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yigagilbert
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salt_language_ID

Text2Text Generation
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  1. README.md +72 -296
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  5. training_args.bin +1 -1
README.md CHANGED
@@ -3,9 +3,26 @@ license: apache-2.0
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  base_model: google/t5-efficient-tiny
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  tags:
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  - generated_from_trainer
 
 
 
 
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  model-index:
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  - name: salt_language_ID
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- results: []
 
 
 
 
 
 
 
 
 
 
 
 
 
9
  ---
10
 
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -13,9 +30,10 @@ should probably proofread and complete it, then remove this comment. -->
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14
  # salt_language_ID
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16
- This model is a fine-tuned version of [google/t5-efficient-tiny](https://huggingface.co/google/t5-efficient-tiny) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0226
 
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  ## Model description
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@@ -34,306 +52,64 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 0.0002
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- - train_batch_size: 32
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- - eval_batch_size: 16
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  - seed: 42
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- - gradient_accumulation_steps: 2
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- - total_train_batch_size: 64
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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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- - lr_scheduler_warmup_steps: 500
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- - num_epochs: 2
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  ### Training results
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50
- | Training Loss | Epoch | Step | Validation Loss |
51
- |:-------------:|:-----:|:----:|:---------------:|
52
- | 290.8962 | 0.01 | 10 | 305.3102 |
53
- | 294.9642 | 0.01 | 20 | 296.0765 |
54
- | 281.0769 | 0.02 | 30 | 279.4232 |
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- | 270.1917 | 0.03 | 40 | 254.2555 |
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- | 243.3464 | 0.04 | 50 | 224.8921 |
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- | 214.4873 | 0.04 | 60 | 196.3753 |
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- | 187.9743 | 0.05 | 70 | 135.2601 |
59
- | 164.024 | 0.06 | 80 | 105.8822 |
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- | 143.6121 | 0.06 | 90 | 90.4786 |
61
- | 126.019 | 0.07 | 100 | 75.9475 |
62
- | 111.2358 | 0.08 | 110 | 64.2843 |
63
- | 96.1458 | 0.09 | 120 | 51.4159 |
64
- | 83.5523 | 0.09 | 130 | 37.5541 |
65
- | 69.0164 | 0.1 | 140 | 24.2653 |
66
- | 55.3427 | 0.11 | 150 | 19.5405 |
67
- | 38.9215 | 0.11 | 160 | 12.6305 |
68
- | 28.0225 | 0.12 | 170 | 10.0512 |
69
- | 21.42 | 0.13 | 180 | 6.2506 |
70
- | 15.1783 | 0.14 | 190 | 3.1231 |
71
- | 11.7336 | 0.14 | 200 | 1.5384 |
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- | 9.3141 | 0.15 | 210 | 1.0360 |
73
- | 6.9583 | 0.16 | 220 | 0.5647 |
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- | 5.7743 | 0.16 | 230 | 0.5395 |
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- | 4.7486 | 0.17 | 240 | 0.5611 |
