--- license: apache-2.0 base_model: Vichentito/Nahuatl_Espanol_v2 tags: - generated_from_trainer metrics: - bleu model-index: - name: Nahuatl_Espanol_vn results: [] --- # Nahuatl_Espanol_vn This model is a fine-tuned version of [Vichentito/Nahuatl_Espanol_v2](https://huggingface.co/Vichentito/Nahuatl_Espanol_v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4730 - Bleu: 12.6156 - Gen Len: 46.1122 ## 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: 0.0003 - 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |:-------------:|:------:|:----:|:---------------:|:-------:|:-------:| | No log | 0.1064 | 100 | 1.9442 | 6.1166 | 48.8218 | | No log | 0.2128 | 200 | 1.9259 | 6.1381 | 50.998 | | No log | 0.3191 | 300 | 1.9115 | 6.573 | 48.1678 | | No log | 0.4255 | 400 | 1.8855 | 6.4758 | 50.9589 | | 2.137 | 0.5319 | 500 | 1.8646 | 6.6628 | 50.1324 | | 2.137 | 0.6383 | 600 | 1.8440 | 6.8852 | 50.913 | | 2.137 | 0.7447 | 700 | 1.8311 | 6.879 | 49.7563 | | 2.137 | 0.8511 | 800 | 1.8105 | 7.268 | 48.5967 | | 2.137 | 0.9574 | 900 | 1.7907 | 7.5189 | 47.8909 | | 2.0246 | 1.0638 | 1000 | 1.7790 | 7.8039 | 49.7481 | | 2.0246 | 1.1702 | 1100 | 1.7751 | 7.8132 | 47.9985 | | 2.0246 | 1.2766 | 1200 | 1.7505 | 7.9468 | 47.9796 | | 2.0246 | 1.3830 | 1300 | 1.7378 | 8.1741 | 47.8028 | | 2.0246 | 1.4894 | 1400 | 1.7219 | 8.1614 | 48.2161 | | 1.8778 | 1.5957 | 1500 | 1.7178 | 8.3463 | 47.3984 | | 1.8778 | 1.7021 | 1600 | 1.7051 | 8.8493 | 48.1068 | | 1.8778 | 1.8085 | 1700 | 1.6907 | 8.5621 | 48.2402 | | 1.8778 | 1.9149 | 1800 | 1.6849 | 8.7522 | 49.7167 | | 1.8778 | 2.0213 | 1900 | 1.6738 | 8.9027 | 47.812 | | 1.7945 | 2.1277 | 2000 | 1.6718 | 9.323 | 47.1293 | | 1.7945 | 2.2340 | 2100 | 1.6619 | 9.1801 | 46.7211 | | 1.7945 | 2.3404 | 2200 | 1.6509 | 9.1763 | 47.085 | | 1.7945 | 2.4468 | 2300 | 1.6394 | 9.2575 | 47.9275 | | 1.7945 | 2.5532 | 2400 | 1.6388 | 9.5591 | 47.2517 | | 1.7164 | 2.6596 | 2500 | 1.6336 | 9.5656 | 47.996 | | 1.7164 | 2.7660 | 2600 | 1.6205 | 9.767 | 47.4039 | | 1.7164 | 2.8723 | 2700 | 1.6152 | 9.5891 | 47.2867 | | 1.7164 | 2.9787 | 2800 | 1.6074 | 9.7122 | 47.3419 | | 1.7164 | 3.0851 | 2900 | 1.6122 | 10.1634 | 47.2597 | | 1.6476 | 3.1915 | 3000 | 1.6016 | 10.0543 | 47.7276 | | 1.6476 | 3.2979 | 3100 | 1.5939 | 9.8821 | 47.8567 | | 1.6476 | 3.4043 | 3200 | 1.5922 | 10.1382 | 47.8498 | | 1.6476 | 3.5106 | 3300 | 1.5808 | 10.1617 | 46.7866 | | 1.6476 | 3.6170 | 3400 | 1.5780 | 10.2872 | 47.1357 | | 1.5916 | 3.7234 | 3500 | 1.5713 | 10.3594 | 47.6514 | | 1.5916 | 3.8298 | 3600 | 1.5657 | 10.3745 | 46.9836 | | 1.5916 | 3.9362 | 3700 | 1.5594 | 10.5178 | 46.7624 | | 1.5916 | 4.0426 | 3800 | 1.5704 | 10.665 | 46.6844 | | 1.5916 | 4.1489 | 3900 | 1.5589 | 10.6936 | 47.1421 | | 1.5475 | 4.2553 | 4000 | 1.5541 | 10.7949 | 46.8528 | | 1.5475 | 4.3617 | 4100 | 1.5481 | 10.631 | 47.3707 | | 1.5475 | 4.4681 | 4200 | 1.5468 | 10.8283 | 46.5979 | | 1.5475 | 4.5745 | 4300 | 