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Vichentito
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Nahuatl_Espanol_v1

Text2Text Generation
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t5
Generated from Trainer
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  1. README.md +206 -15
  2. model.safetensors +1 -1
  3. runs/Apr19_23-20-56_f3e6e5e03bd0/events.out.tfevents.1713568858.f3e6e5e03bd0.389.1 +2 -2
README.md CHANGED
@@ -1,6 +1,4 @@
1
  ---
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- license: apache-2.0
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- base_model: google/flan-t5-base
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  tags:
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  - generated_from_trainer
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  metrics:
@@ -15,11 +13,11 @@ should probably proofread and complete it, then remove this comment. -->
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  # Nahuatl_Espanol_v1
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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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@@ -44,22 +42,215 @@ 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: 5
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49
  ### Training results
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51
- | Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
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- |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
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-
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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 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
58
 
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  ### Framework versions
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  - Transformers 4.38.2
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  - Pytorch 2.2.1+cu121
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- - Datasets 2.18.0
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  - Tokenizers 0.15.2
 
1
  ---
 
 
2
  tags:
3
  - generated_from_trainer
4
  metrics:
 
13
 
14
  # Nahuatl_Espanol_v1
15
 
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+ This model was trained from scratch on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.7412
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+ - Bleu: 1.5025
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+ - Gen Len: 17.0003
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22
  ## Model description
23
 
