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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