<!-- 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. -->
spanish_mlm-mix
This model is a fine-tuned version of che111/spanish_mlm on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0613
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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 300
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 42 | 5.9500 |
| No log | 2.0 | 84 | 4.7513 |
| No log | 3.0 | 126 | 4.0794 |
| 5.3419 | 4.0 | 168 | 3.5736 |
| 5.3419 | 5.0 | 210 | 3.2749 |
| 5.3419 | 6.0 | 252 | 3.0658 |
| 5.3419 | 7.0 | 294 | 2.8606 |
| 3.278 | 8.0 | 336 | 2.7112 |
| 3.278 | 9.0 | 378 | 2.5546 |
| 3.278 | 10.0 | 420 | 2.4961 |
| 3.278 | 11.0 | 462 | 2.4307 |
| 2.6895 | 12.0 | 504 | 2.3194 |
| 2.6895 | 13.0 | 546 | 2.2815 |
| 2.6895 | 14.0 | 588 | 2.2136 |
| 2.6895 | 15.0 | 630 | 2.1758 |
| 2.3782 | 16.0 | 672 | 2.1187 |
| 2.3782 | 17.0 | 714 | 2.0608 |
| 2.3782 | 18.0 | 756 | 2.0388 |
| 2.3782 | 19.0 | 798 | 2.0160 |
| 2.186 | 20.0 | 840 | 1.9685 |
| 2.186 | 21.0 | 882 | 1.9444 |
| 2.186 | 22.0 | 924 | 1.9054 |
| 2.186 | 23.0 | 966 | 1.8580 |
| 2.0439 | 24.0 | 1008 | 1.8534 |
| 2.0439 | 25.0 | 1050 | 1.8177 |
| 2.0439 | 26.0 | 1092 | 1.8268 |
| 2.0439 | 27.0 | 1134 | 1.7650 |
| 1.9344 | 28.0 | 1176 | 1.7497 |
| 1.9344 | 29.0 | 1218 | 1.7341 |
| 1.9344 | 30.0 | 1260 | 1.7258 |
| 1.9344 | 31.0 | 1302 | 1.7173 |
| 1.8419 | 32.0 | 1344 | 1.6928 |
| 1.8419 | 33.0 | 1386 | 1.6886 |
| 1.8419 | 34.0 | 1428 | 1.6449 |
| 1.8419 | 35.0 | 1470 | 1.6714 |
| 1.7739 | 36.0 | 1512 | 1.6572 |
| 1.7739 | 37.0 | 1554 | 1.6197 |
| 1.7739 | 38.0 | 1596 | 1.6021 |
| 1.7739 | 39.0 | 1638 | 1.5920 |
| 1.7081 | 40.0 | 1680 | 1.5883 |
| 1.7081 | 41.0 | 1722 | 1.5790 |
| 1.7081 | 42.0 | 1764 | 1.5516 |
| 1.7081 | 43.0 | 1806 | 1.5335 |
| 1.6567 | 44.0 | 1848 | 1.5363 |
| 1.6567 | 45.0 | 1890 | 1.5222 |
| 1.6567 | 46.0 | 1932 | 1.5366 |
| 1.6567 | 47.0 | 1974 | 1.5192 |
| 1.6096 | 48.0 | 2016 | 1.5261 |
| 1.6096 | 49.0 | 2058 | 1.5103 |
| 1.6096 | 50.0 | 2100 | 1.4700 |
| 1.6096 | 51.0 | 2142 | 1.5236 |
| 1.5683 | 52.0 | 2184 | 1.4769 |
| 1.5683 | 53.0 | 2226 | 1.4812 |
| 1.5683 | 54.0 | 2268 | 1.4569 |
| 1.5683 | 55.0 | 2310 | 1.4486 |