 
42
  - seed: 42
43
  - 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: 20
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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 | 2.1516 | 1.0466 | 17.3424 |
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+ | No log | 0.2 | 200 | 2.1411 | 1.0414 | 17.2935 |
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+ | No log | 0.3 | 300 | 2.1333 | 0.9982 | 17.3255 |
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+ | No log | 0.4 | 400 | 2.1277 | 1.0204 | 17.3515 |
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+ | 2.2991 | 0.5 | 500 | 2.1265 | 1.1358 | 17.1251 |
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+ | 2.2991 | 0.6 | 600 | 2.1101 | 1.0457 | 17.3013 |
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+ | 2.2991 | 0.71 | 700 | 2.1052 | 1.0824 | 17.1894 |
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+ | 2.2991 | 0.81 | 800 | 2.0963 | 1.0598 | 17.0784 |
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+ | 2.2991 | 0.91 | 900 | 2.0911 | 1.0469 | 17.3333 |
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+ | 2.2683 | 1.01 | 1000 | 2.0851 | 1.0935 | 17.241 |
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+ | 2.2683 | 1.11 | 1100 | 2.0749 | 1.035 | 17.3406 |
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+ | 2.2683 | 1.21 | 1200 | 2.0685 | 1.0922 | 17.2731 |
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+ | 2.2683 | 1.31 | 1300 | 2.0613 | 1.1029 | 17.2917 |
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+ | 2.2683 | 1.41 | 1400 | 2.0587 | 1.1158 | 17.1735 |
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+ | 2.2775 | 1.51 | 1500 | 2.0533 | 1.1876 | 17.1563 |
66
+ | 2.2775 | 1.61 | 1600 | 2.0449 | 1.1475 | 17.2615 |
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+ | 2.2775 | 1.71 | 1700 | 2.0410 | 1.1033 | 17.2895 |
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+ | 2.2775 | 1.81 | 1800 | 2.0368 | 1.1283 | 17.1944 |
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+ | 2.2775 | 1.92 | 1900 | 2.0308 | 1.1413 | 17.1435 |
70
+ | 2.2414 | 2.02 | 2000 | 2.0257 | 1.1287 | 17.1473 |
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+ | 2.2414 | 2.12 | 2100 | 2.0193 | 1.1557 | 17.1815 |
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+ | 2.2414 | 2.22 | 2200 | 2.0141 | 1.1325 | 17.0784 |
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+ | 2.2414 | 2.32 | 2300 | 2.0111 | 1.1984 | 17.0877 |
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+ | 2.2414 | 2.42 | 2400 | 2.0075 | 1.2308 | 17.1437 |
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+ | 2.2123 | 2.52 | 2500 | 2.0024 | 1.2126 | 17.1866 |
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+ | 2.2123 | 2.62 | 2600 | 1.9951 | 1.1916 | 17.2325 |
