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---
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: Nahuatl_Espanol_v2
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. -->
# Nahuatl_Espanol_v2
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9402
- Bleu: 6.2508
- Gen Len: 50.5536
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-------:|:-----:|:---------------:|:------:|:-------:|
| No log | 0.1064 | 100 | 3.0336 | 0.8102 | 55.6876 |
| No log | 0.2128 | 200 | 2.8627 | 0.9661 | 53.614 |
| No log | 0.3191 | 300 | 2.7696 | 1.111 | 53.6904 |
| No log | 0.4255 | 400 | 2.6947 | 1.1714 | 54.0762 |
| 3.1672 | 0.5319 | 500 | 2.6405 | 1.2824 | 53.5969 |
| 3.1672 | 0.6383 | 600 | 2.5967 | 1.3386 | 54.4867 |
| 3.1672 | 0.7447 | 700 | 2.5557 | 1.4915 | 55.2298 |
| 3.1672 | 0.8511 | 800 | 2.5261 | 1.5893 | 55.9804 |
| 3.1672 | 0.9574 | 900 | 2.4952 | 1.654 | 57.1207 |
| 2.8149 | 1.0638 | 1000 | 2.4734 | 1.7442 | 55.0846 |
| 2.8149 | 1.1702 | 1100 | 2.4484 | 1.8547 | 58.1569 |
| 2.8149 | 1.2766 | 1200 | 2.4287 | 1.9455 | 55.4888 |
| 2.8149 | 1.3830 | 1300 | 2.4103 | 2.0445 | 55.8386 |
| 2.8149 | 1.4894 | 1400 | 2.3908 | 2.2811 | 54.4788 |
| 2.6669 | 1.5957 | 1500 | 2.3750 | 2.4738 | 56.8398 |
| 2.6669 | 1.7021 | 1600 | 2.3572 | 2.5497 | 55.0454 |
| 2.6669 | 1.8085 | 1700 | 2.3422 | 2.7111 | 54.0798 |
| 2.6669 | 1.9149 | 1800 | 2.3286 | 2.8169 | 55.7837 |
| 2.6669 | 2.0213 | 1900 | 2.3147 | 2.9554 | 55.3014 |
| 2.5801 | 2.1277 | 2000 | 2.3018 | 3.133 | 54.3346 |
| 2.5801 | 2.2340 | 2100 | 2.2902 | 3.2281 | 55.0323 |
| 2.5801 | 2.3404 | 2200 | 2.2838 | 3.2981 | 56.7257 |
| 2.5801 | 2.4468 | 2300 | 2.2696 | 3.4102 | 54.1903 |
| 2.5801 | 2.5532 | 2400 | 2.2585 | 3.3897 | 55.325 |
| 2.5044 | 2.6596 | 2500 | 2.2480 | 3.6232 | 55.6974 |
| 2.5044 | 2.7660 | 2600 | 2.2401 | 3.6573 | 55.2994 |
| 2.5044 | 2.8723 | 2700 | 2.2306 | 3.722 | 56.7022 |
| 2.5044 | 2.9787 | 2800 | 2.2230 | 3.7379 | 52.895 |
| 2.5044 | 3.0851 | 2900 | 2.2132 | 3.7066 | 54.8602 |
| 2.4485 | 3.1915 | 3000 | 2.2064 | 3.9008 | 55.416 |
| 2.4485 | 3.2979 | 3100 | 2.1977 | 3.8825 | 54.9111 |
| 2.4485 | 3.4043 | 3200 | 2.1895 | 3.9786 | 54.3261 |
| 2.4485 | 3.5106 | 3300 | 2.1844 | 3.9746 | 54.5299 |
| 2.4485 | 3.6170 | 3400 | 2.1765 | 4.0218 | 55.0695 |
| 2.3988 | 3.7234 | 3500 | 2.1679 | 4.0382 | 56.6191 |
| 2.3988 | 3.8298 | 3600 | 2.1643 | 4.0658 | 54.9788 |
| 2.3988 | 3.9362 | 3700 | 2.1584 | 4.0867 | 54.61 |
| 2.3988 | 4.0426 | 3800 | 2.1540 | 4.3096 | 54.9816 |
| 2.3988 | 4.1489 | 3900 | 2.1455 | 4.2104 | 54.6118 |
| 2.3646 | 4.2553 | 4000 | 2.1413 | 4.4737 | 54.0416 |
| 2.3646 | 4.3617 | 4100 | 2.1350 | 4.4082 | 55.2328 |
| 2.3646 | 4.4681 | 4200 | 2.1300 | 4.3824 | 55.6597 |
| 2.3646 | 4.5745 | 4300 | 2.1252 | 4.4839 | 53.1048 |