| 1.5298 | 56.0 | 2352 | 1.4268 |
| 1.5298 | 57.0 | 2394 | 1.4462 |
| 1.5298 | 58.0 | 2436 | 1.4322 |
| 1.5298 | 59.0 | 2478 | 1.4285 |
| 1.4985 | 60.0 | 2520 | 1.4244 |
| 1.4985 | 61.0 | 2562 | 1.4169 |
| 1.4985 | 62.0 | 2604 | 1.4175 |
| 1.4985 | 63.0 | 2646 | 1.4138 |
| 1.466 | 64.0 | 2688 | 1.3719 |
| 1.466 | 65.0 | 2730 | 1.3681 |
| 1.466 | 66.0 | 2772 | 1.3770 |
| 1.466 | 67.0 | 2814 | 1.3715 |
| 1.4392 | 68.0 | 2856 | 1.3871 |
| 1.4392 | 69.0 | 2898 | 1.3667 |
| 1.4392 | 70.0 | 2940 | 1.3694 |
| 1.4392 | 71.0 | 2982 | 1.3551 |
| 1.4138 | 72.0 | 3024 | 1.3607 |
| 1.4138 | 73.0 | 3066 | 1.3565 |
| 1.4138 | 74.0 | 3108 | 1.3428 |
| 1.4138 | 75.0 | 3150 | 1.3335 |
| 1.388 | 76.0 | 3192 | 1.3509 |
| 1.388 | 77.0 | 3234 | 1.3153 |
| 1.388 | 78.0 | 3276 | 1.3231 |
| 1.388 | 79.0 | 3318 | 1.2944 |
| 1.3723 | 80.0 | 3360 | 1.3176 |
| 1.3723 | 81.0 | 3402 | 1.3244 |
| 1.3723 | 82.0 | 3444 | 1.3017 |
| 1.3496 | 83.0 | 3486 | 1.3050 |
| 1.3496 | 84.0 | 3528 | 1.3079 |
| 1.3496 | 85.0 | 3570 | 1.2923 |
| 1.3496 | 86.0 | 3612 | 1.3047 |
| 1.3312 | 87.0 | 3654 | 1.2832 |
| 1.3312 | 88.0 | 3696 | 1.2729 |
| 1.3312 | 89.0 | 3738 | 1.2600 |
| 1.3312 | 90.0 | 3780 | 1.2604 |
| 1.313 | 91.0 | 3822 | 1.3075 |
| 1.313 | 92.0 | 3864 | 1.2712 |
| 1.313 | 93.0 | 3906 | 1.2681 |
| 1.313 | 94.0 | 3948 | 1.2699 |
| 1.2979 | 95.0 | 3990 | 1.2800 |
| 1.2979 | 96.0 | 4032 | 1.2604 |
| 1.2979 | 97.0 | 4074 | 1.2444 |
| 1.2979 | 98.0 | 4116 | 1.2730 |
| 1.2811 | 99.0 | 4158 | 1.2571 |
| 1.2811 | 100.0 | 4200 | 1.2459 |
| 1.2811 | 101.0 | 4242 | 1.2524 |
| 1.2811 | 102.0 | 4284 | 1.2556 |
| 1.2663 | 103.0 | 4326 | 1.2384 |
| 1.2663 | 104.0 | 4368 | 1.2216 |
| 1.2663 | 105.0 | 4410 | 1.2529 |
| 1.2663 | 106.0 | 4452 | 1.2171 |
| 1.2507 | 107.0 | 4494 | 1.2323 |
| 1.2507 | 108.0 | 4536 | 1.2152 |
| 1.2507 | 109.0 | 4578 | 1.2108 |
| 1.2507 | 110.0 | 4620 | 1.2110 |
| 1.2379 | 111.0 | 4662 | 1.1962 |
| 1.2379 | 112.0 | 4704 | 1.2161 |
| 1.2379 | 113.0 | 4746 | 1.2215 |
| 1.2379 | 114.0 | 4788 | 1.2114 |
| 1.2227 | 115.0 | 4830 | 1.2251 |
| 1.2227 | 116.0 | 4872 | 1.2069 |
| 1.2227 | 117.0 | 4914 | 1.2084 |
| 1.2227 | 118.0 | 4956 | 1.1883 |
