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MIX3_ja-en_helsinki
This model is a fine-tuned version of Helsinki-NLP/opus-mt-ja-en on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.4832
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: 0.0003
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.8699 | 0.01 | 5000 | 2.3465 |
| 2.6168 | 0.02 | 10000 | 2.2205 |
| 2.5083 | 0.03 | 15000 | 2.2382 |
| 2.4359 | 0.04 | 20000 | 2.1670 |
| 2.3821 | 0.06 | 25000 | 2.1122 |
| 2.3358 | 0.07 | 30000 | 2.0902 |
| 2.3045 | 0.08 | 35000 | 2.0461 |
| 2.2782 | 0.09 | 40000 | 2.0290 |
| 2.2481 | 0.1 | 45000 | 1.9910 |
| 2.2267 | 0.11 | 50000 | 2.0059 |
| 2.2056 | 0.12 | 55000 | 1.9858 |
| 2.1903 | 0.13 | 60000 | 1.9725 |
| 2.173 | 0.15 | 65000 | 1.9797 |
| 2.154 | 0.16 | 70000 | 1.9654 |
| 2.1429 | 0.17 | 75000 | 1.9567 |
| 2.1304 | 0.18 | 80000 | 1.9348 |
| 2.1232 | 0.19 | 85000 | 1.9361 |
| 2.116 | 0.2 | 90000 | 1.9277 |
| 2.1016 | 0.21 | 95000 | 1.9193 |
| 2.0984 | 0.22 | 100000 | 1.9064 |
| 2.0797 | 0.24 | 105000 | 1.9177 |
| 2.0767 | 0.25 | 110000 | 1.8975 |
| 2.0642 | 0.26 | 115000 | 1.8782 |
| 2.0595 | 0.27 | 120000 | 1.9012 |
| 2.0533 | 0.28 | 125000 | 1.8977 |
| 2.044 | 0.29 | 130000 | 1.8984 |
| 2.0374 | 0.3 | 135000 | 1.9221 |
| 2.0305 | 0.31 | 140000 | 1.9243 |
| 2.02 | 0.32 | 145000 | 1.8773 |
| 2.0195 | 0.34 | 150000 | 1.8676 |
| 2.0151 | 0.35 | 155000 | 1.8637 |
| 2.0065 | 0.36 | 160000 | 1.8556 |
| 2.0037 | 0.37 | 165000 | 1.8399 |
| 1.9963 | 0.38 | 170000 | 1.8452 |
| 1.9878 | 0.39 | 175000 | 1.8644 |
| 1.9871 | 0.4 | 180000 | 1.8576 |
| 1.9779 | 0.41 | 185000 | 1.8509 |
| 1.9721 | 0.43 | 190000 | 1.8405 |
| 1.9724 | 0.44 | 195000 | 1.8594 |
| 1.9685 | 0.45 | 200000 | 1.8540 |
| 1.9634 | 0.46 | 205000 | 1.8694 |
| 1.9583 | 0.47 | 210000 | 1.8591 |
| 1.9557 | 0.48 | 215000 | 1.8539 |
| 1.9494 | 0.49 | 220000 | 1.8673 |
| 1.9484 | 0.5 | 225000 | 1.8021 |
| 1.9395 | 0.52 | 230000 | 1.8309 |
| 1.9384 | 0.53 | 235000 | 1.7933 |
| 1.937 | 0.54 | 240000 | 1.8199 |
| 1.9315 | 0.55 | 245000 | 1.8065 |
| 1.9276 | 0.56 | 250000 | 1.7857 |
| 1.9248 | 0.57 | 255000 | 1.8207 |
| 1.9195 | 0.58 | 260000 | 1.7898 |
| 1.9187 | 0.59 | 265000 | 1.8097 |
| 1.9138 | 0.6 | 270000 | 1.7909 |
