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---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: tl-bic-model
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. -->
# tl-bic-model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0048
- Bleu: 9.1518
- Gen Len: 9.681
## 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: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- 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 | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 11 | 2.9595 | 0.2068 | 9.7301 |
| No log | 2.0 | 22 | 2.5919 | 0.4412 | 10.0736 |
| No log | 3.0 | 33 | 2.2077 | 0.9166 | 9.6626 |
| No log | 4.0 | 44 | 1.9446 | 0.7991 | 9.8037 |
| No log | 5.0 | 55 | 1.6666 | 0.8674 | 9.8221 |
| No log | 6.0 | 66 | 1.4209 | 1.0262 | 10.0613 |
| No log | 7.0 | 77 | 1.1828 | 1.573 | 9.9693 |
| No log | 8.0 | 88 | 0.9715 | 1.6163 | 9.9509 |
| No log | 9.0 | 99 | 0.8203 | 2.1844 | 9.7362 |
| No log | 10.0 | 110 | 0.6698 | 2.193 | 9.6687 |
| No log | 11.0 | 121 | 0.5533 | 3.1733 | 9.816 |
| No log | 12.0 | 132 | 0.4650 | 3.0054 | 9.6687 |
| No log | 13.0 | 143 | 0.3783 | 3.5488 | 9.6012 |
| No log | 14.0 | 154 | 0.3130 | 4.1709 | 9.7362 |
| No log | 15.0 | 165 | 0.2620 | 4.9365 | 9.6442 |
| No log | 16.0 | 176 | 0.2351 | 5.5276 | 9.546 |
| No log | 17.0 | 187 | 0.1953 | 5.6558 | 9.6074 |
| No log | 18.0 | 198 | 0.1524 | 6.4656 | 9.6503 |
| No log | 19.0 | 209 | 0.1226 | 6.9583 | 9.5828 |
| No log | 20.0 | 220 | 0.0953 | 7.7977 | 9.5951 |
| No log | 21.0 | 231 | 0.0766 | 7.7172 | 9.638 |
| No log | 22.0 | 242 | 0.0633 | 8.2632 | 9.6135 |
| No log | 23.0 | 253 | 0.0581 | 8.3314 | 9.6135 |
| No log | 24.0 | 264 | 0.0478 | 8.6339 | 9.6564 |
| No log | 25.0 | 275 | 0.0379 | 8.4599 | 9.681 |
| No log | 26.0 | 286 | 0.0349 | 8.8518 | 9.681 |
| No log | 27.0 | 297 | 0.0284 | 8.6561 | 9.6994 |
| No log | 28.0 | 308 | 0.0215 | 8.8647 | 9.6748 |
| No log | 29.0 | 319 | 0.0189 | 8.8318 | 9.681 |
| No log | 30.0 | 330 | 0.0211 | 8.7839 | 9.681 |
| No log | 31.0 | 341 | 0.0223 | 9.0581 | 9.6687 |
| No log | 32.0 | 352 | 0.0172 | 9.0431 | 9.6687 |
| No log | 33.0 | 363 | 0.0131 | 9.0838 | 9.681 |
