100 | 0.0066 | 0.4118 | 0.4644 | 0.4366 | 0.4124 | 0.4704 | 0.4395 | 75.9796 | 0.7695 | 0.5254 |
| 0.0112 | 2.4573 | 1150 | 0.0065 | 0.4055 | 0.4674 | 0.4342 | 0.4057 | 0.4732 | 0.4369 | 75.5003 | 0.7654 | 0.5203 |
| 0.012 | 2.5641 | 1200 | 0.0064 | 0.3971 | 0.4585 | 0.4256 | 0.3972 | 0.4642 | 0.4281 | 75.0879 | 0.7662 | 0.5205 |
| 0.0107 | 2.6709 | 1250 | 0.0064 | 0.3883 | 0.4490 | 0.4165 | 0.3885 | 0.4550 | 0.4191 | 74.6715 | 0.7654 | 0.5178 |
| 0.0103 | 2.7778 | 1300 | 0.0064 | 0.3981 | 0.4585 | 0.4262 | 0.3983 | 0.4645 | 0.4289 | 75.1906 | 0.7673 | 0.5222 |
| 0.0106 | 2.8846 | 1350 | 0.0064 | 0.3985 | 0.4578 | 0.4261 | 0.3987 | 0.4639 | 0.4288 | 75.2734 | 0.7661 | 0.5196 |
| 0.0099 | 2.9915 | 1400 | 0.0064 | 0.4082 | 0.4681 | 0.4361 | 0.4085 | 0.4741 | 0.4389 | 75.6457 | 0.7656 | 0.5220 |
| 0.0101 | 3.0983 | 1450 | 0.0064 | 0.4028 | 0.4622 | 0.4305 | 0.4031 | 0.4680 | 0.4331 | 75.4024 | 0.7658 | 0.5199 |
| 0.0101 | 3.2051 | 1500 | 0.0064 | 0.4028 | 0.4607 | 0.4298 | 0.4031 | 0.4663 | 0.4324 | 75.3197 | 0.7648 | 0.5179 |
| 0.01 | 3.3120 | 1550 | 0.0064 | 0.4074 | 0.4666 | 0.4350 | 0.4078 | 0.4726 | 0.4378 | 75.5829 | 0.7650 | 0.5206 |
| 0.0095 | 3.4188 | 1600 | 0.0063 | 0.4078 | 0.4688 | 0.4362 | 0.4081 | 0.4749 | 0.4390 | 75.6771 | 0.7665 | 0.5231 |
| 0.0099 | 3.5256 | 1650 | 0.0063 | 0.4075 | 0.4688 | 0.4360 | 0.4078 | 0.4748 | 0.4388 | 75.6573 | 0.7662 | 0.5226 |
| 0.0098 | 3.6325 | 1700 | 0.0063 | 0.4069 | 0.4681 | 0.4353 | 0.4072 | 0.4742 | 0.4382 | 75.6457 | 0.7665 | 0.5226 |
| 0.0093 | 3.7393 | 1750 | 0.0063 | 0.4071 | 0.4681 | 0.4355 | 0.4075 | 0.4742 | 0.4383 | 75.6457 | 0.7658 | 0.5221 |
| 0.0095 | 3.8462 | 1800 | 0.0063 | 0.4065 | 0.4674 | 0.4348 | 0.4068 | 0.4736 | 0.4377 | 75.6341 | 0.7660 | 0.5221 |
| 0.0095 | 3.9530 | 1850 | 0.0063 | 0.4069 | 0.4681 | 0.4353 | 0.4072 | 0.4742 | 0.4382 | 75.6457 | 0.7665 | 0.5226 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
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