1.5403 | 10.9811 | 47.1724 | | 1.5475 | 4.6809 | 4400 | 1.5356 | 11.0659 | 46.682 | | 1.4988 | 4.7872 | 4500 | 1.5379 | 11.0334 | 46.9275 | | 1.4988 | 4.8936 | 4600 | 1.5257 | 10.9602 | 46.5027 | | 1.4988 | 5.0 | 4700 | 1.5260 | 11.1289 | 46.8976 | | 1.4988 | 5.1064 | 4800 | 1.5311 | 11.1567 | 46.4451 | | 1.4988 | 5.2128 | 4900 | 1.5274 | 11.3486 | 46.6272 | | 1.4535 | 5.3191 | 5000 | 1.5259 | 11.2413 | 46.9351 | | 1.4535 | 5.4255 | 5100 | 1.5215 | 11.3214 | 46.8237 | | 1.4535 | 5.5319 | 5200 | 1.5129 | 11.4718 | 47.1328 | | 1.4535 | 5.6383 | 5300 | 1.5125 | 11.4864 | 46.6589 | | 1.4535 | 5.7447 | 5400 | 1.5121 | 11.5694 | 46.5577 | | 1.4219 | 5.8511 | 5500 | 1.5036 | 11.6487 | 46.5487 | | 1.4219 | 5.9574 | 5600 | 1.5000 | 11.5189 | 46.5733 | | 1.4219 | 6.0638 | 5700 | 1.5075 | 11.5882 | 46.5391 | | 1.4219 | 6.1702 | 5800 | 1.5096 | 11.7659 | 46.1593 | | 1.4219 | 6.2766 | 5900 | 1.5083 | 11.5189 | 46.4194 | | 1.3736 | 6.3830 | 6000 | 1.4987 | 11.7254 | 46.3748 | | 1.3736 | 6.4894 | 6100 | 1.4974 | 11.709 | 46.7318 | | 1.3736 | 6.5957 | 6200 | 1.4940 | 11.7516 | 46.5484 | | 1.3736 | 6.7021 | 6300 | 1.4918 | 11.828 | 46.4844 | | 1.3736 | 6.8085 | 6400 | 1.4933 | 11.9539 | 46.5024 | | 1.3705 | 6.9149 | 6500 | 1.4856 | 11.8196 | 46.6158 | | 1.3705 | 7.0213 | 6600 | 1.4959 | 11.8671 | 46.5148 | | 1.3705 | 7.1277 | 6700 | 1.4959 | 11.9404 | 46.1803 | | 1.3705 | 7.2340 | 6800 | 1.4974 | 12.0784 | 46.2473 | | 1.3705 | 7.3404 | 6900 | 1.4882 | 12.3014 | 46.27 | | 1.3223 | 7.4468 | 7000 | 1.4813 | 12.0859 | 46.5862 | | 1.3223 | 7.5532 | 7100 | 1.4846 | 12.1787 | 46.0993 | | 1.3223 | 7.6596 | 7200 | 1.4853 | 12.1633 | 46.142 | | 1.3223 | 7.7660 | 7300 | 1.4811 | 12.1962 | 46.4309 | | 1.3223 | 7.8723 | 7400 | 1.4819 | 12.1183 | 46.0882 | | 1.3154 | 7.9787 | 7500 | 1.4757 | 12.2428 | 46.2431 | | 1.3154 | 8.0851 | 7600 | 1.4811 | 12.2027 | 46.3626 | | 1.3154 | 8.1915 | 7700 | 1.4803 | 12.3011 | 46.328 | | 1.3154 | 8.2979 | 7800 | 1.4830 | 12.2846 | 46.3101 | | 1.3154 | 8.4043 | 7900 | 1.4808 | 12.3297 | 45.987 | | 1.2766 | 8.5106 | 8000 | 1.4789 | 12.3831 | 46.1575 | | 1.2766 | 8.6170 | 8100 | 1.4774 | 12.4203 | 46.2323 | | 1.2766 | 8.7234 | 8200 | 1.4737 | 12.5194 | 46.2774 | | 1.2766 | 8.8298 | 8300 | 1.4738 | 12.3472 | 46.2114 | | 1.2766 | 8.9362 | 8400 | 1.4687 | 12.3894 | 46.3324 | | 1.2752 | 9.0426 | 8500 | 1.4748 | 12.4876 | 46.0959 | | 1.2752 | 9.1489 | 8600 | 1.4792 | 12.597 | 45.985 | | 1.2752 | 9.2553 | 8700 | 1.4761 | 12.5547 | 46.2209 | | 1.2752 | 9.3617 | 8800 | 1.4759 | 12.5615 | 46.0812 | | 1.2752 | 9.4681 | 8900 | 1.4752 | 12.5736 | 46.1437 | | 1.2454 | 9.5745 | 9000 | 1.4765 | 12.5976 | 46.0358 | | 1.2454 | 9.6809 | 9100 | 1.4745 | 12.5204 | 46.1139 | | 1.2454 | 9.7872 | 9200 | 1.4735 | 12.5765 | 46.107 | | 1.2454 | 9.8936 | 9300 | 1.4732 | 12.5875 | 46.1734 | | 1.2454 | 10.0 | 9400 | 1.4730 | 12.6156 | 46.1122 | ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1