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+ | 2.2123 | 2.72 | 2700 | 1.9909 | 1.2253 | 17.1599 |
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+ | 2.2123 | 2.82 | 2800 | 1.9878 | 1.2269 | 17.1614 |
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+ | 2.2123 | 2.92 | 2900 | 1.9855 | 1.2308 | 17.1031 |
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+ | 2.1786 | 3.02 | 3000 | 1.9791 | 1.2687 | 17.1392 |
81
+ | 2.1786 | 3.12 | 3100 | 1.9731 | 1.2657 | 17.0366 |
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+ | 2.1786 | 3.23 | 3200 | 1.9677 | 1.2537 | 17.1979 |
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+ | 2.1786 | 3.33 | 3300 | 1.9657 | 1.2297 | 17.1485 |
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+ | 2.1786 | 3.43 | 3400 | 1.9605 | 1.2355 | 17.0915 |
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+ | 2.1423 | 3.53 | 3500 | 1.9592 | 1.2284 | 17.1583 |
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+ | 2.1423 | 3.63 | 3600 | 1.9541 | 1.212 | 17.1735 |
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+ | 2.1423 | 3.73 | 3700 | 1.9504 | 1.2675 | 17.113 |
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+ | 2.1423 | 3.83 | 3800 | 1.9452 | 1.3087 | 17.119 |
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+ | 2.1423 | 3.93 | 3900 | 1.9460 | 1.3126 | 17.0731 |
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+ | 2.1445 | 4.03 | 4000 | 1.9410 | 1.2955 | 17.0759 |
91
+ | 2.1445 | 4.13 | 4100 | 1.9391 | 1.2635 | 17.1543 |
92
+ | 2.1445 | 4.23 | 4200 | 1.9366 | 1.2737 | 17.1077 |
93
+ | 2.1445 | 4.33 | 4300 | 1.9285 | 1.2516 | 17.1215 |
94
+ | 2.1445 | 4.44 | 4400 | 1.9274 | 1.2881 | 17.1054 |
95
+ | 2.1092 | 4.54 | 4500 | 1.9275 | 1.2693 | 17.147 |
96
+ | 2.1092 | 4.64 | 4600 | 1.9193 | 1.3048 | 17.113 |
97
+ | 2.1092 | 4.74 | 4700 | 1.9171 | 1.2784 | 17.0648 |
98
+ | 2.1092 | 4.84 | 4800 | 1.9130 | 1.2716 | 17.0792 |
99
+ | 2.1092 | 4.94 | 4900 | 1.9105 | 1.2649 | 17.1394 |
100
+ | 2.0841 | 5.04 | 5000 | 1.9076 | 1.3088 | 17.1069 |
101
+ | 2.0841 | 5.14 | 5100 | 1.9052 | 1.343 | 17.1122 |
102
+ | 2.0841 | 5.24 | 5200 | 1.9041 | 1.2905 | 17.1997 |
103
+ | 2.0841 | 5.34 | 5300 | 1.9012 | 1.3532 | 17.0872 |
104
+ | 2.0841 | 5.44 | 5400 | 1.8951 | 1.3142 | 17.0577 |
105
+ | 2.0667 | 5.54 | 5500 | 1.8932 | 1.3118 | 17.0918 |
106
+ | 2.0667 | 5.65 | 5600 | 1.8919 | 1.2924 | 17.032 |
107
+ | 2.0667 | 5.75 | 5700 | 1.8902 | 1.2985 | 17.0857 |
108
+ | 2.0667 | 5.85 | 5800 | 1.8878 | 1.3215 | 17.064 |
109
+ | 2.0667 | 5.95 | 5900 | 1.8845 | 1.3527 | 17.1079 |
110
+ | 2.0568 | 6.05 | 6000 | 1.8803 | 1.3159 | 17.084 |
111
+ | 2.0568 | 6.15 | 6100 | 1.8824 | 1.3597 | 17.0681 |
112
+ | 2.0568 | 6.25 | 6200 | 1.8784 | 1.3658 | 17.0383 |
113
+ | 2.0568 | 6.35 | 6300 | 1.8728 | 1.3394 | 17.0338 |
114
+ | 2.0568 | 6.45 | 6400 | 1.8689 | 1.3449 | 17.0542 |
115