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- | 3.7387 | 0.18 | 250 | 0.4738 |
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- | 3.3398 | 0.19 | 260 | 0.4057 |
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- | 3.1383 | 0.19 | 270 | 0.7111 |
79
- | 2.5906 | 0.2 | 280 | 0.3963 |
80
- | 2.4711 | 0.21 | 290 | 0.6025 |
81
- | 2.0874 | 0.21 | 300 | 0.4192 |
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- | 2.2178 | 0.22 | 310 | 0.5130 |
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- | 1.9783 | 0.23 | 320 | 0.2481 |
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- | 1.9655 | 0.24 | 330 | 0.2947 |
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- | 1.677 | 0.24 | 340 | 0.1795 |
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- | 1.5847 | 0.25 | 350 | 0.4913 |
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- | 1.6727 | 0.26 | 360 | 0.3358 |
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- | 1.5304 | 0.26 | 370 | 0.4296 |
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- | 1.4964 | 0.27 | 380 | 0.1527 |
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- | 1.3643 | 0.28 | 390 | 0.4387 |
91
- | 1.1374 | 0.29 | 400 | 0.1458 |
92
- | 1.0719 | 0.29 | 410 | 0.1550 |
93
- | 1.2705 | 0.3 | 420 | 0.3249 |
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- | 0.863 | 0.31 | 430 | 0.1285 |
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- | 0.9644 | 0.31 | 440 | 0.2107 |
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- | 0.9679 | 0.32 | 450 | 0.1729 |
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- | 0.9753 | 0.33 | 460 | 0.2159 |
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- | 0.7938 | 0.33 | 470 | 0.3218 |
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- | 0.739 | 0.34 | 480 | 0.1385 |
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- | 0.6355 | 0.35 | 490 | 0.4408 |
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- | 0.8578 | 0.36 | 500 | 0.1109 |
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- | 0.758 | 0.36 | 510 | 0.1401 |
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- | 0.5957 | 0.37 | 520 | 0.1470 |
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- | 0.5933 | 0.38 | 530 | 0.1215 |
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- | 0.6636 | 0.38 | 540 | 0.2513 |
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- | 0.6857 | 0.39 | 550 | 0.1407 |
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- | 0.5623 | 0.4 | 560 | 0.1325 |
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- | 0.5055 | 0.41 | 570 | 0.1624 |
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- | 0.5866 | 0.41 | 580 | 0.1763 |
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- | 0.5811 | 0.42 | 590 | 0.1567 |
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- | 0.5255 | 0.43 | 600 | 0.0916 |
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- | 0.4512 | 0.43 | 610 | 0.1853 |
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- | 0.4862 | 0.44 | 620 | 0.1168 |
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- | 0.4378 | 0.45 | 630 | 0.0929 |
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- | 0.4966 | 0.46 | 640 | 0.1254 |
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- | 0.5138 | 0.46 | 650 | 0.1285 |
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- | 0.4926 | 0.47 | 660 | 0.1072 |
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- | 0.4133 | 0.48 | 670 | 0.0727 |
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- | 0.3738 | 0.48 | 680 | 0.1100 |
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- | 0.4558 | 0.49 | 690 | 0.1214 |
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- | 0.3865 | 0.5 | 700 | 0.0773 |
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- | 0.4216 | 0.51 | 710 | 0.1595 |