| 1.9094 | 0.62 | 275000 | 1.7995 |
| 1.9098 | 0.63 | 280000 | 1.8165 |
| 1.9038 | 0.64 | 285000 | 1.8132 |
| 1.9034 | 0.65 | 290000 | 1.7951 |
| 1.899 | 0.66 | 295000 | 1.7880 |
| 1.8965 | 0.67 | 300000 | 1.7953 |
| 1.8941 | 0.68 | 305000 | 1.7986 |
| 1.8919 | 0.69 | 310000 | 1.7964 |
| 1.8875 | 0.71 | 315000 | 1.8041 |
| 1.884 | 0.72 | 320000 | 1.7764 |
| 1.8798 | 0.73 | 325000 | 1.8019 |
| 1.8801 | 0.74 | 330000 | 1.7790 |
| 1.8809 | 0.75 | 335000 | 1.7849 |
| 1.8736 | 0.76 | 340000 | 1.7800 |
| 1.8727 | 0.77 | 345000 | 1.7900 |
| 1.8722 | 0.78 | 350000 | 1.7727 |
| 1.8699 | 0.8 | 355000 | 1.7597 |
| 1.8672 | 0.81 | 360000 | 1.7824 |
| 1.8638 | 0.82 | 365000 | 1.7674 |
| 1.8609 | 0.83 | 370000 | 1.7715 |
| 1.8584 | 0.84 | 375000 | 1.7694 |
| 1.8568 | 0.85 | 380000 | 1.7776 |
| 1.8523 | 0.86 | 385000 | 1.7697 |
| 1.8584 | 0.87 | 390000 | 1.7436 |
| 1.8474 | 0.88 | 395000 | 1.7644 |
| 1.8492 | 0.9 | 400000 | 1.7732 |
| 1.8465 | 0.91 | 405000 | 1.7611 |
| 1.846 | 0.92 | 410000 | 1.7717 |
| 1.8431 | 0.93 | 415000 | 1.7514 |
| 1.8402 | 0.94 | 420000 | 1.7353 |
| 1.8398 | 0.95 | 425000 | 1.7720 |
| 1.8314 | 0.96 | 430000 | 1.7728 |
| 1.8322 | 0.97 | 435000 | 1.7491 |
| 1.8284 | 0.99 | 440000 | 1.7561 |
| 1.8301 | 1.0 | 445000 | 1.7499 |
| 1.8182 | 1.01 | 450000 | 1.7514 |
| 1.8111 | 1.02 | 455000 | 1.7596 |
| 1.8116 | 1.03 | 460000 | 1.7455 |
| 1.8098 | 1.04 | 465000 | 1.7495 |
| 1.809 | 1.05 | 470000 | 1.7446 |
| 1.8088 | 1.06 | 475000 | 1.7290 |
| 1.8127 | 1.08 | 480000 | 1.7453 |
| 1.8051 | 1.09 | 485000 | 1.7495 |
| 1.8026 | 1.1 | 490000 | 1.7453 |
| 1.8028 | 1.11 | 495000 | 1.7615 |
| 1.8046 | 1.12 | 500000 | 1.7491 |
| 1.8052 | 1.13 | 505000 | 1.7280 |
| 1.7997 | 1.14 | 510000 | 1.7482 |
| 1.7976 | 1.15 | 515000 | 1.7368 |
| 1.7981 | 1.16 | 520000 | 1.7354 |
| 1.7949 | 1.18 | 525000 | 1.7076 |
| 1.7943 | 1.19 | 530000 | 1.7020 |
| 1.7911 | 1.2 | 535000 | 1.7121 |
| 1.7909 | 1.21 | 540000 | 1.7170 |
| 1.7926 | 1.22 | 545000 | 1.7310 |
| 1.7856 | 1.23 | 550000 | 1.7218 |
| 1.7875 | 1.24 | 555000 | 1.7362 |
| 1.7801 | 1.25 | 560000 | 1.7484 |
| 1.7854 | 1.27 | 565000 | 1.7466 |
| 1.7799 | 1.28 | 570000 | 1.7248 |
| 1.7823 | 1.29 | 575000 | 1.7355 |
