--- license: apache-2.0 base_model: t5-small tags: - generated_from_keras_callback model-index: - name: pijarcandra22/t5Jawa2Indo results: [] --- # pijarcandra22/t5Jawa2Indo This model is a fine-tuned version of [t5-small](https://huggingface.co./t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.9572 - Validation Loss: 1.1659 - Epoch: 299 ## 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 3.8958 | 3.3598 | 0 | | 3.4684 | 3.0863 | 1 | | 3.2505 | 2.9092 | 2 | | 3.0952 | 2.7813 | 3 | | 2.9749 | 2.6834 | 4 | | 2.8813 | 2.6016 | 5 | | 2.8008 | 2.5321 | 6 | | 2.7323 | 2.4726 | 7 | | 2.6741 | 2.4187 | 8 | | 2.6219 | 2.3724 | 9 | | 2.5735 | 2.3279 | 10 | | 2.5324 | 2.2918 | 11 | | 2.4934 | 2.2575 | 12 | | 2.4570 | 2.2271 | 13 | | 2.4214 | 2.1950 | 14 | | 2.3906 | 2.1661 | 15 | | 2.3628 | 2.1396 | 16 | | 2.3341 | 2.1168 | 17 | | 2.3097 | 2.0924 | 18 | | 2.2824 | 2.0717 | 19 | | 2.2592 | 2.0504 | 20 | | 2.2377 | 2.0338 | 21 | | 2.2139 | 2.0142 | 22 | | 2.1953 | 1.9946 | 23 | | 2.1751 | 1.9793 | 24 | | 2.1572 | 1.9625 | 25 | | 2.1375 | 1.9471 | 26 | | 2.1208 | 1.9300 | 27 | | 2.1063 | 1.9190 | 28 | | 2.0866 | 1.9050 | 29 | | 2.0748 | 1.8916 | 30 | | 2.0568 | 1.8809 | 31 | | 2.0418 | 1.8682 | 32 | | 2.0274 | 1.8551 | 33 | | 2.0139 | 1.8468 | 34 | | 2.0026 | 1.8347 | 35 | | 1.9880 | 1.8248 | 36 | | 1.9746 | 1.8128 | 37 | | 1.9608 | 1.8056 | 38 | | 1.9524 | 1.7968 | 39 | | 1.9414 | 1.7840 | 40 | | 1.9269 | 1.7764 | 41 | | 1.9160 | 1.7662 | 42 | | 1.9041 | 1.7602 | 43 | | 1.8962 | 1.7503 | 44 | | 1.8826 | 1.7414 | 45 | | 1.8737 | 1.7359 | 46 | | 1.8635 | 1.7273 | 47 | | 1.8544 | 1.7207 | 48 | | 1.8476 | 1.7135 | 49 | | 1.8355 | 1.7051 | 50 | | 1.8272 | 1.6969 | 51 | | 1.8178 | 1.6906 | 52 | | 1.8079 | 1.6862 | 53 | | 1.7998 | 1.6786 | 54 | | 1.7939 | 1.6712 | 55 | | 1.7826 | 1.6628 | 56 | | 1.7752 | 1.6567 | 57 | | 1.7675 | 1.6518 | 58 | | 1.7606 | 1.6464 | 59 | | 1.7510 | 1.6408 | 60 | | 1.7456 | 1.6329 | 61 | | 1.7390 | 1.6284 | 62 | | 1.7289 | 1.6233 | 63 | | 1.7183 | 1.6176 | 64 | | 1.7127 | 1.6125 | 65 | | 1.7087 | 1.6098 | 66 | | 1.6990 | 1.5985 | 67 | | 1.6945 | 1.5934 | 68 | | 1.6872 | 1.5876 | 69 | | 1.6795 | 1.5816 | 70 | | 1.6758 | 1.5778 | 71 | | 1.6659 | 1.5742 | 72 | | 1.6603 | 1.5702 | 73 | | 1.6516 | 1.5618 | 74 | | 1.6463 | 1.5592 | 75 | | 1.6400 | 1.5541 | 76 | | 1.6354 | 1.5484 | 77 | | 1.6305 | 1.5424 | 78 | | 1.6217 | 1.5378 | 79 | | 1.6169 | 1.5338 | 80 | | 1.6102 | 1.5301 | 81 | | 1.6070 | 1.5229 | 82 | | 1.5979 | 1.5195 | 83 | | 1.5926 | 1.5163 | 84 | | 1.5875 | 1.5106 | 85 | | 1.5814 | 1.5075 | 86 | | 1.5748 | 1.5021 | 87 | | 1.5672 | 1.4984 | 88 | | 1.5657 | 1.4945 | 89 | | 1.5597 | 1.4913 | 90 | | 1.5530 | 1.4863 | 91 | | 1.5506 | 1.4821 | 92 | | 1.5437 | 1.4785 | 93 | | 1.5405 | 1.4730 | 94 | | 1.5325 | 1.4678 | 95 | | 1.5285 | 1.4666 | 96 | | 1.5233 | 1.4634 | 97 | | 1.5189 | 1.4580 | 98 | | 1.5122 | 1.4558 | 99 | | 1.5078 | 1.4517 | 100 | | 1.5059 | 1.4471 | 101 | | 1.4956 | 1.4446 | 102 | | 1.4944 | 1.4396 | 103 | | 1.4881 | 1.4371 | 104 | | 1.4851 | 1.4334 | 105 | | 1.4763 | 1.4295 | 106 | | 1.4725 | 1.4273 | 107 | | 1.4686 | 1.4243 | 108 | | 1.4663 | 1.4196 | 109 | | 1.4588 | 1.4180 | 110 | | 1.4558 | 1.4152 | 111 | | 1.4525 | 1.4127 | 112 | | 1.4465 | 1.4085 | 113 | | 1.4431 | 1.4052 | 114 | | 1.4386 | 1.4025 | 115 | | 1.4343 | 1.4000 | 116 | | 1.4306 | 1.3969 | 117 | | 1.4259 | 1.3925 | 118 | | 1.4192 | 1.3919 | 119 | | 1.4165 | 1.3886 | 120 | | 1.4109 | 1.3857 | 121 | | 1.4093 | 1.3844 | 122 | | 1.4058 | 1.3797 | 123 | | 1.4003 | 1.3779 | 124 | | 1.3992 | 1.3733 | 125 | | 1.3898 | 1.3721 | 126 | | 1.3877 | 1.3692 | 127 | | 1.3845 | 1.3681 | 128 | | 1.3821 | 1.3665 | 129 | | 1.3767 | 1.3652 | 130 | | 1.3720 | 1.3600 | 131 | | 1.3707 | 1.3572 | 132 | | 1.3674 | 1.3546 | 133 | | 1.3628 | 1.3550 | 134 | | 1.3582 | 1.3510 | 135 | | 1.3548 | 1.3484 | 136 | | 1.3518 | 1.3481 | 137 | | 1.3490 | 1.3467 | 138 | | 1.3463 | 1.3423 | 139 | | 1.3411 | 1.3401 | 140 | | 1.3367 | 1.3387 | 141 | | 1.3332 | 1.3371 | 142 | | 1.3313 | 1.3341 | 143 | | 1.3285 | 1.3304 | 144 | | 1.3235 | 1.3302 | 145 | | 1.3203 | 1.3292 | 146 | | 1.3186 | 1.3259 | 147 | | 1.3132 | 1.3230 | 148 | | 1.3106 | 1.3233 | 149 | | 1.3083 | 1.3169 | 150 | | 1.3011 | 1.3179 | 151 | | 1.2986 | 1.3151 | 152 | | 1.2975 | 1.3150 | 153 | | 1.2905 | 1.3124 | 154 | | 1.2887 | 1.3096 | 155 | | 1.2862 | 1.3105 | 156 | | 1.2831 | 1.3064 | 157 | | 1.2796 | 1.3051 | 158 | | 1.2777 | 1.3024 | 159 | | 1.2758 | 1.2993 | 160 | | 1.2694 | 1.2997 | 161 | | 1.2681 | 1.2974 | 162 | | 1.2626 | 1.2935 | 163 | | 1.2617 | 1.2946 | 164 | | 1.2592 | 1.2928 | 165 | | 1.2562 | 1.2899 | 166 | | 1.2520 | 1.2890 | 167 | | 1.2488 | 1.2876 | 168 | | 1.2468 | 1.2848 | 169 | | 1.2450 | 1.2840 | 170 | | 1.2388 | 1.2861 | 171 | | 1.2384 | 1.2815 | 172 | | 1.2331 | 1.2808 | 173 | | 1.2328 | 1.2774 | 174 | | 1.2299 | 1.2770 | 175 | | 1.2253 | 1.2752 | 176 | | 1.2251 | 1.2740 | 177 | | 1.2188 | 1.2722 | 178 | | 1.2167 | 1.2706 | 179 | | 1.2141 | 1.2679 | 180 | | 1.2125 | 1.2671 | 181 | | 1.2080 | 1.2674 | 182 | | 1.2049 | 1.2665 | 183 | | 1.2021 | 1.2635 | 184 | | 1.2013 | 1.2629 | 185 | | 1.1975 | 1.2599 | 186 | | 1.1946 | 1.2593 | 187 | | 1.1939 | 1.2599 | 188 | | 1.1897 | 1.2560 | 189 | | 1.1879 | 1.2569 | 190 | | 1.1841 | 1.2539 | 191 | | 1.1829 | 1.2540 | 192 | | 1.1804 | 1.2538 | 193 | | 1.1759 | 1.2513 | 194 | | 1.1745 | 1.2480 | 195 | | 1.1690 | 1.2483 | 196 | | 1.1686 | 1.2458 | 197 | | 1.1647 | 1.2450 | 198 | | 1.1628 | 1.2457 | 199 | | 1.1624 | 1.2461 | 200 | | 1.1584 | 1.2429 | 201 | | 1.1563 | 1.2417 | 202 | | 1.1543 | 1.2407 | 203 | | 1.1489 | 1.2391 | 204 | | 1.1464 | 1.2422 | 205 | | 1.1482 | 1.2384 | 206 | | 1.1446 | 1.2355 | 207 | | 1.1425 | 1.2351 | 208 | | 1.1373 | 1.2343 | 209 | | 1.1378 | 1.2327 | 210 | | 1.1362 | 1.2311 | 211 | | 1.1331 | 1.2304 | 212 | | 1.1315 | 1.2279 | 213 | | 1.1265 | 1.2290 | 214 | | 1.1254 | 1.2284 | 215 | | 1.1220 | 1.2276 | 216 | | 1.1208 | 1.2230 | 217 | | 1.1218 | 1.2220 | 218 | | 1.1140 | 1.2222 | 219 | | 1.1115 | 1.2205 | 220 | | 1.1120 | 1.2223 | 221 | | 1.1081 | 1.2213 | 222 | | 1.1059 | 1.2190 | 223 | | 1.1025 | 1.2186 | 224 | | 1.1031 | 1.2182 | 225 | | 1.0996 | 1.2155 | 226 | | 1.0972 | 1.2144 | 227 | | 1.0953 | 1.2136 | 228 | | 1.0929 | 1.2126 | 229 | | 1.0893 | 1.2153 | 230 | | 1.0868 | 1.2147 | 231 | | 1.0877 | 1.2114 | 232 | | 1.0834 | 1.2118 | 233 | | 1.0815 | 1.2103 | 234 | | 1.0802 | 1.2096 | 235 | | 1.0771 | 1.2110 | 236 | | 1.0740 | 1.2087 | 237 | | 1.0735 | 1.2058 | 238 | | 1.0731 | 1.2077 | 239 | | 1.0693 | 1.2051 | 240 | | 1.0667 | 1.2055 | 241 | | 1.0662 | 1.2034 | 242 | | 1.0659 | 1.2028 | 243 | | 1.0619 | 1.2009 | 244 | | 1.0601 | 1.2020 | 245 | | 1.0578 | 1.1984 | 246 | | 1.0541 | 1.2002 | 247 | | 1.0524 | 1.1992 | 248 | | 1.0474 | 1.1996 | 249 | | 1.0493 | 1.1975 | 250 | | 1.0466 | 1.1986 | 251 | | 1.0454 | 1.1955 | 252 | | 1.0448 | 1.1940 | 253 | | 1.0388 | 1.1944 | 254 | | 1.0373 | 1.1930 | 255 | | 1.0345 | 1.1956 | 256 | | 1.0330 | 1.1915 | 257 | | 1.0329 | 1.1902 | 258 | | 1.0310 | 1.1923 | 259 | | 1.0277 | 1.1905 | 260 | | 1.0282 | 1.1890 | 261 | | 1.0229 | 1.1895 | 262 | | 1.0225 | 1.1888 | 263 | | 1.0227 | 1.1877 | 264 | | 1.0207 | 1.1845 | 265 | | 1.0165 | 1.1870 | 266 | | 1.0143 | 1.1850 | 267 | | 1.0133 | 1.1838 | 268 | | 1.0107 | 1.1851 | 269 | | 1.0097 | 1.1852 | 270 | | 1.0082 | 1.1829 | 271 | | 1.0050 | 1.1824 | 272 | | 1.0032 | 1.1834 | 273 | | 1.0017 | 1.1806 | 274 | | 1.0017 | 1.1805 | 275 | | 0.9989 | 1.1814 | 276 | | 0.9985 | 1.1779 | 277 | | 0.9947 | 1.1782 | 278 | | 0.9940 | 1.1776 | 279 | | 0.9921 | 1.1779 | 280 | | 0.9909 | 1.1788 | 281 | | 0.9876 | 1.1764 | 282 | | 0.9867 | 1.1763 | 283 | | 0.9832 | 1.1762 | 284 | | 0.9795 | 1.1743 | 285 | | 0.9791 | 1.1762 | 286 | | 0.9772 | 1.1724 | 287 | | 0.9770 | 1.1729 | 288 | | 0.9754 | 1.1757 | 289 | | 0.9730 | 1.1711 | 290 | | 0.9707 | 1.1734 | 291 | | 0.9700 | 1.1732 | 292 | | 0.9683 | 1.1699 | 293 | | 0.9653 | 1.1705 | 294 | | 0.9660 | 1.1706 | 295 | | 0.9626 | 1.1679 | 296 | | 0.9625 | 1.1666 | 297 | | 0.9592 | 1.1693 | 298 | | 0.9572 | 1.1659 | 299 | ### Framework versions - Transformers 4.35.2 - TensorFlow 2.14.0 - Datasets 2.15.0 - Tokenizers 0.15.0