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- | 0.3449 | 0.51 | 720 | 0.0754 |
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- | 0.4205 | 0.52 | 730 | 0.0982 |
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- | 0.3779 | 0.53 | 740 | 0.1105 |
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- | 0.3229 | 0.53 | 750 | 0.1698 |
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- | 0.3178 | 0.54 | 760 | 0.0753 |
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- | 0.3405 | 0.55 | 770 | 0.2353 |
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- | 0.3267 | 0.56 | 780 | 0.0656 |
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- | 0.2672 | 0.56 | 790 | 0.0955 |
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- | 0.4229 | 0.57 | 800 | 0.0635 |
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- | 0.3338 | 0.58 | 810 | 0.1630 |
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- | 0.337 | 0.58 | 820 | 0.0740 |
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- | 0.2945 | 0.59 | 830 | 0.1947 |
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- | 0.3374 | 0.6 | 840 | 0.1016 |
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- | 0.3101 | 0.61 | 850 | 0.0946 |
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- | 0.2595 | 0.61 | 860 | 0.0785 |
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- | 0.3179 | 0.62 | 870 | 0.0758 |
139
- | 0.244 | 0.63 | 880 | 0.0606 |
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- | 0.3186 | 0.63 | 890 | 0.0601 |
141
- | 0.2838 | 0.64 | 900 | 0.0847 |
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- | 0.2624 | 0.65 | 910 | 0.0638 |
143
- | 0.2806 | 0.66 | 920 | 0.0915 |
144
- | 0.2859 | 0.66 | 930 | 0.0630 |
145
- | 0.213 | 0.67 | 940 | 0.0592 |
146
- | 0.2174 | 0.68 | 950 | 0.0514 |
147
- | 0.2555 | 0.68 | 960 | 0.0832 |
148
- | 0.2361 | 0.69 | 970 | 0.0442 |
149
- | 0.1854 | 0.7 | 980 | 0.1016 |
150
- | 0.2414 | 0.71 | 990 | 0.0615 |
151
- | 0.2522 | 0.71 | 1000 | 0.0420 |
152
- | 0.2331 | 0.72 | 1010 | 0.0609 |
153
- | 0.2191 | 0.73 | 1020 | 0.0605 |
154
- | 0.1605 | 0.73 | 1030 | 0.0535 |
155
- | 0.2002 | 0.74 | 1040 | 0.0607 |
156
- | 0.2003 | 0.75 | 1050 | 0.0535 |
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- | 0.2306 | 0.76 | 1060 | 0.0597 |
158
- | 0.2004 | 0.76 | 1070 | 0.0583 |
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- | 0.1524 | 0.77 | 1080 | 0.0653 |
160
- | 0.2124 | 0.78 | 1090 | 0.0543 |
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- | 0.1635 | 0.78 | 1100 | 0.0490 |
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- | 0.2245 | 0.79 | 1110 | 0.0538 |
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- | 0.2144 | 0.8 | 1120 | 0.0411 |
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- | 0.2212 | 0.81 | 1130 | 0.0421 |
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- | 0.2369 | 0.81 | 1140 | 0.0373 |
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- | 0.1499 | 0.82 | 1150 | 0.1028 |
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- | 0.2434 | 0.83 | 1160 | 0.0515 |
168
- | 0.214 | 0.83 | 1170 | 0.0388 |
169
- | 0.1667 | 0.84 | 1180 | 0.0576 |
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- | 0.2044 | 0.85 | 1190 | 0.0360 |
171
- | 0.1666 | 0.86 | 1200 | 0.0532 |
172
- | 0.1679 | 0.86 | 1210 | 0.0389 |
173
- | 0.2201 | 0.87 | 1220 | 0.0411 |
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- | 0.1384 | 0.88 | 1230 | 0.0653 |
175
- | 0.2331 | 0.88 | 1240 | 0.0364 |
176
- | 0.1344 | 0.89 | 1250 | 0.0432 |
177
- | 0.1661 | 0.9 | 1260 | 0.0604 |
178
- | 0.1689 | 0.91 | 1270 | 0.0426 |
179
- | 0.1465 | 0.91 | 1280 | 0.0448 |
180
- | 0.2009 | 0.92 | 1290 | 0.0389 |
181
- | 0.1384 | 0.93 | 1300 | 0.0362 |
182
- | 0.179 | 0.93 | 1310 | 0.0466 |
183
- | 0.1728 | 0.94 | 1320 | 0.0373 |
184