+ | 2.0375 | 6.55 | 6500 | 1.8690 | 1.3396 | 17.0484 |
116
+ | 2.0375 | 6.65 | 6600 | 1.8663 | 1.365 | 17.064 |
117
+ | 2.0375 | 6.75 | 6700 | 1.8624 | 1.3818 | 17.0272 |
118
+ | 2.0375 | 6.85 | 6800 | 1.8596 | 1.3753 | 17.0451 |
119
+ | 2.0375 | 6.96 | 6900 | 1.8601 | 1.3729 | 17.0386 |
120
+ | 2.0146 | 7.06 | 7000 | 1.8578 | 1.3698 | 17.0691 |
121
+ | 2.0146 | 7.16 | 7100 | 1.8567 | 1.379 | 17.0666 |
122
+ | 2.0146 | 7.26 | 7200 | 1.8540 | 1.3879 | 17.0466 |
123
+ | 2.0146 | 7.36 | 7300 | 1.8512 | 1.3935 | 17.0295 |
124
+ | 2.0146 | 7.46 | 7400 | 1.8490 | 1.376 | 17.0638 |
125
+ | 2.0007 | 7.56 | 7500 | 1.8458 | 1.391 | 17.034 |
126
+ | 2.0007 | 7.66 | 7600 | 1.8454 | 1.3952 | 17.0403 |
127
+ | 2.0007 | 7.76 | 7700 | 1.8425 | 1.3835 | 17.0532 |
128
+ | 2.0007 | 7.86 | 7800 | 1.8398 | 1.3824 | 17.1062 |
129
+ | 2.0007 | 7.96 | 7900 | 1.8362 | 1.3773 | 17.0257 |
130
+ | 1.9958 | 8.06 | 8000 | 1.8392 | 1.4047 | 17.0648 |
131
+ | 1.9958 | 8.17 | 8100 | 1.8359 | 1.4128 | 17.053 |
132
+ | 1.9958 | 8.27 | 8200 | 1.8352 | 1.4283 | 17.0414 |
133
+ | 1.9958 | 8.37 | 8300 | 1.8339 | 1.4156 | 17.033 |
134
+ | 1.9958 | 8.47 | 8400 | 1.8333 | 1.4265 | 17.0514 |
135
+ | 1.9757 | 8.57 | 8500 | 1.8271 | 1.4015 | 17.0368 |
136
+ | 1.9757 | 8.67 | 8600 | 1.8262 | 1.4201 | 17.03 |
137
+ | 1.9757 | 8.77 | 8700 | 1.8240 | 1.4229 | 16.9897 |
138
+ | 1.9757 | 8.87 | 8800 | 1.8217 | 1.4076 | 17.0345 |
139
+ | 1.9757 | 8.97 | 8900 | 1.8215 | 1.4097 | 17.0663 |
140
+ | 1.9724 | 9.07 | 9000 | 1.8184 | 1.4134 | 17.0298 |
141
+ | 1.9724 | 9.17 | 9100 | 1.8199 | 1.4336 | 17.0232 |
142
+ | 1.9724 | 9.27 | 9200 | 1.8157 | 1.4273 | 17.0315 |
143
+ | 1.9724 | 9.38 | 9300 | 1.8164 | 1.4237 | 17.0582 |
144
+ | 1.9724 | 9.48 | 9400 | 1.8120 | 1.438 | 17.0335 |
145
+ | 1.9576 | 9.58 | 9500 | 1.8110 | 1.4099 | 17.0139 |
146
+ | 1.9576 | 9.68 | 9600 | 1.8072 | 1.4037 | 17.0265 |
147
+ | 1.9576 | 9.78 | 9700 | 1.8100 | 1.4179 | 17.0272 |
148
+ | 1.9576 | 9.88 | 9800 | 1.8104 | 1.4613 | 16.9927 |
149
+ | 1.9576 | 9.98 | 9900 | 1.8029 | 1.4167 | 17.0477 |
150
+ | 1.9489 | 10.08 | 10000 | 1.8082 | 1.4385 | 17.0194 |
151
+ | 1.9489 | 10.18 | 10100 | 1.8037 | 1.4452 | 17.0229 |
152
+ | 1.9489 | 10.28 | 10200 | 1.8023 | 1.433 | 17.0043 |
153
+ | 1.9489 | 10.38 | 10300 | 1.8026 | 1.4307 | 17.028 |
154
+ | 1.9489 | 10.48 | 10400 | 1.7999 | 1.4571 | 17.0275 |
155
+ | 1.9345 | 10.58 | 10500 | 1.7996 | 1.4477 | 17.0802 |
156
+ | 1.9345 | 10.69 | 10600 | 1.7963 | 1.4575 | 17.0161 |
157
+ | 1.9345 | 10.79 | 10700 | 1.7963 | 1.4435 | 17.0103 |
158
+ | 1.9345 | 10.89 | 10800 | 1.7914 | 1.4397 | 17.0388 |
159
+ | 1.9345 | 10.99 | 10900 | 1.7927 | 1.4422 | 16.9829 |
160
+ | 1.9293 | 11.09 | 11000 | 1.7894 | 1.4422 | 17.0066 |
161
+ | 1.9293 | 11.19 | 11100 | 1.7923 | 1.4843 | 17.0401 |
162
+ | 1.9293 | 11.29 | 11200 | 1.7912 | 1.4638 | 17.0182 |
163
