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Vichentito
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Nahuatl_Espanol_v1

Text2Text Generation
Transformers
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Safetensors
t5
Generated from Trainer
text-generation-inference
Inference Endpoints
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End of training

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  1. README.md +53 -7
  2. model.safetensors +1 -1
  3. runs/Apr19_06-15-31_e554b8d932a8/events.out.tfevents.1713507332.e554b8d932a8.786.0 +2 -2
README.md CHANGED
@@ -17,9 +17,9 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.4812
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- - Bleu: 1.0887
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- - Gen Len: 17.0652
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  ## Model description
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@@ -44,15 +44,61 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 3
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
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- | 3.448 | 1.0 | 906 | 2.6910 | 0.626 | 16.9832 |
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- | 2.8267 | 2.0 | 1812 | 2.5231 | 1.0654 | 17.0591 |
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- | 2.7104 | 3.0 | 2718 | 2.4812 | 1.0887 | 17.0652 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.3093
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+ - Bleu: 0.928
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+ - Gen Len: 17.3008
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  ## Model description
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - num_epochs: 5
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
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+ | No log | 0.1 | 100 | 3.4034 | 0.3143 | 17.3089 |
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+ | No log | 0.2 | 200 | 3.1638 | 0.3187 | 17.4173 |
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+ | No log | 0.3 | 300 | 3.0323 | 0.3292 | 17.2922 |
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+ | No log | 0.4 | 400 | 2.9391 | 0.3713 | 17.1362 |
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+ | 3.4051 | 0.5 | 500 | 2.8715 | 0.4182 | 17.2461 |
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+ | 3.4051 | 0.6 | 600 | 2.8084 | 0.4782 | 17.2052 |
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+ | 3.4051 | 0.71 | 700 | 2.7614 | 0.4541 | 17.1109 |
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+ | 3.4051 | 0.81 | 800 | 2.7171 | 0.6082 | 17.2327 |
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+ | 3.4051 | 0.91 | 900 | 2.6860 | 0.7904 | 17.2443 |
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+ | 2.961 | 1.01 | 1000 | 2.6516 | 0.8488 | 17.3227 |
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+ | 2.961 | 1.11 | 1100 | 2.6256 | 0.8094 | 17.3409 |
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+ | 2.961 | 1.21 | 1200 | 2.5967 | 0.7545 | 17.3147 |
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+ | 2.961 | 1.31 | 1300 | 2.5753 | 0.7891 | 17.2418 |
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+ | 2.961 | 1.41 | 1400 | 2.5545 | 0.7925 | 17.2849 |
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+ | 2.7764 | 1.51 | 1500 | 2.5335 | 0.7728 | 17.3243 |
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+ | 2.7764 | 1.61 | 1600 | 2.5163 | 0.8055 | 17.4302 |
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+ | 2.7764 | 1.71 | 1700 | 2.4993 | 0.843 | 17.2501 |
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+ | 2.7764 | 1.81 | 1800 | 2.4840 | 0.8297 | 17.2136 |
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+ | 2.7764 | 1.92 | 1900 | 2.4719 | 0.8457 | 17.3364 |
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+ | 2.6783 | 2.02 | 2000 | 2.4598 | 0.9152 | 17.2453 |
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+ | 2.6783 | 2.12 | 2100 | 2.4458 | 0.8597 | 17.2405 |
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+ | 2.6783 | 2.22 | 2200 | 2.4345 | 0.8741 | 17.2436 |
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+ | 2.6783 | 2.32 | 2300 | 2.4245 | 0.8681 | 17.3492 |
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+ | 2.6783 | 2.42 | 2400 | 2.4166 | 0.8936 | 17.2708 |
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+ | 2.6228 | 2.52 | 2500 | 2.4055 | 0.9256 | 17.3568 |
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+ | 2.6228 | 2.62 | 2600 | 2.3960 | 0.9354 | 17.3248 |
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+ | 2.6228 | 2.72 | 2700 | 2.3873 | 0.9543 | 17.3961 |
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+ | 2.6228 | 2.82 | 2800 | 2.3823 | 0.93 | 17.2564 |
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+ | 2.6228 | 2.92 | 2900 | 2.3740 | 0.9701 | 17.2161 |
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+ | 2.5527 | 3.02 | 3000 | 2.3671 | 0.9669 | 17.26 |
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+ | 2.5527 | 3.12 | 3100 | 2.3583 | 0.8909 | 17.4082 |
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+ | 2.5527 | 3.23 | 3200 | 2.3555 | 0.9186 | 17.3296 |
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+ | 2.5527 | 3.33 | 3300 | 2.3508 | 0.9321 | 17.3119 |
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+ | 2.5527 | 3.43 | 3400 | 2.3459 | 0.9821 | 17.2859 |
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+ | 2.5106 | 3.53 | 3500 | 2.3410 | 0.9371 | 17.292 |
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+ | 2.5106 | 3.63 | 3600 | 2.3373 | 0.9368 | 17.2756 |
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+ | 2.5106 | 3.73 | 3700 | 2.3330 | 0.9312 | 17.295 |
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+ | 2.5106 | 3.83 | 3800 | 2.3304 | 0.9827 | 17.2847 |
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+ | 2.5106 | 3.93 | 3900 | 2.3263 | 0.9637 | 17.2867 |
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+ | 2.5138 | 4.03 | 4000 | 2.3216 | 0.9209 | 17.3356 |
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+ | 2.5138 | 4.13 | 4100 | 2.3198 | 0.9088 | 17.3548 |
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+ | 2.5138 | 4.23 | 4200 | 2.3177 | 0.9193 | 17.3401 |
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+ | 2.5138 | 4.33 | 4300 | 2.3151 | 0.937 | 17.3346 |
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+ | 2.5138 | 4.44 | 4400 | 2.3142 | 0.9396 | 17.2953 |
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+ | 2.4804 | 4.54 | 4500 | 2.3130 | 0.9446 | 17.3099 |
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+ | 2.4804 | 4.64 | 4600 | 2.3109 | 0.9449 | 17.3018 |
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+ | 2.4804 | 4.74 | 4700 | 2.3102 | 0.9363 | 17.3106 |
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+ | 2.4804 | 4.84 | 4800 | 2.3095 | 0.9306 | 17.3033 |
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+ | 2.4804 | 4.94 | 4900 | 2.3093 | 0.928 | 17.3008 |
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  ### Framework versions
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