- | 0.139 | 0.95 | 1330 | 0.0469 |
185
- | 0.1359 | 0.96 | 1340 | 0.0339 |
186
- | 0.1666 | 0.96 | 1350 | 0.0390 |
187
- | 0.0943 | 0.97 | 1360 | 0.0359 |
188
- | 0.1155 | 0.98 | 1370 | 0.0499 |
189
- | 0.1176 | 0.98 | 1380 | 0.0390 |
190
- | 0.1034 | 0.99 | 1390 | 0.0603 |
191
- | 0.1147 | 1.0 | 1400 | 0.0370 |
192
- | 0.127 | 1.0 | 1410 | 0.0513 |
193
- | 0.1474 | 1.01 | 1420 | 0.0341 |
194
- | 0.1509 | 1.02 | 1430 | 0.0378 |
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- | 0.0809 | 1.03 | 1440 | 0.0521 |
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- | 0.1262 | 1.03 | 1450 | 0.0320 |
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- | 0.1305 | 1.04 | 1460 | 0.0484 |
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- | 0.1552 | 1.05 | 1470 | 0.0311 |
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- | 0.1147 | 1.05 | 1480 | 0.0341 |
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- | 0.1099 | 1.06 | 1490 | 0.0330 |
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- | 0.1196 | 1.07 | 1500 | 0.0332 |
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- | 0.0823 | 1.08 | 1510 | 0.0475 |
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- | 0.1426 | 1.08 | 1520 | 0.0377 |
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- | 0.1118 | 1.09 | 1530 | 0.0336 |
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- | 0.0665 | 1.1 | 1540 | 0.0328 |
206
- | 0.1171 | 1.1 | 1550 | 0.0324 |
207
- | 0.1166 | 1.11 | 1560 | 0.0447 |
208
- | 0.1348 | 1.12 | 1570 | 0.0342 |
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- | 0.1327 | 1.13 | 1580 | 0.0413 |
210
- | 0.1099 | 1.13 | 1590 | 0.0335 |
211
- | 0.0801 | 1.14 | 1600 | 0.0370 |
212
- | 0.13 | 1.15 | 1610 | 0.0389 |
213
- | 0.1238 | 1.15 | 1620 | 0.0516 |
214
- | 0.1092 | 1.16 | 1630 | 0.0311 |
215
- | 0.1007 | 1.17 | 1640 | 0.0399 |
216
- | 0.1142 | 1.18 | 1650 | 0.0383 |
217
- | 0.0893 | 1.18 | 1660 | 0.0328 |
218
- | 0.1115 | 1.19 | 1670 | 0.0536 |
219
- | 0.0861 | 1.2 | 1680 | 0.0289 |
220
- | 0.1141 | 1.2 | 1690 | 0.0334 |
221
- | 0.1487 | 1.21 | 1700 | 0.0314 |
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- | 0.1214 | 1.22 | 1710 | 0.0371 |
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- | 0.0876 | 1.23 | 1720 | 0.0296 |
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- | 0.0927 | 1.23 | 1730 | 0.0292 |
225
- | 0.0651 | 1.24 | 1740 | 0.0309 |
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- | 0.1355 | 1.25 | 1750 | 0.0372 |
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- | 0.0883 | 1.25 | 1760 | 0.0359 |
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- | 0.1067 | 1.26 | 1770 | 0.0305 |
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- | 0.1166 | 1.27 | 1780 | 0.0368 |
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- | 0.0603 | 1.28 | 1790 | 0.0306 |
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- | 0.073 | 1.28 | 1800 | 0.0292 |
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- | 0.1029 | 1.29 | 1810 | 0.0308 |
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- | 0.1019 | 1.3 | 1820 | 0.0279 |
234
- | 0.0989 | 1.3 | 1830 | 0.0356 |
235
- | 0.1132 | 1.31 | 1840 | 0.0506 |
236
- | 0.0978 | 1.32 | 1850 | 0.0280 |
237
- | 0.0743 | 1.33 | 1860 | 0.0305 |
238
- | 0.0573 | 1.33 | 1870 | 0.0265 |
239
- | 0.0861 | 1.34 | 1880 | 0.0303 |
240
- | 0.0782 | 1.35 | 1890 | 0.0467 |
241
- | 0.0931 | 1.35 | 1900 | 0.0286 |
242
- | 0.0812 | 1.36 | 1910 | 0.0329 |
243
- | 0.0993 | 1.37 | 1920 | 0.0440 |
244
- | 0.1547 | 1.38 | 1930 | 0.0411 |
245
- | 0.081 | 1.38 | 1940 | 0.0308 |
246
- | 0.1014 | 1.39 | 1950 | 0.0289 |
247
- | 0.0674 | 1.4 | 1960 | 0.0362 |
248
- | 0.1119 | 1.4 | 1970 | 0.0412 |
249
- | 0.0996 | 1.41 | 1980 | 0.0267 |
250
- | 0.1239 | 1.42 | 1990 | 0.0272 |
251
- | 0.0919 | 1.43 | 2000 | 0.0334 |
252
