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---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: ingredient_prune
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. -->
# ingredient_prune
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0255
- Rouge1: 88.3061
- Rouge2: 76.6099
- Rougel: 88.3242
- Rougelsum: 88.2429
- Gen Len: 10.5872
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 2.9499 | 0.09 | 10 | 1.3100 | 33.1645 | 23.9561 | 32.6647 | 32.7137 | 14.7431 |
| 1.9454 | 0.18 | 20 | 0.6787 | 30.1119 | 21.203 | 29.5079 | 29.6061 | 13.8349 |
| 1.309 | 0.28 | 30 | 0.5147 | 25.3399 | 17.694 | 24.4102 | 24.4425 | 11.6514 |
| 1.0307 | 0.37 | 40 | 0.4398 | 17.4522 | 11.66 | 16.2846 | 16.3817 | 8.5413 |
| 0.9574 | 0.46 | 50 | 0.4302 | 16.6745 | 10.6799 | 15.8568 | 16.4301 | 8.0092 |
| 0.7183 | 0.55 | 60 | 0.3818 | 14.4343 | 9.4646 | 13.9825 | 14.1979 | 6.9725 |
| 0.5636 | 0.64 | 70 | 0.3096 | 9.4156 | 5.2844 | 9.0143 | 9.239 | 5.5596 |
| 0.4603 | 0.73 | 80 | 0.2664 | 8.6106 | 4.7574 | 7.9285 | 8.4429 | 5.0917 |
| 0.4607 | 0.83 | 90 | 0.2319 | 6.7868 | 3.9309 | 6.1844 | 6.7007 | 3.8349 |
| 0.352 | 0.92 | 100 | 0.1991 | 6.2965 | 3.5572 | 5.3616 | 5.9941 | 3.2661 |
| 0.3426 | 1.01 | 110 | 0.1735 | 6.1795 | 3.1174 | 5.3783 | 5.9261 | 3.3119 |
| 0.2901 | 1.1 | 120 | 0.1553 | 5.5031 | 2.739 | 4.9926 | 5.5079 | 3.1376 |
| 0.3619 | 1.19 | 130 | 0.1452 | 4.1403 | 1.8462 | 4.0877 | 4.1877 | 3.0092 |
| 0.2509 | 1.28 | 140 | 0.1338 | 4.1399 | 1.8019 | 3.9836 | 4.1506 | 2.9541 |
| 0.1938 | 1.38 | 150 | 0.1187 | 2.9515 | 1.2174 | 2.7845 | 3.0192 | 2.2569 |
| 0.1987 | 1.47 | 160 | 0.1068 | 4.8991 | 3.4459 | 4.7552 | 4.9489 | 2.1284 |
| 0.1702 | 1.56 | 170 | 0.0983 | 8.7082 | 5.5788 | 8.5531 | 8.8267 | 3.4587 |
| 0.1535 | 1.65 | 180 | 0.0871 | 11.5572 | 7.6669 | 11.4688 | 11.5381 | 4.6972 |
| 0.1629 | 1.74 | 190 | 0.0771 | 16.33 | 11.587 | 16.0842 | 16.1965 | 6.6055 |
| 0.1618 | 1.83 | 200 | 0.0690 | 21.4186 | 14.9296 | 21.2789 | 21.2002 | 8.367 |
| 0.1617 | 1.93 | 210 | 0.0628 | 27.6198 | 19.8907 | 27.4479 | 27.4515 | 10.3394 |
| 0.1136 | 2.02 | 220 | 0.0572 | 36.7416 | 28.2903 | 36.7181 | 36.719 | 12.3578 |
| 0.1278 | 2.11 | 230 | 0.0526 | 46.9007 | 36.6481 | 47.1002 | 46.8623 | 13.7064 |
| 0.0915 | 2.2 | 240 | 0.0486 | 56.1238 | 45.5624 | 56.3372 | 56.0369 | 14.1101 |
| 0.0736 | 2.29 | 250 | 0.0448 | 63.3857 | 51.8889 | 63.6163 | 63.2685 | 13.4771 |
| 0.0855 | 2.39 | 260 | 0.0420 | 72.669 | 59.9359 | 72.7393 | 72.6055 | 12.3486 |