| 1.2116 | 119.0 | 4998 | 1.2315 |
| 1.2116 | 120.0 | 5040 | 1.1973 |
| 1.2116 | 121.0 | 5082 | 1.1830 |
| 1.2116 | 122.0 | 5124 | 1.1748 |
| 1.2029 | 123.0 | 5166 | 1.1850 |
| 1.2029 | 124.0 | 5208 | 1.2040 |
| 1.2029 | 125.0 | 5250 | 1.1948 |
| 1.2029 | 126.0 | 5292 | 1.1992 |
| 1.1938 | 127.0 | 5334 | 1.1996 |
| 1.1938 | 128.0 | 5376 | 1.1886 |
| 1.1938 | 129.0 | 5418 | 1.1774 |
| 1.1938 | 130.0 | 5460 | 1.1844 |
| 1.181 | 131.0 | 5502 | 1.2097 |
| 1.181 | 132.0 | 5544 | 1.1771 |
| 1.181 | 133.0 | 5586 | 1.1672 |
| 1.181 | 134.0 | 5628 | 1.1708 |
| 1.1734 | 135.0 | 5670 | 1.1659 |
| 1.1734 | 136.0 | 5712 | 1.1655 |
| 1.1734 | 137.0 | 5754 | 1.1470 |
| 1.1734 | 138.0 | 5796 | 1.1508 |
| 1.1634 | 139.0 | 5838 | 1.1509 |
| 1.1634 | 140.0 | 5880 | 1.1546 |
| 1.1634 | 141.0 | 5922 | 1.1689 |
| 1.1634 | 142.0 | 5964 | 1.1536 |
| 1.1512 | 143.0 | 6006 | 1.1574 |
| 1.1512 | 144.0 | 6048 | 1.1369 |
| 1.1512 | 145.0 | 6090 | 1.1491 |
| 1.1512 | 146.0 | 6132 | 1.1581 |
| 1.1438 | 147.0 | 6174 | 1.1517 |
| 1.1438 | 148.0 | 6216 | 1.1560 |
| 1.1438 | 149.0 | 6258 | 1.1433 |
| 1.1438 | 150.0 | 6300 | 1.1408 |
| 1.1364 | 151.0 | 6342 | 1.1565 |
| 1.1364 | 152.0 | 6384 | 1.1478 |
| 1.1364 | 153.0 | 6426 | 1.1287 |
| 1.1364 | 154.0 | 6468 | 1.1287 |
| 1.1296 | 155.0 | 6510 | 1.1319 |
| 1.1296 | 156.0 | 6552 | 1.1347 |
| 1.1296 | 157.0 | 6594 | 1.1125 |
| 1.1296 | 158.0 | 6636 | 1.1241 |
| 1.1203 | 159.0 | 6678 | 1.1393 |
| 1.1203 | 160.0 | 6720 | 1.1201 |
| 1.1203 | 161.0 | 6762 | 1.1432 |
| 1.1203 | 162.0 | 6804 | 1.1386 |
| 1.1109 | 163.0 | 6846 | 1.1266 |
| 1.1109 | 164.0 | 6888 | 1.1344 |
| 1.1109 | 165.0 | 6930 | 1.1305 |
| 1.1054 | 166.0 | 6972 | 1.1269 |
| 1.1054 | 167.0 | 7014 | 1.1268 |
| 1.1054 | 168.0 | 7056 | 1.1252 |
| 1.1054 | 169.0 | 7098 | 1.1220 |
| 1.0987 | 170.0 | 7140 | 1.1327 |
| 1.0987 | 171.0 | 7182 | 1.1277 |
| 1.0987 | 172.0 | 7224 | 1.1152 |
| 1.0987 | 173.0 | 7266 | 1.1059 |
| 1.0925 | 174.0 | 7308 | 1.1138 |
| 1.0925 | 175.0 | 7350 | 1.1110 |
| 1.0925 | 176.0 | 7392 | 1.1282 |
| 1.0925 | 177.0 | 7434 | 1.1002 |
| 1.0895 | 178.0 | 7476 | 1.1103 |
| 1.0895 | 179.0 | 7518 | 1.1033 |
| 1.0895 | 180.0 | 7560 | 1.1181 |