- | 0.1352 | 1.43 | 2010 | 0.0276 |
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- | 0.068 | 1.44 | 2020 | 0.0283 |
254
- | 0.094 | 1.45 | 2030 | 0.0282 |
255
- | 0.0844 | 1.45 | 2040 | 0.0315 |
256
- | 0.0486 | 1.46 | 2050 | 0.0247 |
257
- | 0.0721 | 1.47 | 2060 | 0.0275 |
258
- | 0.1169 | 1.48 | 2070 | 0.0331 |
259
- | 0.1055 | 1.48 | 2080 | 0.0292 |
260
- | 0.0665 | 1.49 | 2090 | 0.0241 |
261
- | 0.078 | 1.5 | 2100 | 0.0245 |
262
- | 0.0923 | 1.5 | 2110 | 0.0277 |
263
- | 0.0983 | 1.51 | 2120 | 0.0292 |
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- | 0.0993 | 1.52 | 2130 | 0.0241 |
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- | 0.0381 | 1.53 | 2140 | 0.0316 |
266
- | 0.0614 | 1.53 | 2150 | 0.0269 |
267
- | 0.0616 | 1.54 | 2160 | 0.0239 |
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- | 0.0535 | 1.55 | 2170 | 0.0246 |
269
- | 0.0645 | 1.55 | 2180 | 0.0286 |
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- | 0.0895 | 1.56 | 2190 | 0.0310 |
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- | 0.0963 | 1.57 | 2200 | 0.0266 |
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- | 0.087 | 1.58 | 2210 | 0.0253 |
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- | 0.0976 | 1.58 | 2220 | 0.0252 |
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- | 0.1 | 1.59 | 2230 | 0.0332 |
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- | 0.0679 | 1.6 | 2240 | 0.0301 |
276
- | 0.0949 | 1.6 | 2250 | 0.0263 |
277
- | 0.0508 | 1.61 | 2260 | 0.0232 |
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- | 0.0619 | 1.62 | 2270 | 0.0274 |
279
- | 0.0649 | 1.63 | 2280 | 0.0239 |
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- | 0.0837 | 1.63 | 2290 | 0.0253 |
281
- | 0.0903 | 1.64 | 2300 | 0.0262 |
282
- | 0.0655 | 1.65 | 2310 | 0.0274 |
283
- | 0.0782 | 1.65 | 2320 | 0.0340 |
284
- | 0.0905 | 1.66 | 2330 | 0.0279 |
285
- | 0.1116 | 1.67 | 2340 | 0.0278 |
286
- | 0.06 | 1.67 | 2350 | 0.0256 |
287
- | 0.0915 | 1.68 | 2360 | 0.0285 |
288
- | 0.0826 | 1.69 | 2370 | 0.0259 |
289
- | 0.0593 | 1.7 | 2380 | 0.0265 |
290
- | 0.0551 | 1.7 | 2390 | 0.0247 |
291
- | 0.0732 | 1.71 | 2400 | 0.0252 |
292
- | 0.0936 | 1.72 | 2410 | 0.0271 |
293
- | 0.0706 | 1.72 | 2420 | 0.0263 |
294
- | 0.0544 | 1.73 | 2430 | 0.0250 |
295
- | 0.0606 | 1.74 | 2440 | 0.0247 |
296
- | 0.0707 | 1.75 | 2450 | 0.0256 |
297
- | 0.0759 | 1.75 | 2460 | 0.0269 |
298
- | 0.0688 | 1.76 | 2470 | 0.0260 |
299
- | 0.0537 | 1.77 | 2480 | 0.0239 |
300
- | 0.0979 | 1.77 | 2490 | 0.0246 |
301
- | 0.0899 | 1.78 | 2500 | 0.0263 |
302
- | 0.0834 | 1.79 | 2510 | 0.0274 |
303
- | 0.0597 | 1.8 | 2520 | 0.0253 |
304
- | 0.0807 | 1.8 | 2530 | 0.0250 |
305
- | 0.0902 | 1.81 | 2540 | 0.0221 |
306
- | 0.0849 | 1.82 | 2550 | 0.0223 |
307
- | 0.0722 | 1.82 | 2560 | 0.0222 |
308
- | 0.0647 | 1.83 | 2570 | 0.0211 |
309
- | 0.0789 | 1.84 | 2580 | 0.0217 |
310
- | 0.0839 | 1.85 | 2590 | 0.0248 |
311
- | 0.0761 | 1.85 | 2600 | 0.0252 |
312
- | 0.1191 | 1.86 | 2610 | 0.0267 |
313
- | 0.093 | 1.87 | 2620 | 0.0254 |
314
- | 0.0581 | 1.87 | 2630 | 0.0245 |
315
- | 0.0776 | 1.88 | 2640 | 0.0246 |
316
- | 0.0699 | 1.89 | 2650 | 0.0242 |
317
- | 0.07 | 1.9 | 2660 | 0.0246 |
318
- | 0.0523 | 1.9 | 2670 | 0.0238 |
319
- | 0.0773 | 1.91 | 2680 | 0.0226 |
320
- | 0.0781 | 1.92 | 2690 | 0.0221 |
321
- | 0.0593 | 1.92 | 2700 | 0.0223 |
322
- | 0.0955 | 1.93 | 2710 | 0.0234 |
323
- | 0.0662 | 1.94 | 2720 | 0.0235 |
324
- | 0.0704 | 1.95 | 2730 | 0.0229 |
325
- | 0.0785 | 1.95 | 2740 | 0.0222 |
326
- | 0.0778 | 1.96 | 2750 | 0.0219 |
327
- | 0.0462 | 1.97 | 2760 | 0.0220 |
328
- | 0.0596 | 1.97 | 2770 | 0.0222 |
329
- | 0.0599 | 1.98 | 2780 | 0.0224 |
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- | 0.0489 | 1.99 | 2790 | 0.0226 |
331
- | 0.0738 | 2.0 | 2800 | 0.0226 |
332
 