| No log | 34.0 | 374 | 0.0152 | 8.9549 | 9.681 |
| No log | 35.0 | 385 | 0.0121 | 9.0402 | 9.681 |
| No log | 36.0 | 396 | 0.0178 | 9.1416 | 9.6442 |
| No log | 37.0 | 407 | 0.0161 | 9.0402 | 9.6564 |
| No log | 38.0 | 418 | 0.0139 | 9.1518 | 9.681 |
| No log | 39.0 | 429 | 0.0162 | 9.0741 | 9.681 |
| No log | 40.0 | 440 | 0.0126 | 9.1518 | 9.681 |
| No log | 41.0 | 451 | 0.0108 | 9.0897 | 9.681 |
| No log | 42.0 | 462 | 0.0144 | 9.0836 | 9.6933 |
| No log | 43.0 | 473 | 0.0238 | 9.1129 | 9.6871 |
| No log | 44.0 | 484 | 0.0075 | 9.1518 | 9.681 |
| No log | 45.0 | 495 | 0.0108 | 8.9628 | 9.681 |
| 0.7724 | 46.0 | 506 | 0.0071 | 8.9863 | 9.681 |
| 0.7724 | 47.0 | 517 | 0.0087 | 9.1518 | 9.681 |
| 0.7724 | 48.0 | 528 | 0.0082 | 9.1518 | 9.681 |
| 0.7724 | 49.0 | 539 | 0.0064 | 9.1518 | 9.681 |
| 0.7724 | 50.0 | 550 | 0.0095 | 9.1518 | 9.681 |
| 0.7724 | 51.0 | 561 | 0.0090 | 9.1518 | 9.681 |
| 0.7724 | 52.0 | 572 | 0.0091 | 9.1801 | 9.681 |
| 0.7724 | 53.0 | 583 | 0.0105 | 9.1801 | 9.681 |
| 0.7724 | 54.0 | 594 | 0.0180 | 8.9309 | 9.681 |
| 0.7724 | 55.0 | 605 | 0.0123 | 9.1518 | 9.681 |
| 0.7724 | 56.0 | 616 | 0.0119 | 9.1518 | 9.681 |
| 0.7724 | 57.0 | 627 | 0.0061 | 9.1518 | 9.681 |
| 0.7724 | 58.0 | 638 | 0.0082 | 9.1518 | 9.681 |
| 0.7724 | 59.0 | 649 | 0.0059 | 9.1518 | 9.681 |
| 0.7724 | 60.0 | 660 | 0.0146 | 9.0639 | 9.681 |
| 0.7724 | 61.0 | 671 | 0.0123 | 9.0639 | 9.681 |
| 0.7724 | 62.0 | 682 | 0.0084 | 9.0639 | 9.681 |
| 0.7724 | 63.0 | 693 | 0.0122 | 9.0639 | 9.681 |
| 0.7724 | 64.0 | 704 | 0.0319 | 9.1518 | 9.681 |
| 0.7724 | 65.0 | 715 | 0.0142 | 9.1518 | 9.681 |
| 0.7724 | 66.0 | 726 | 0.0086 | 9.1518 | 9.681 |
| 0.7724 | 67.0 | 737 | 0.0078 | 9.0847 | 9.681 |
| 0.7724 | 68.0 | 748 | 0.0122 | 9.1518 | 9.681 |
| 0.7724 | 69.0 | 759 | 0.0092 | 9.1518 | 9.681 |
| 0.7724 | 70.0 | 770 | 0.0059 | 9.1518 | 9.681 |
| 0.7724 | 71.0 | 781 | 0.0090 | 9.0944 | 9.6871 |
| 0.7724 | 72.0 | 792 | 0.0127 | 9.0944 | 9.6871 |
| 0.7724 | 73.0 | 803 | 0.0108 | 9.0944 | 9.6871 |
| 0.7724 | 74.0 | 814 | 0.0091 | 9.1518 | 9.681 |
| 0.7724 | 75.0 | 825 | 0.0073 | 9.1518 | 9.681 |
| 0.7724 | 76.0 | 836 | 0.0112 | 9.1518 | 9.681 |