| 2.3646 | 4.6809 | 4400 | 2.1185 | 4.5227 | 54.9721 |
| 2.3419 | 4.7872 | 4500 | 2.1130 | 4.3608 | 54.6448 |
| 2.3419 | 4.8936 | 4600 | 2.1119 | 4.5737 | 53.6723 |
| 2.3419 | 5.0 | 4700 | 2.1053 | 4.6235 | 53.8272 |
| 2.3419 | 5.1064 | 4800 | 2.0997 | 4.5814 | 53.8788 |
| 2.3419 | 5.2128 | 4900 | 2.0955 | 4.7139 | 53.5962 |
| 2.2982 | 5.3191 | 5000 | 2.0901 | 4.6879 | 53.3208 |
| 2.2982 | 5.4255 | 5100 | 2.0876 | 4.7353 | 53.6727 |
| 2.2982 | 5.5319 | 5200 | 2.0796 | 4.8038 | 53.7201 |
| 2.2982 | 5.6383 | 5300 | 2.0803 | 4.7483 | 53.5483 |
| 2.2982 | 5.7447 | 5400 | 2.0730 | 4.7057 | 53.3165 |
| 2.2785 | 5.8511 | 5500 | 2.0700 | 4.806 | 52.9666 |
| 2.2785 | 5.9574 | 5600 | 2.0679 | 4.9122 | 53.3892 |
| 2.2785 | 6.0638 | 5700 | 2.0642 | 4.9269 | 52.246 |
| 2.2785 | 6.1702 | 5800 | 2.0619 | 4.9346 | 52.926 |
| 2.2785 | 6.2766 | 5900 | 2.0560 | 5.1039 | 53.1269 |
| 2.2496 | 6.3830 | 6000 | 2.0550 | 5.1386 | 53.2045 |
| 2.2496 | 6.4894 | 6100 | 2.0504 | 5.2122 | 52.5518 |
| 2.2496 | 6.5957 | 6200 | 2.0460 | 5.1658 | 53.8375 |
| 2.2496 | 6.7021 | 6300 | 2.0441 | 5.2456 | 53.3426 |
| 2.2496 | 6.8085 | 6400 | 2.0399 | 5.2046 | 52.6617 |
| 2.2291 | 6.9149 | 6500 | 2.0359 | 5.1886 | 53.0398 |
| 2.2291 | 7.0213 | 6600 | 2.0342 | 5.3257 | 51.6602 |
| 2.2291 | 7.1277 | 6700 | 2.0323 | 5.2897 | 53.2622 |
| 2.2291 | 7.2340 | 6800 | 2.0298 | 5.4175 | 52.2951 |
| 2.2291 | 7.3404 | 6900 | 2.0271 | 5.4847 | 51.9924 |
| 2.2072 | 7.4468 | 7000 | 2.0240 | 5.4262 | 52.9876 |
| 2.2072 | 7.5532 | 7100 | 2.0205 | 5.5376 | 52.325 |
| 2.2072 | 7.6596 | 7200 | 2.0176 | 5.4789 | 52.4324 |
| 2.2072 | 7.7660 | 7300 | 2.0144 | 5.4898 | 52.2098 |
| 2.2072 | 7.8723 | 7400 | 2.0117 | 5.4634 | 52.3385 |
| 2.1996 | 7.9787 | 7500 | 2.0098 | 5.4655 | 52.7998 |
| 2.1996 | 8.0851 | 7600 | 2.0105 | 5.5251 | 52.1311 |
| 2.1996 | 8.1915 | 7700 | 2.0060 | 5.6941 | 51.5917 |
| 2.1996 | 8.2979 | 7800 | 2.0066 | 5.6255 | 52.1727 |
| 2.1996 | 8.4043 | 7900 | 2.0011 | 5.605 | 52.4629 |
| 2.172 | 8.5106 | 8000 | 2.0009 | 5.6421 | 51.6606 |
| 2.172 | 8.6170 | 8100 | 1.9979 | 5.7238 | 51.2952 |
| 2.172 | 8.7234 | 8200 | 1.9957 | 5.6869 | 51.3821 |
| 2.172 | 8.8298 | 8300 | 1.9924 | 5.7112 | 51.0052 |
| 2.172 | 8.9362 | 8400 | 1.9900 | 5.7394 | 51.8168 |
| 2.1697 | 9.0426 | 8500 | 1.9923 | 5.8348 | 51.0765 |
| 2.1697 | 9.1489 | 8600 | 1.9854 | 5.7641 | 51.7404 |
| 2.1697 | 9.2553 | 8700 | 1.9860 | 5.8078 | 50.6541 |
| 2.1697 | 9.3617 | 8800 | 1.9841 | 5.7624 | 51.7386 |
| 2.1697 | 9.4681 | 8900 | 1.9826 | 5.8623 | 51.401 |
| 2.1488 | 9.5745 | 9000 | 1.9814 | 5.887 | 50.9682 |
| 2.1488 | 9.6809 | 9100 | 1.9793 | 5.8872 | 50.88 |
| 2.1488 | 9.7872 | 9200 | 1.9777 | 5.8794 | 50.9482 |
| 2.1488 | 9.8936 | 9300 | 1.9742 | 5.8443 | 51.1684 |
| 2.1488 | 10.0 | 9400 | 1.9759 | 5.9447 | 51.2332 |