333
 
334
  ### Framework versions
335
 
336
- - Transformers 4.38.2
337
- - Pytorch 2.1.2
338
- - Datasets 2.1.0
339
- - Tokenizers 0.15.2
 
3
  base_model: google/t5-efficient-tiny
4
  tags:
5
  - generated_from_trainer
6
+ datasets:
7
+ - generator
8
+ metrics:
9
+ - accuracy
10
  model-index:
11
  - name: salt_language_ID
12
+ results:
13
+ - task:
14
+ name: Sequence-to-sequence Language Modeling
15
+ type: text2text-generation
16
+ dataset:
17
+ name: generator
18
+ type: generator
19
+ config: default
20
+ split: train
21
+ args: default
22
+ metrics:
23
+ - name: Accuracy
24
+ type: accuracy
25
+ value: 0.980510752688172
26
  ---
27
 
28
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
30
 
31
  # salt_language_ID
32
 
33
+ This model is a fine-tuned version of [google/t5-efficient-tiny](https://huggingface.co/google/t5-efficient-tiny) on the generator dataset.
34
  It achieves the following results on the evaluation set:
35
+ - Loss: 0.0127
36
+ - Accuracy: 0.9805
37
 
38
  ## Model description
39
 
 
52
  ### Training hyperparameters
53
 
54
  The following hyperparameters were used during training:
55
+ - learning_rate: 0.001
56
+ - train_batch_size: 64
57
+ - eval_batch_size: 64
58
  - seed: 42
 
 
59
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
60
  - lr_scheduler_type: linear
61
+ - lr_scheduler_warmup_steps: 10
62
+ - training_steps: 20000
63
 