| 1.7765 | 1.3 | 580000 | 1.7188 |
| 1.7779 | 1.31 | 585000 | 1.6993 |
| 1.7751 | 1.32 | 590000 | 1.7154 |
| 1.7762 | 1.33 | 595000 | 1.7348 |
| 1.7725 | 1.34 | 600000 | 1.7272 |
| 1.7701 | 1.36 | 605000 | 1.7157 |
| 1.7644 | 1.37 | 610000 | 1.7161 |
| 1.7707 | 1.38 | 615000 | 1.6961 |
| 1.764 | 1.39 | 620000 | 1.6930 |
| 1.7639 | 1.4 | 625000 | 1.6927 |
| 1.7654 | 1.41 | 630000 | 1.6989 |
| 1.7623 | 1.42 | 635000 | 1.6892 |
| 1.7598 | 1.43 | 640000 | 1.6911 |
| 1.7575 | 1.44 | 645000 | 1.7199 |
| 1.7574 | 1.46 | 650000 | 1.6992 |
| 1.7526 | 1.47 | 655000 | 1.6981 |
| 1.7556 | 1.48 | 660000 | 1.6860 |
| 1.7558 | 1.49 | 665000 | 1.7099 |
| 1.7539 | 1.5 | 670000 | 1.6950 |
| 1.7454 | 1.51 | 675000 | 1.6999 |
| 1.748 | 1.52 | 680000 | 1.6871 |
| 1.7476 | 1.53 | 685000 | 1.6884 |
| 1.7493 | 1.55 | 690000 | 1.6984 |
| 1.745 | 1.56 | 695000 | 1.6999 |
| 1.7397 | 1.57 | 700000 | 1.7036 |
| 1.7429 | 1.58 | 705000 | 1.7223 |
| 1.7367 | 1.59 | 710000 | 1.7111 |
| 1.7403 | 1.6 | 715000 | 1.6691 |
| 1.7361 | 1.61 | 720000 | 1.6693 |
| 1.737 | 1.62 | 725000 | 1.6884 |
| 1.7347 | 1.63 | 730000 | 1.6641 |
| 1.7323 | 1.65 | 735000 | 1.6628 |
| 1.7329 | 1.66 | 740000 | 1.6759 |
| 1.7292 | 1.67 | 745000 | 1.6654 |
| 1.7275 | 1.68 | 750000 | 1.6738 |
| 1.7266 | 1.69 | 755000 | 1.6792 |
| 1.7259 | 1.7 | 760000 | 1.6752 |
| 1.7231 | 1.71 | 765000 | 1.6641 |
| 1.7238 | 1.72 | 770000 | 1.6676 |
| 1.7223 | 1.74 | 775000 | 1.6563 |
| 1.722 | 1.75 | 780000 | 1.6541 |
| 1.7195 | 1.76 | 785000 | 1.6560 |
| 1.7171 | 1.77 | 790000 | 1.6786 |
| 1.7187 | 1.78 | 795000 | 1.6434 |
| 1.7186 | 1.79 | 800000 | 1.6538 |
| 1.7115 | 1.8 | 805000 | 1.6535 |
| 1.7119 | 1.81 | 810000 | 1.6738 |
| 1.7106 | 1.83 | 815000 | 1.6597 |
| 1.7088 | 1.84 | 820000 | 1.6486 |
| 1.7079 | 1.85 | 825000 | 1.6576 |
| 1.7062 | 1.86 | 830000 | 1.6676 |
| 1.7084 | 1.87 | 835000 | 1.6449 |
| 1.7059 | 1.88 | 840000 | 1.6515 |
| 1.7057 | 1.89 | 845000 | 1.6609 |
| 1.7021 | 1.9 | 850000 | 1.6482 |
| 1.7005 | 1.91 | 855000 | 1.6653 |
| 1.6988 | 1.93 | 860000 | 1.6801 |
| 1.6964 | 1.94 | 865000 | 1.6830 |
| 1.6954 | 1.95 | 870000 | 1.6589 |
| 1.693 | 1.96 | 875000 | 1.6553 |
| 1.689 | 1.97 | 880000 | 1.6554 |
| 1.69 | 1.98 | 885000 | 1.6424 |