+ | 1.9293 | 11.39 | 11300 | 1.7872 | 1.4528 | 17.0477 |
164
+ | 1.9293 | 11.49 | 11400 | 1.7855 | 1.4406 | 17.0444 |
165
+ | 1.9106 | 11.59 | 11500 | 1.7856 | 1.4566 | 17.0398 |
166
+ | 1.9106 | 11.69 | 11600 | 1.7859 | 1.4779 | 17.025 |
167
+ | 1.9106 | 11.79 | 11700 | 1.7828 | 1.4783 | 17.0149 |
168
+ | 1.9106 | 11.9 | 11800 | 1.7819 | 1.451 | 17.0325 |
169
+ | 1.9106 | 12.0 | 11900 | 1.7793 | 1.4928 | 17.0391 |
170
+ | 1.9126 | 12.1 | 12000 | 1.7805 | 1.4568 | 16.9945 |
171
+ | 1.9126 | 12.2 | 12100 | 1.7806 | 1.4858 | 16.9783 |
172
+ | 1.9126 | 12.3 | 12200 | 1.7781 | 1.4565 | 16.9912 |
173
+ | 1.9126 | 12.4 | 12300 | 1.7784 | 1.474 | 17.0255 |
174
+ | 1.9126 | 12.5 | 12400 | 1.7760 | 1.4754 | 17.0217 |
175
+ | 1.9055 | 12.6 | 12500 | 1.7764 | 1.4778 | 17.0113 |
176
+ | 1.9055 | 12.7 | 12600 | 1.7748 | 1.4778 | 17.0204 |
177
+ | 1.9055 | 12.8 | 12700 | 1.7737 | 1.4919 | 17.0219 |
178
+ | 1.9055 | 12.9 | 12800 | 1.7722 | 1.4691 | 17.0098 |
179
+ | 1.9055 | 13.0 | 12900 | 1.7698 | 1.4749 | 17.0139 |
180
+ | 1.9039 | 13.1 | 13000 | 1.7701 | 1.4694 | 17.0282 |
181
+ | 1.9039 | 13.21 | 13100 | 1.7737 | 1.4957 | 16.9755 |
182
+ | 1.9039 | 13.31 | 13200 | 1.7711 | 1.5004 | 17.0214 |
183
+ | 1.9039 | 13.41 | 13300 | 1.7693 | 1.4821 | 17.0207 |
184
+ | 1.9039 | 13.51 | 13400 | 1.7650 | 1.4707 | 17.0255 |
185
+ | 1.8825 | 13.61 | 13500 | 1.7673 | 1.4961 | 17.0219 |
186
+ | 1.8825 | 13.71 | 13600 | 1.7672 | 1.4643 | 17.028 |
187
+ | 1.8825 | 13.81 | 13700 | 1.7647 | 1.4712 | 16.9861 |
188
+ | 1.8825 | 13.91 | 13800 | 1.7627 | 1.4686 | 17.0015 |
189
+ | 1.8825 | 14.01 | 13900 | 1.7608 | 1.4556 | 17.0033 |
190
+ | 1.8863 | 14.11 | 14000 | 1.7621 | 1.4764 | 17.0025 |
191
+ | 1.8863 | 14.21 | 14100 | 1.7614 | 1.481 | 17.0207 |
192
+ | 1.8863 | 14.31 | 14200 | 1.7611 | 1.4844 | 17.0166 |
193
+ | 1.8863 | 14.42 | 14300 | 1.7591 | 1.4837 | 16.9622 |
194
+ | 1.8863 | 14.52 | 14400 | 1.7585 | 1.4864 | 17.0111 |
195
+ | 1.8877 | 14.62 | 14500 | 1.7589 | 1.4742 | 17.0353 |
196
+ | 1.8877 | 14.72 | 14600 | 1.7585 | 1.474 | 16.9977 |
197
+ | 1.8877 | 14.82 | 14700 | 1.7604 | 1.4952 | 17.0048 |
198
+ | 1.8877 | 14.92 | 14800 | 1.7562 | 1.4678 | 17.0096 |
199
+ | 1.8877 | 15.02 | 14900 | 1.7561 | 1.4883 | 17.0008 |
200
+ | 1.8722 | 15.12 | 15000 | 1.7547 | 1.4768 | 16.9871 |
201
+ | 1.8722 | 15.22 | 15100 | 1.7554 | 1.4822 | 17.0444 |
202
+ | 1.8722 | 15.32 | 15200 | 1.7536 | 1.5027 | 16.9897 |
203
+ | 1.8722 | 15.42 | 15300 | 1.7563 | 1.4845 | 17.0101 |
204
+ | 1.8722 | 15.52 | 15400 | 1.7521 | 1.4844 | 17.0144 |
205
+ | 1.8685 | 15.62 | 15500 | 1.7522 | 1.4963 | 16.9793 |
206
+ | 1.8685 | 15.73 | 15600 | 1.7523 | 1.4978 | 16.9939 |
207
+ | 1.8685 | 15.83 | 15700 | 1.7512 | 1.4761 | 16.9967 |
208
+ | 1.8685 | 15.93 | 15800 | 1.7524 | 1.4903 | 17.0035 |
209
+ | 1.8685 | 16.03 | 15900 | 1.7510 | 1.4934 | 16.999 |
210