| 1.0895 | 181.0 | 7602 | 1.0851 |
| 1.0827 | 182.0 | 7644 | 1.1038 |
| 1.0827 | 183.0 | 7686 | 1.1133 |
| 1.0827 | 184.0 | 7728 | 1.1104 |
| 1.0827 | 185.0 | 7770 | 1.1150 |
| 1.0747 | 186.0 | 7812 | 1.0945 |
| 1.0747 | 187.0 | 7854 | 1.1055 |
| 1.0747 | 188.0 | 7896 | 1.1056 |
| 1.0747 | 189.0 | 7938 | 1.1010 |
| 1.0733 | 190.0 | 7980 | 1.0929 |
| 1.0733 | 191.0 | 8022 | 1.1085 |
| 1.0733 | 192.0 | 8064 | 1.0827 |
| 1.0733 | 193.0 | 8106 | 1.1026 |
| 1.0656 | 194.0 | 8148 | 1.0983 |
| 1.0656 | 195.0 | 8190 | 1.0766 |
| 1.0656 | 196.0 | 8232 | 1.0864 |
| 1.0656 | 197.0 | 8274 | 1.0967 |
| 1.0598 | 198.0 | 8316 | 1.0998 |
| 1.0598 | 199.0 | 8358 | 1.1029 |
| 1.0598 | 200.0 | 8400 | 1.1155 |
| 1.0598 | 201.0 | 8442 | 1.0716 |
| 1.0582 | 202.0 | 8484 | 1.0981 |
| 1.0582 | 203.0 | 8526 | 1.0785 |
| 1.0582 | 204.0 | 8568 | 1.0903 |
| 1.0582 | 205.0 | 8610 | 1.0936 |
| 1.0519 | 206.0 | 8652 | 1.0909 |
| 1.0519 | 207.0 | 8694 | 1.0815 |
| 1.0519 | 208.0 | 8736 | 1.0837 |
| 1.0519 | 209.0 | 8778 | 1.1162 |
| 1.0474 | 210.0 | 8820 | 1.0747 |
| 1.0474 | 211.0 | 8862 | 1.0972 |
| 1.0474 | 212.0 | 8904 | 1.0827 |
| 1.0474 | 213.0 | 8946 | 1.0908 |
| 1.0439 | 214.0 | 8988 | 1.0716 |
| 1.0439 | 215.0 | 9030 | 1.0713 |
| 1.0439 | 216.0 | 9072 | 1.0787 |
| 1.0439 | 217.0 | 9114 | 1.0749 |
| 1.0406 | 218.0 | 9156 | 1.0843 |
| 1.0406 | 219.0 | 9198 | 1.0794 |
| 1.0406 | 220.0 | 9240 | 1.0822 |
| 1.0406 | 221.0 | 9282 | 1.0782 |
| 1.0387 | 222.0 | 9324 | 1.0710 |
| 1.0387 | 223.0 | 9366 | 1.0572 |
| 1.0387 | 224.0 | 9408 | 1.0498 |
| 1.0387 | 225.0 | 9450 | 1.0916 |
| 1.034 | 226.0 | 9492 | 1.0890 |
| 1.034 | 227.0 | 9534 | 1.0613 |
| 1.034 | 228.0 | 9576 | 1.0822 |
| 1.034 | 229.0 | 9618 | 1.0644 |
| 1.0296 | 230.0 | 9660 | 1.0578 |
| 1.0296 | 231.0 | 9702 | 1.0716 |
| 1.0296 | 232.0 | 9744 | 1.0647 |
| 1.0296 | 233.0 | 9786 | 1.0886 |
| 1.0289 | 234.0 | 9828 | 1.0633 |
| 1.0289 | 235.0 | 9870 | 1.0626 |
| 1.0289 | 236.0 | 9912 | 1.0711 |
| 1.0289 | 237.0 | 9954 | 1.0780 |
| 1.0242 | 238.0 | 9996 | 1.0731 |
| 1.0242 | 239.0 | 10038 | 1.0643 |
| 1.0242 | 240.0 | 10080 | 1.0705 |
| 1.0242 | 241.0 | 10122 | 1.0668 |
| 1.0215 | 242.0 | 10164 | 1.0696 |