| 0.7724 | 77.0 | 847 | 0.0113 | 9.1518 | 9.681 |
| 0.7724 | 78.0 | 858 | 0.0093 | 9.1518 | 9.681 |
| 0.7724 | 79.0 | 869 | 0.0048 | 9.1518 | 9.681 |
| 0.7724 | 80.0 | 880 | 0.0064 | 9.1518 | 9.681 |
| 0.7724 | 81.0 | 891 | 0.0102 | 9.1518 | 9.681 |
| 0.7724 | 82.0 | 902 | 0.0110 | 9.1467 | 9.6748 |
| 0.7724 | 83.0 | 913 | 0.0104 | 9.1467 | 9.6748 |
| 0.7724 | 84.0 | 924 | 0.0089 | 9.1467 | 9.6748 |
| 0.7724 | 85.0 | 935 | 0.0078 | 9.1518 | 9.681 |
| 0.7724 | 86.0 | 946 | 0.0067 | 9.1518 | 9.681 |
| 0.7724 | 87.0 | 957 | 0.0047 | 9.1518 | 9.681 |
| 0.7724 | 88.0 | 968 | 0.0047 | 9.1518 | 9.681 |
| 0.7724 | 89.0 | 979 | 0.0058 | 9.1518 | 9.681 |
| 0.7724 | 90.0 | 990 | 0.0045 | 9.1518 | 9.681 |
| 0.0426 | 91.0 | 1001 | 0.0087 | 9.1518 | 9.681 |
| 0.0426 | 92.0 | 1012 | 0.0096 | 9.1518 | 9.681 |
| 0.0426 | 93.0 | 1023 | 0.0063 | 9.1518 | 9.681 |
| 0.0426 | 94.0 | 1034 | 0.0076 | 9.1518 | 9.681 |
| 0.0426 | 95.0 | 1045 | 0.0055 | 9.1518 | 9.681 |
| 0.0426 | 96.0 | 1056 | 0.0054 | 9.1518 | 9.681 |
| 0.0426 | 97.0 | 1067 | 0.0052 | 9.1518 | 9.681 |
| 0.0426 | 98.0 | 1078 | 0.0046 | 9.1518 | 9.681 |
| 0.0426 | 99.0 | 1089 | 0.0100 | 9.1518 | 9.681 |
| 0.0426 | 100.0 | 1100 | 0.0104 | 9.1518 | 9.681 |
| 0.0426 | 101.0 | 1111 | 0.0180 | 9.1518 | 9.681 |
| 0.0426 | 102.0 | 1122 | 0.0208 | 9.1518 | 9.681 |
| 0.0426 | 103.0 | 1133 | 0.0159 | 9.1518 | 9.681 |
| 0.0426 | 104.0 | 1144 | 0.0139 | 9.1518 | 9.681 |
| 0.0426 | 105.0 | 1155 | 0.0122 | 9.1518 | 9.681 |
| 0.0426 | 106.0 | 1166 | 0.0080 | 9.1518 | 9.681 |
| 0.0426 | 107.0 | 1177 | 0.0063 | 9.1518 | 9.681 |
| 0.0426 | 108.0 | 1188 | 0.0149 | 9.1467 | 9.6687 |
| 0.0426 | 109.0 | 1199 | 0.0147 | 9.1518 | 9.681 |
| 0.0426 | 110.0 | 1210 | 0.0113 | 9.1518 | 9.681 |
| 0.0426 | 111.0 | 1221 | 0.0170 | 9.1518 | 9.681 |
| 0.0426 | 112.0 | 1232 | 0.0138 | 9.1518 | 9.681 |
| 0.0426 | 113.0 | 1243 | 0.0129 | 9.1518 | 9.681 |
| 0.0426 | 114.0 | 1254 | 0.0095 | 9.1518 | 9.681 |
| 0.0426 | 115.0 | 1265 | 0.0133 | 9.1518 | 9.681 |
| 0.0426 | 116.0 | 1276 | 0.0128 | 9.1518 | 9.681 |
| 0.0426 | 117.0 | 1287 | 0.0112 | 9.1518 | 9.681 |
| 0.0426 | 118.0 | 1298 | 0.0093 | 9.1518 | 9.681 |
| 0.0426 | 119.0 | 1309 | 0.0066 | 9.1518 | 9.681 |