| 1.6893 | 1.99 | 890000 | 1.6628 |
| 1.6772 | 2.0 | 895000 | 1.6709 |
| 1.6703 | 2.02 | 900000 | 1.6627 |
| 1.6726 | 2.03 | 905000 | 1.6612 |
| 1.669 | 2.04 | 910000 | 1.6595 |
| 1.6696 | 2.05 | 915000 | 1.6427 |
| 1.6672 | 2.06 | 920000 | 1.6497 |
| 1.669 | 2.07 | 925000 | 1.6288 |
| 1.6675 | 2.08 | 930000 | 1.6443 |
| 1.6685 | 2.09 | 935000 | 1.6316 |
| 1.6671 | 2.11 | 940000 | 1.6451 |
| 1.6673 | 2.12 | 945000 | 1.6313 |
| 1.6649 | 2.13 | 950000 | 1.6363 |
| 1.6655 | 2.14 | 955000 | 1.6440 |
| 1.6637 | 2.15 | 960000 | 1.6238 |
| 1.6632 | 2.16 | 965000 | 1.6226 |
| 1.6599 | 2.17 | 970000 | 1.6171 |
| 1.6602 | 2.18 | 975000 | 1.6466 |
| 1.658 | 2.19 | 980000 | 1.6341 |
| 1.6571 | 2.21 | 985000 | 1.6500 |
| 1.6572 | 2.22 | 990000 | 1.6225 |
| 1.6572 | 2.23 | 995000 | 1.6296 |
| 1.6552 | 2.24 | 1000000 | 1.6437 |
| 1.6548 | 2.25 | 1005000 | 1.6162 |
| 1.6552 | 2.26 | 1010000 | 1.6223 |
| 1.6544 | 2.27 | 1015000 | 1.6355 |
| 1.6464 | 2.28 | 1020000 | 1.6250 |
| 1.652 | 2.3 | 1025000 | 1.6217 |
| 1.6481 | 2.31 | 1030000 | 1.6079 |
| 1.6466 | 2.32 | 1035000 | 1.6110 |
| 1.6462 | 2.33 | 1040000 | 1.6210 |
| 1.6448 | 2.34 | 1045000 | 1.5993 |
| 1.6461 | 2.35 | 1050000 | 1.6096 |
| 1.6396 | 2.36 | 1055000 | 1.6137 |
| 1.644 | 2.37 | 1060000 | 1.6189 |
| 1.6396 | 2.39 | 1065000 | 1.6211 |
| 1.639 | 2.4 | 1070000 | 1.6149 |
| 1.6358 | 2.41 | 1075000 | 1.6144 |
| 1.6356 | 2.42 | 1080000 | 1.6018 |
| 1.6364 | 2.43 | 1085000 | 1.5999 |
| 1.6352 | 2.44 | 1090000 | 1.6095 |
| 1.634 | 2.45 | 1095000 | 1.6114 |
| 1.6279 | 2.46 | 1100000 | 1.6156 |
| 1.6272 | 2.47 | 1105000 | 1.6124 |
| 1.6319 | 2.49 | 1110000 | 1.6046 |
| 1.6276 | 2.5 | 1115000 | 1.6152 |
| 1.6285 | 2.51 | 1120000 | 1.6129 |
| 1.6242 | 2.52 | 1125000 | 1.5984 |
| 1.6261 | 2.53 | 1130000 | 1.6116 |
| 1.623 | 2.54 | 1135000 | 1.6061 |
| 1.6203 | 2.55 | 1140000 | 1.6182 |
| 1.62 | 2.56 | 1145000 | 1.5887 |
| 1.6177 | 2.58 | 1150000 | 1.5731 |
| 1.6172 | 2.59 | 1155000 | 1.5990 |
| 1.6179 | 2.6 | 1160000 | 1.5965 |
| 1.6206 | 2.61 | 1165000 | 1.6000 |
| 1.6156 | 2.62 | 1170000 | 1.5873 |
| 1.6124 | 2.63 | 1175000 | 1.5899 |
| 1.613 | 2.64 | 1180000 | 1.5910 |
| 1.6134 | 2.65 | 1185000 | 1.6017 |
| 1.609 | 2.67 | 1190000 | 1.5822 |