| 1.0215 | 243.0 | 10206 | 1.0767 |
| 1.0215 | 244.0 | 10248 | 1.0614 |
| 1.0215 | 245.0 | 10290 | 1.0608 |
| 1.0212 | 246.0 | 10332 | 1.0547 |
| 1.0212 | 247.0 | 10374 | 1.0738 |
| 1.0212 | 248.0 | 10416 | 1.0688 |
| 1.0187 | 249.0 | 10458 | 1.0582 |
| 1.0187 | 250.0 | 10500 | 1.0479 |
| 1.0187 | 251.0 | 10542 | 1.0658 |
| 1.0187 | 252.0 | 10584 | 1.0610 |
| 1.015 | 253.0 | 10626 | 1.0524 |
| 1.015 | 254.0 | 10668 | 1.0507 |
| 1.015 | 255.0 | 10710 | 1.0573 |
| 1.015 | 256.0 | 10752 | 1.0611 |
| 1.0136 | 257.0 | 10794 | 1.0618 |
| 1.0136 | 258.0 | 10836 | 1.0712 |
| 1.0136 | 259.0 | 10878 | 1.0605 |
| 1.0136 | 260.0 | 10920 | 1.0842 |
| 1.0093 | 261.0 | 10962 | 1.0524 |
| 1.0093 | 262.0 | 11004 | 1.0243 |
| 1.0093 | 263.0 | 11046 | 1.0607 |
| 1.0093 | 264.0 | 11088 | 1.0584 |
| 1.009 | 265.0 | 11130 | 1.0593 |
| 1.009 | 266.0 | 11172 | 1.0652 |
| 1.009 | 267.0 | 11214 | 1.0512 |
| 1.009 | 268.0 | 11256 | 1.0392 |
| 1.0088 | 269.0 | 11298 | 1.0663 |
| 1.0088 | 270.0 | 11340 | 1.0705 |
| 1.0088 | 271.0 | 11382 | 1.0431 |
| 1.0088 | 272.0 | 11424 | 1.0629 |
| 1.0044 | 273.0 | 11466 | 1.0588 |
| 1.0044 | 274.0 | 11508 | 1.0501 |
| 1.0044 | 275.0 | 11550 | 1.0704 |
| 1.0044 | 276.0 | 11592 | 1.0768 |
| 1.0084 | 277.0 | 11634 | 1.0532 |
| 1.0084 | 278.0 | 11676 | 1.0556 |
| 1.0084 | 279.0 | 11718 | 1.0741 |
| 1.0084 | 280.0 | 11760 | 1.0717 |
| 1.0017 | 281.0 | 11802 | 1.0755 |
| 1.0017 | 282.0 | 11844 | 1.0776 |
| 1.0017 | 283.0 | 11886 | 1.0700 |
| 1.0017 | 284.0 | 11928 | 1.0579 |
| 1.0043 | 285.0 | 11970 | 1.0414 |
| 1.0043 | 286.0 | 12012 | 1.0447 |
| 1.0043 | 287.0 | 12054 | 1.0595 |
| 1.0043 | 288.0 | 12096 | 1.0523 |
| 1.0026 | 289.0 | 12138 | 1.0540 |
| 1.0026 | 290.0 | 12180 | 1.0591 |
| 1.0026 | 291.0 | 12222 | 1.0622 |
| 1.0026 | 292.0 | 12264 | 1.0584 |
| 1.0008 | 293.0 | 12306 | 1.0349 |
| 1.0008 | 294.0 | 12348 | 1.0472 |
| 1.0008 | 295.0 | 12390 | 1.0552 |
| 1.0008 | 296.0 | 12432 | 1.0614 |
| 1.002 | 297.0 | 12474 | 1.0770 |
| 1.002 | 298.0 | 12516 | 1.0712 |
| 1.002 | 299.0 | 12558 | 1.0773 |
| 1.002 | 300.0 | 12600 | 1.0570 |
Framework versions
- Transformers 4.26.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.13.3