| 0.0426 | 120.0 | 1320 | 0.0048 | 9.1518 | 9.681 |
| 0.0426 | 121.0 | 1331 | 0.0079 | 9.1518 | 9.681 |
| 0.0426 | 122.0 | 1342 | 0.0095 | 9.1518 | 9.681 |
| 0.0426 | 123.0 | 1353 | 0.0069 | 9.1518 | 9.681 |
| 0.0426 | 124.0 | 1364 | 0.0056 | 9.1518 | 9.681 |
| 0.0426 | 125.0 | 1375 | 0.0049 | 9.1518 | 9.681 |
| 0.0426 | 126.0 | 1386 | 0.0066 | 9.1518 | 9.681 |
| 0.0426 | 127.0 | 1397 | 0.0080 | 9.1518 | 9.681 |
| 0.0426 | 128.0 | 1408 | 0.0073 | 9.1467 | 9.6687 |
| 0.0426 | 129.0 | 1419 | 0.0063 | 9.1518 | 9.681 |
| 0.0426 | 130.0 | 1430 | 0.0063 | 9.1518 | 9.681 |
| 0.0426 | 131.0 | 1441 | 0.0051 | 9.1518 | 9.681 |
| 0.0426 | 132.0 | 1452 | 0.0045 | 9.1518 | 9.681 |
| 0.0426 | 133.0 | 1463 | 0.0061 | 9.1518 | 9.681 |
| 0.0426 | 134.0 | 1474 | 0.0061 | 9.1518 | 9.681 |
| 0.0426 | 135.0 | 1485 | 0.0042 | 9.1518 | 9.681 |
| 0.0426 | 136.0 | 1496 | 0.0043 | 9.1518 | 9.681 |
| 0.0153 | 137.0 | 1507 | 0.0045 | 9.1518 | 9.681 |
| 0.0153 | 138.0 | 1518 | 0.0056 | 9.1518 | 9.681 |
| 0.0153 | 139.0 | 1529 | 0.0113 | 9.1518 | 9.681 |
| 0.0153 | 140.0 | 1540 | 0.0054 | 9.1518 | 9.681 |
| 0.0153 | 141.0 | 1551 | 0.0054 | 9.1518 | 9.681 |
| 0.0153 | 142.0 | 1562 | 0.0058 | 9.1518 | 9.681 |
| 0.0153 | 143.0 | 1573 | 0.0048 | 9.1518 | 9.681 |
| 0.0153 | 144.0 | 1584 | 0.0049 | 9.1518 | 9.681 |
| 0.0153 | 145.0 | 1595 | 0.0047 | 9.1518 | 9.681 |
| 0.0153 | 146.0 | 1606 | 0.0046 | 9.1518 | 9.681 |
| 0.0153 | 147.0 | 1617 | 0.0046 | 9.1518 | 9.681 |
| 0.0153 | 148.0 | 1628 | 0.0046 | 9.1518 | 9.681 |
| 0.0153 | 149.0 | 1639 | 0.0045 | 9.1518 | 9.681 |
| 0.0153 | 150.0 | 1650 | 0.0048 | 9.1518 | 9.681 |
| 0.0153 | 151.0 | 1661 | 0.0054 | 9.1518 | 9.681 |
| 0.0153 | 152.0 | 1672 | 0.0058 | 9.1518 | 9.681 |
| 0.0153 | 153.0 | 1683 | 0.0057 | 9.1518 | 9.681 |
| 0.0153 | 154.0 | 1694 | 0.0056 | 9.1518 | 9.681 |
| 0.0153 | 155.0 | 1705 | 0.0056 | 9.1518 | 9.681 |
| 0.0153 | 156.0 | 1716 | 0.0061 | 9.1518 | 9.681 |
| 0.0153 | 157.0 | 1727 | 0.0062 | 9.1518 | 9.681 |
| 0.0153 | 158.0 | 1738 | 0.0060 | 9.1518 | 9.681 |
| 0.0153 | 159.0 | 1749 | 0.0060 | 9.1518 | 9.681 |
| 0.0153 | 160.0 | 1760 | 0.0061 | 9.1518 | 9.681 |