64
  ### Training results
65
 
66
+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
67
+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|
68
+ | 0.5069 | 0.025 | 500 | 0.1145 | 0.8337 |
69
+ | 0.0644 | 0.05 | 1000 | 0.0489 | 0.9170 |
70
+ | 0.0511 | 0.075 | 1500 | 0.0605 | 0.9056 |
71
+ | 0.0462 | 0.1 | 2000 | 0.0332 | 0.9432 |
72
+ | 0.0411 | 0.125 | 2500 | 0.0358 | 0.9385 |
73
+ | 0.0409 | 0.15 | 3000 | 0.0267 | 0.9509 |
74
+ | 0.0365 | 0.175 | 3500 | 0.0244 | 0.9563 |
75
+ | 0.0359 | 0.2 | 4000 | 0.0285 | 0.9536 |
76
+ | 0.035 | 0.225 | 4500 | 0.0355 | 0.9388 |
77
+ | 0.0321 | 0.25 | 5000 | 0.0264 | 0.9570 |
78
+ | 0.0327 | 0.275 | 5500 | 0.0278 | 0.9513 |
79
+ | 0.0313 | 0.3 | 6000 | 0.0217 | 0.9630 |
80
+ | 0.0305 | 0.325 | 6500 | 0.0255 | 0.9556 |
81
+ | 0.0285 | 0.35 | 7000 | 0.0187 | 0.9630 |
82
+ | 0.0293 | 0.375 | 7500 | 0.0225 | 0.9620 |
83
+ | 0.0264 | 0.4 | 8000 | 0.0228 | 0.9614 |
84
+ | 0.0272 | 0.425 | 8500 | 0.0195 | 0.9664 |
85
+ | 0.0268 | 0.45 | 9000 | 0.0178 | 0.9688 |
86
+ | 0.0259 | 0.475 | 9500 | 0.0164 | 0.9677 |
87
+ | 0.0256 | 0.5 | 10000 | 0.0167 | 0.9721 |
88
+ | 0.0241 | 0.525 | 10500 | 0.0182 | 0.9647 |
89
+ | 0.0235 | 0.55 | 11000 | 0.0212 | 0.9657 |
90
+ | 0.0239 | 0.575 | 11500 | 0.0145 | 0.9735 |
91
+ | 0.0239 | 0.6 | 12000 | 0.0173 | 0.9704 |
92
+ | 0.0234 | 0.625 | 12500 | 0.0152 | 0.9768 |
93
+ | 0.0229 | 0.65 | 13000 | 0.0181 | 0.9698 |
94
+ | 0.023 | 0.675 | 13500 | 0.0154 | 0.9735 |
95
+ | 0.0224 | 0.7 | 14000 | 0.0157 | 0.9708 |
96
+ | 0.0221 | 0.725 | 14500 | 0.0155 | 0.9714 |
97
+ | 0.0219 | 0.75 | 15000 | 0.0145 | 0.9755 |
98
+ | 0.0213 | 0.775 | 15500 | 0.0159 | 0.9735 |
99
+ | 0.0197 | 0.8 | 16000 | 0.0129 | 0.9751 |
100
+ | 0.0206 | 0.825 | 16500 | 0.0154 | 0.9724 |
101
+ | 0.02 | 0.85 | 17000 | 0.0140 | 0.9724 |
102
+ | 0.0209 | 0.875 | 17500 | 0.0115 | 0.9772 |
103
+ | 0.0191 | 0.9 | 18000 | 0.0129 | 0.9735 |
104
+ | 0.0194 | 0.925 | 18500 | 0.0120 | 0.9765 |
105
+ | 0.0191 | 0.95 | 19000 | 0.0133 | 0.9741 |
106
+ | 0.0183 | 0.975 | 19500 | 0.0166 | 0.9731 |
107
+ | 0.0207 | 1.0 | 20000 | 0.0127 | 0.9805 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
108
 
109
 
110
  ### Framework versions
111
 
112
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113
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+ - Datasets 2.19.1
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+ - Tokenizers 0.19.1
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