| 2.1508 | 10.1064 | 9500 | 1.9735 | 5.9591 | 51.3292 |
| 2.1508 | 10.2128 | 9600 | 1.9717 | 5.9751 | 51.5011 |
| 2.1508 | 10.3191 | 9700 | 1.9700 | 5.9655 | 50.8294 |
| 2.1508 | 10.4255 | 9800 | 1.9689 | 6.011 | 51.0793 |
| 2.1508 | 10.5319 | 9900 | 1.9683 | 5.9508 | 51.3352 |
| 2.1312 | 10.6383 | 10000 | 1.9658 | 5.9563 | 51.2867 |
| 2.1312 | 10.7447 | 10100 | 1.9635 | 5.9983 | 51.4218 |
| 2.1312 | 10.8511 | 10200 | 1.9616 | 6.0576 | 50.6682 |
| 2.1312 | 10.9574 | 10300 | 1.9618 | 6.0675 | 50.7527 |
| 2.1312 | 11.0638 | 10400 | 1.9604 | 6.1017 | 51.0262 |
| 2.1182 | 11.1702 | 10500 | 1.9603 | 6.114 | 50.9301 |
| 2.1182 | 11.2766 | 10600 | 1.9587 | 6.1085 | 51.0076 |
| 2.1182 | 11.3830 | 10700 | 1.9571 | 6.1066 | 51.0695 |
| 2.1182 | 11.4894 | 10800 | 1.9562 | 6.0495 | 51.5161 |
| 2.1182 | 11.5957 | 10900 | 1.9545 | 6.0907 | 50.8989 |
| 2.1194 | 11.7021 | 11000 | 1.9541 | 6.0534 | 50.7665 |
| 2.1194 | 11.8085 | 11100 | 1.9549 | 6.1778 | 50.403 |
| 2.1194 | 11.9149 | 11200 | 1.9528 | 6.1294 | 50.8481 |
| 2.1194 | 12.0213 | 11300 | 1.9510 | 6.1648 | 50.5486 |
| 2.1194 | 12.1277 | 11400 | 1.9526 | 6.1964 | 50.7805 |
| 2.1119 | 12.2340 | 11500 | 1.9506 | 6.1739 | 50.8039 |
| 2.1119 | 12.3404 | 11600 | 1.9502 | 6.1606 | 50.7453 |
| 2.1119 | 12.4468 | 11700 | 1.9490 | 6.2117 | 50.6436 |
| 2.1119 | 12.5532 | 11800 | 1.9485 | 6.1857 | 50.5681 |
| 2.1119 | 12.6596 | 11900 | 1.9471 | 6.1786 | 50.5037 |
| 2.0983 | 12.7660 | 12000 | 1.9470 | 6.1598 | 50.8716 |
| 2.0983 | 12.8723 | 12100 | 1.9453 | 6.174 | 50.8151 |
| 2.0983 | 12.9787 | 12200 | 1.9471 | 6.2005 | 50.6052 |
| 2.0983 | 13.0851 | 12300 | 1.9446 | 6.1764 | 50.6152 |
| 2.0983 | 13.1915 | 12400 | 1.9439 | 6.2014 | 50.8932 |
| 2.1012 | 13.2979 | 12500 | 1.9439 | 6.2146 | 50.7171 |
| 2.1012 | 13.4043 | 12600 | 1.9429 | 6.2222 | 50.6078 |
| 2.1012 | 13.5106 | 12700 | 1.9427 | 6.1982 | 50.7399 |
| 2.1012 | 13.6170 | 12800 | 1.9420 | 6.2085 | 50.8413 |
| 2.1012 | 13.7234 | 12900 | 1.9421 | 6.2133 | 50.6482 |
| 2.0958 | 13.8298 | 13000 | 1.9430 | 6.2267 | 50.6948 |
| 2.0958 | 13.9362 | 13100 | 1.9418 | 6.2637 | 50.5335 |
| 2.0958 | 14.0426 | 13200 | 1.9410 | 6.2697 | 50.5071 |
| 2.0958 | 14.1489 | 13300 | 1.9416 | 6.2494 | 50.5313 |
| 2.0958 | 14.2553 | 13400 | 1.9413 | 6.2439 | 50.5995 |
| 2.0922 | 14.3617 | 13500 | 1.9407 | 6.2484 | 50.509 |
| 2.0922 | 14.4681 | 13600 | 1.9407 | 6.2464 | 50.5193 |
| 2.0922 | 14.5745 | 13700 | 1.9403 | 6.2474 | 50.5404 |
| 2.0922 | 14.6809 | 13800 | 1.9405 | 6.2663 | 50.5403 |
| 2.0922 | 14.7872 | 13900 | 1.9403 | 6.26 | 50.5487 |
| 2.0898 | 14.8936 | 14000 | 1.9402 | 6.2518 | 50.5451 |
| 2.0898 | 15.0 | 14100 | 1.9402 | 6.2508 | 50.5536 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.1.0
- Datasets 2.19.1
- Tokenizers 0.19.1
|