| 1.6084 | 2.68 | 1195000 | 1.5906 |
| 1.6101 | 2.69 | 1200000 | 1.6218 |
| 1.6077 | 2.7 | 1205000 | 1.6149 |
| 1.6057 | 2.71 | 1210000 | 1.5994 |
| 1.6018 | 2.72 | 1215000 | 1.5839 |
| 1.6049 | 2.73 | 1220000 | 1.5864 |
| 1.6012 | 2.74 | 1225000 | 1.5994 |
| 1.6013 | 2.75 | 1230000 | 1.5821 |
| 1.5957 | 2.77 | 1235000 | 1.5964 |
| 1.5971 | 2.78 | 1240000 | 1.5897 |
| 1.5967 | 2.79 | 1245000 | 1.5774 |
| 1.5927 | 2.8 | 1250000 | 1.5861 |
| 1.5954 | 2.81 | 1255000 | 1.5789 |
| 1.5937 | 2.82 | 1260000 | 1.5739 |
| 1.5895 | 2.83 | 1265000 | 1.5701 |
| 1.5912 | 2.84 | 1270000 | 1.5622 |
| 1.5922 | 2.86 | 1275000 | 1.5730 |
| 1.5883 | 2.87 | 1280000 | 1.5775 |
| 1.5864 | 2.88 | 1285000 | 1.5726 |
| 1.5837 | 2.89 | 1290000 | 1.5679 |
| 1.5824 | 2.9 | 1295000 | 1.5683 |
| 1.5817 | 2.91 | 1300000 | 1.5508 |
| 1.5778 | 2.92 | 1305000 | 1.5620 |
| 1.5822 | 2.93 | 1310000 | 1.5556 |
| 1.5783 | 2.95 | 1315000 | 1.5693 |
| 1.5751 | 2.96 | 1320000 | 1.5781 |
| 1.5716 | 2.97 | 1325000 | 1.5655 |
| 1.5765 | 2.98 | 1330000 | 1.5528 |
| 1.5728 | 2.99 | 1335000 | 1.5748 |
| 1.5672 | 3.0 | 1340000 | 1.5597 |
| 1.5467 | 3.01 | 1345000 | 1.5461 |
| 1.547 | 3.02 | 1350000 | 1.5516 |
| 1.5462 | 3.03 | 1355000 | 1.5519 |
| 1.5464 | 3.05 | 1360000 | 1.5593 |
| 1.5457 | 3.06 | 1365000 | 1.5576 |
| 1.5441 | 3.07 | 1370000 | 1.5653 |
| 1.544 | 3.08 | 1375000 | 1.5662 |
| 1.5467 | 3.09 | 1380000 | 1.5611 |
| 1.5439 | 3.1 | 1385000 | 1.5635 |
| 1.5449 | 3.11 | 1390000 | 1.5467 |
| 1.5417 | 3.12 | 1395000 | 1.5495 |
| 1.5428 | 3.14 | 1400000 | 1.5552 |
| 1.5432 | 3.15 | 1405000 | 1.5347 |
| 1.5401 | 3.16 | 1410000 | 1.5394 |
| 1.5391 | 3.17 | 1415000 | 1.5497 |
| 1.539 | 3.18 | 1420000 | 1.5431 |
| 1.5368 | 3.19 | 1425000 | 1.5479 |
| 1.5365 | 3.2 | 1430000 | 1.5513 |
| 1.5327 | 3.21 | 1435000 | 1.5467 |
| 1.5337 | 3.23 | 1440000 | 1.5477 |
| 1.5317 | 3.24 | 1445000 | 1.5398 |
| 1.5315 | 3.25 | 1450000 | 1.5481 |
| 1.532 | 3.26 | 1455000 | 1.5385 |
| 1.5312 | 3.27 | 1460000 | 1.5520 |
| 1.5328 | 3.28 | 1465000 | 1.5423 |
| 1.5288 | 3.29 | 1470000 | 1.5489 |
| 1.5271 | 3.3 | 1475000 | 1.5395 |
| 1.5273 | 3.31 | 1480000 | 1.5335 |
| 1.5235 | 3.33 | 1485000 | 1.5381 |
| 1.5224 | 3.34 | 1490000 | 1.5289 |