+ | 1.8759 | 16.13 | 16000 | 1.7526 | 1.5051 | 17.0068 |
211
+ | 1.8759 | 16.23 | 16100 | 1.7494 | 1.5 | 17.0156 |
212
+ | 1.8759 | 16.33 | 16200 | 1.7500 | 1.5096 | 17.0262 |
213
+ | 1.8759 | 16.43 | 16300 | 1.7499 | 1.4996 | 17.0121 |
214
+ | 1.8759 | 16.53 | 16400 | 1.7501 | 1.5095 | 16.9899 |
215
+ | 1.8591 | 16.63 | 16500 | 1.7465 | 1.4867 | 17.0197 |
216
+ | 1.8591 | 16.73 | 16600 | 1.7490 | 1.5094 | 16.9836 |
217
+ | 1.8591 | 16.83 | 16700 | 1.7478 | 1.499 | 16.9982 |
218
+ | 1.8591 | 16.94 | 16800 | 1.7470 | 1.4973 | 17.0103 |
219
+ | 1.8591 | 17.04 | 16900 | 1.7465 | 1.5013 | 16.9708 |
220
+ | 1.8634 | 17.14 | 17000 | 1.7470 | 1.5025 | 16.9856 |
221
+ | 1.8634 | 17.24 | 17100 | 1.7475 | 1.5054 | 16.9607 |
222
+ | 1.8634 | 17.34 | 17200 | 1.7467 | 1.5119 | 16.972 |
223
+ | 1.8634 | 17.44 | 17300 | 1.7460 | 1.5091 | 16.9836 |
224
+ | 1.8634 | 17.54 | 17400 | 1.7441 | 1.4993 | 16.9962 |
225
+ | 1.8599 | 17.64 | 17500 | 1.7434 | 1.5036 | 16.9743 |
226
+ | 1.8599 | 17.74 | 17600 | 1.7440 | 1.5023 | 17.001 |
227
+ | 1.8599 | 17.84 | 17700 | 1.7455 | 1.5053 | 16.9972 |
228
+ | 1.8599 | 17.94 | 17800 | 1.7438 | 1.5095 | 17.0141 |
229
+ | 1.8599 | 18.04 | 17900 | 1.7439 | 1.5115 | 16.9894 |
230
+ | 1.8574 | 18.15 | 18000 | 1.7437 | 1.5022 | 16.999 |
231
+ | 1.8574 | 18.25 | 18100 | 1.7435 | 1.5055 | 17.0066 |
232
+ | 1.8574 | 18.35 | 18200 | 1.7433 | 1.5102 | 17.0113 |
233
+ | 1.8574 | 18.45 | 18300 | 1.7419 | 1.5027 | 16.9919 |
234
+ | 1.8574 | 18.55 | 18400 | 1.7416 | 1.5019 | 17.0008 |
235
+ | 1.8454 | 18.65 | 18500 | 1.7418 | 1.509 | 16.9924 |
236
+ | 1.8454 | 18.75 | 18600 | 1.7415 | 1.5002 | 16.9912 |
237
+ | 1.8454 | 18.85 | 18700 | 1.7414 | 1.5028 | 16.9894 |
238
+ | 1.8454 | 18.95 | 18800 | 1.7417 | 1.5089 | 16.9816 |
239
+ | 1.8454 | 19.05 | 18900 | 1.7416 | 1.5065 | 17.0003 |
240
+ | 1.8574 | 19.15 | 19000 | 1.7419 | 1.506 | 16.9909 |
241
+ | 1.8574 | 19.25 | 19100 | 1.7417 | 1.5017 | 16.9987 |
242
+ | 1.8574 | 19.35 | 19200 | 1.7416 | 1.504 | 17.0025 |
243
+ | 1.8574 | 19.46 | 19300 | 1.7413 | 1.5001 | 16.9997 |
244
+ | 1.8574 | 19.56 | 19400 | 1.7409 | 1.5015 | 17.002 |
245
+ | 1.8435 | 19.66 | 19500 | 1.7410 | 1.5009 | 17.005 |
246
+ | 1.8435 | 19.76 | 19600 | 1.7411 | 1.5014 | 17.0018 |
247
+ | 1.8435 | 19.86 | 19700 | 1.7412 | 1.5015 | 17.0005 |
248
+ | 1.8435 | 19.96 | 19800 | 1.7412 | 1.5025 | 17.0003 |
249
 
250
 
251
  ### Framework versions
252
 
253
  - Transformers 4.38.2
254
  - Pytorch 2.2.1+cu121
255
+ - Datasets 2.19.0
256
  - Tokenizers 0.15.2
model.safetensors CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:9082909ee5365f3e9322351103ffaf040df6960dca94807c71e46ee4abbfdaf5
3
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runs/Apr19_23-20-56_f3e6e5e03bd0/events.out.tfevents.1713568858.f3e6e5e03bd0.389.1 CHANGED
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