| 0.0921 | 2.48 | 270 | 0.0388 | 78.2224 | 65.2581 | 78.2789 | 77.9532 | 11.3578 |
| 0.077 | 2.57 | 280 | 0.0364 | 82.3881 | 68.397 | 82.4999 | 82.3175 | 10.5872 |
| 0.0848 | 2.66 | 290 | 0.0347 | 85.4014 | 72.793 | 85.495 | 85.3917 | 10.633 |
| 0.0978 | 2.75 | 300 | 0.0332 | 86.0947 | 72.9678 | 86.1325 | 86.0028 | 10.5138 |
| 0.0635 | 2.84 | 310 | 0.0323 | 86.158 | 73.833 | 86.2727 | 86.1471 | 10.5596 |
| 0.0555 | 2.94 | 320 | 0.0314 | 86.0306 | 73.8297 | 86.0421 | 85.9571 | 10.5688 |
| 0.0792 | 3.03 | 330 | 0.0305 | 87.5066 | 75.3885 | 87.6496 | 87.3874 | 10.3761 |
| 0.0536 | 3.12 | 340 | 0.0297 | 88.0844 | 75.8754 | 88.1956 | 87.9164 | 10.4954 |
| 0.063 | 3.21 | 350 | 0.0290 | 88.0844 | 75.8754 | 88.1956 | 87.9164 | 10.4954 |
| 0.0563 | 3.3 | 360 | 0.0283 | 88.0783 | 75.989 | 88.2233 | 87.9578 | 10.5138 |
| 0.0547 | 3.39 | 370 | 0.0279 | 88.1265 | 76.3196 | 88.3078 | 88.0765 | 10.6147 |
| 0.0635 | 3.49 | 380 | 0.0275 | 86.9846 | 74.8237 | 87.0556 | 86.9021 | 10.5872 |
| 0.0835 | 3.58 | 390 | 0.0271 | 86.933 | 75.3277 | 87.0357 | 86.931 | 10.6147 |
| 0.0628 | 3.67 | 400 | 0.0269 | 87.5981 | 75.5811 | 87.6905 | 87.4594 | 10.6789 |
| 0.0554 | 3.76 | 410 | 0.0267 | 88.0124 | 76.5633 | 88.174 | 87.9292 | 10.578 |
| 0.0342 | 3.85 | 420 | 0.0266 | 88.0124 | 76.5633 | 88.174 | 87.9292 | 10.578 |
| 0.0396 | 3.94 | 430 | 0.0263 | 88.0064 | 76.6947 | 88.1712 | 87.9434 | 10.5872 |
| 0.045 | 4.04 | 440 | 0.0262 | 87.7466 | 76.3605 | 87.8932 | 87.6273 | 10.5505 |
| 0.0566 | 4.13 | 450 | 0.0262 | 87.8577 | 76.5633 | 88.0399 | 87.7835 | 10.6055 |
| 0.0582 | 4.22 | 460 | 0.0261 | 87.8103 | 76.1351 | 87.9277 | 87.7032 | 10.6697 |
| 0.051 | 4.31 | 470 | 0.0260 | 87.8103 | 76.1351 | 87.9277 | 87.7032 | 10.6697 |
| 0.0398 | 4.4 | 480 | 0.0258 | 88.1974 | 76.4006 | 88.2158 | 88.0622 | 10.6789 |
| 0.0364 | 4.5 | 490 | 0.0257 | 88.3353 | 76.5513 | 88.3291 | 88.2557 | 10.633 |
| 0.0498 | 4.59 | 500 | 0.0257 | 88.4083 | 76.5513 | 88.4132 | 88.35 | 10.6147 |
| 0.0406 | 4.68 | 510 | 0.0256 | 88.3061 | 76.6099 | 88.3242 | 88.2429 | 10.5872 |
| 0.0403 | 4.77 | 520 | 0.0256 | 88.3061 | 76.6099 | 88.3242 | 88.2429 | 10.5872 |
| 0.0421 | 4.86 | 530 | 0.0255 | 88.3061 | 76.6099 | 88.3242 | 88.2429 | 10.5872 |
| 0.0271 | 4.95 | 540 | 0.0255 | 88.3061 | 76.6099 | 88.3242 | 88.2429 | 10.5872 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2
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