| 1.5206 | 3.35 | 1495000 | 1.5331 |
| 1.5189 | 3.36 | 1500000 | 1.5343 |
| 1.5152 | 3.37 | 1505000 | 1.5246 |
| 1.5225 | 3.38 | 1510000 | 1.5280 |
| 1.5168 | 3.39 | 1515000 | 1.5315 |
| 1.5161 | 3.4 | 1520000 | 1.5284 |
| 1.5111 | 3.42 | 1525000 | 1.5278 |
| 1.5154 | 3.43 | 1530000 | 1.5148 |
| 1.515 | 3.44 | 1535000 | 1.5286 |
| 1.5117 | 3.45 | 1540000 | 1.5291 |
| 1.5099 | 3.46 | 1545000 | 1.5320 |
| 1.5097 | 3.47 | 1550000 | 1.5323 |
| 1.5075 | 3.48 | 1555000 | 1.5157 |
| 1.5059 | 3.49 | 1560000 | 1.5214 |
| 1.5011 | 3.51 | 1565000 | 1.5199 |
| 1.5074 | 3.52 | 1570000 | 1.5114 |
| 1.5033 | 3.53 | 1575000 | 1.5145 |
| 1.5009 | 3.54 | 1580000 | 1.5184 |
| 1.4994 | 3.55 | 1585000 | 1.5125 |
| 1.5041 | 3.56 | 1590000 | 1.5048 |
| 1.5002 | 3.57 | 1595000 | 1.5156 |
| 1.4967 | 3.58 | 1600000 | 1.5176 |
| 1.4923 | 3.59 | 1605000 | 1.5128 |
| 1.495 | 3.61 | 1610000 | 1.5188 |
| 1.4929 | 3.62 | 1615000 | 1.5149 |
| 1.4921 | 3.63 | 1620000 | 1.5097 |
| 1.4916 | 3.64 | 1625000 | 1.5161 |
| 1.4852 | 3.65 | 1630000 | 1.5134 |
| 1.4881 | 3.66 | 1635000 | 1.5101 |
| 1.4873 | 3.67 | 1640000 | 1.5027 |
| 1.4911 | 3.68 | 1645000 | 1.4968 |
| 1.488 | 3.7 | 1650000 | 1.4962 |
| 1.4842 | 3.71 | 1655000 | 1.5030 |
| 1.4829 | 3.72 | 1660000 | 1.5041 |
| 1.4816 | 3.73 | 1665000 | 1.5076 |
| 1.479 | 3.74 | 1670000 | 1.5029 |
| 1.4768 | 3.75 | 1675000 | 1.5053 |
| 1.4769 | 3.76 | 1680000 | 1.5026 |
| 1.4781 | 3.77 | 1685000 | 1.5016 |
| 1.4781 | 3.79 | 1690000 | 1.5034 |
| 1.4777 | 3.8 | 1695000 | 1.4976 |
| 1.4736 | 3.81 | 1700000 | 1.5002 |
| 1.4715 | 3.82 | 1705000 | 1.4995 |
| 1.4716 | 3.83 | 1710000 | 1.4996 |
| 1.4648 | 3.84 | 1715000 | 1.4952 |
| 1.4711 | 3.85 | 1720000 | 1.4934 |
| 1.4682 | 3.86 | 1725000 | 1.4965 |
| 1.4659 | 3.87 | 1730000 | 1.4932 |
| 1.4689 | 3.89 | 1735000 | 1.4920 |
| 1.4656 | 3.9 | 1740000 | 1.4910 |
| 1.4666 | 3.91 | 1745000 | 1.4893 |
| 1.4611 | 3.92 | 1750000 | 1.4888 |
| 1.4623 | 3.93 | 1755000 | 1.4898 |
| 1.4637 | 3.94 | 1760000 | 1.4909 |
| 1.4585 | 3.95 | 1765000 | 1.4858 |
| 1.4586 | 3.96 | 1770000 | 1.4847 |
| 1.4579 | 3.98 | 1775000 | 1.4841 |
| 1.458 | 3.99 | 1780000 | 1.4840 |
| 1.4572 | 4.0 | 1785000 | 1.4832 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