| 0.0153 | 161.0 | 1771 | 0.0052 | 9.1518 | 9.681 |
| 0.0153 | 162.0 | 1782 | 0.0049 | 9.1518 | 9.681 |
| 0.0153 | 163.0 | 1793 | 0.0047 | 9.1518 | 9.681 |
| 0.0153 | 164.0 | 1804 | 0.0046 | 9.1518 | 9.681 |
| 0.0153 | 165.0 | 1815 | 0.0045 | 9.1518 | 9.681 |
| 0.0153 | 166.0 | 1826 | 0.0046 | 9.1518 | 9.681 |
| 0.0153 | 167.0 | 1837 | 0.0048 | 9.1518 | 9.681 |
| 0.0153 | 168.0 | 1848 | 0.0052 | 9.1518 | 9.681 |
| 0.0153 | 169.0 | 1859 | 0.0051 | 9.1518 | 9.681 |
| 0.0153 | 170.0 | 1870 | 0.0055 | 9.1518 | 9.681 |
| 0.0153 | 171.0 | 1881 | 0.0056 | 9.1518 | 9.681 |
| 0.0153 | 172.0 | 1892 | 0.0051 | 9.1518 | 9.681 |
| 0.0153 | 173.0 | 1903 | 0.0050 | 9.1518 | 9.681 |
| 0.0153 | 174.0 | 1914 | 0.0048 | 9.1518 | 9.681 |
| 0.0153 | 175.0 | 1925 | 0.0048 | 9.1518 | 9.681 |
| 0.0153 | 176.0 | 1936 | 0.0045 | 9.1518 | 9.681 |
| 0.0153 | 177.0 | 1947 | 0.0045 | 9.1518 | 9.681 |
| 0.0153 | 178.0 | 1958 | 0.0045 | 9.1518 | 9.681 |
| 0.0153 | 179.0 | 1969 | 0.0044 | 9.1518 | 9.681 |
| 0.0153 | 180.0 | 1980 | 0.0046 | 9.1518 | 9.681 |
| 0.0153 | 181.0 | 1991 | 0.0046 | 9.1518 | 9.681 |
| 0.007 | 182.0 | 2002 | 0.0046 | 9.1518 | 9.681 |
| 0.007 | 183.0 | 2013 | 0.0046 | 9.1518 | 9.681 |
| 0.007 | 184.0 | 2024 | 0.0046 | 9.1518 | 9.681 |
| 0.007 | 185.0 | 2035 | 0.0046 | 9.1518 | 9.681 |
| 0.007 | 186.0 | 2046 | 0.0046 | 9.1518 | 9.681 |
| 0.007 | 187.0 | 2057 | 0.0046 | 9.1518 | 9.681 |
| 0.007 | 188.0 | 2068 | 0.0047 | 9.1518 | 9.681 |
| 0.007 | 189.0 | 2079 | 0.0047 | 9.1518 | 9.681 |
| 0.007 | 190.0 | 2090 | 0.0048 | 9.1518 | 9.681 |
| 0.007 | 191.0 | 2101 | 0.0048 | 9.1518 | 9.681 |
| 0.007 | 192.0 | 2112 | 0.0049 | 9.1518 | 9.681 |
| 0.007 | 193.0 | 2123 | 0.0049 | 9.1518 | 9.681 |
| 0.007 | 194.0 | 2134 | 0.0048 | 9.1518 | 9.681 |
| 0.007 | 195.0 | 2145 | 0.0048 | 9.1518 | 9.681 |
| 0.007 | 196.0 | 2156 | 0.0048 | 9.1518 | 9.681 |
| 0.007 | 197.0 | 2167 | 0.0048 | 9.1518 | 9.681 |
| 0.007 | 198.0 | 2178 | 0.0048 | 9.1518 | 9.681 |
| 0.007 | 199.0 | 2189 | 0.0049 | 9.1518 | 9.681 |
| 0.007 | 200.0 | 2200 | 0.0048 | 9.1518 | 9.681 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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