generated_from_trainer

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mt5-base-finetuned-novel-chinese-to-spanish-v1

This model is a fine-tuned version of quickman/mt5-base-finetuned-chinese-to-spanish on the None dataset. It achieves the following results on the evaluation set:

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:

Training results

Training Loss Epoch Step Validation Loss Score Counts Totals Precisions Bp Sys Len Ref Len Bleu Gen Len
2.7093 0.28 500 1.9080 0.0035 [510, 185, 91, 37] [848, 784, 720, 656] [60.14150943396226, 23.596938775510203, 12.63888888888889, 5.640243902439025] 0.0002 848 8089 0.0035 19.0
2.4994 0.55 1000 1.7520 0.0036 [524, 199, 100, 46] [842, 778, 714, 650] [62.23277909738717, 25.57840616966581, 14.005602240896359, 7.076923076923077] 0.0002 842 8089 0.0036 19.0
2.3427 0.83 1500 1.6632 0.0040 [530, 212, 109, 53] [844, 780, 716, 652] [62.796208530805686, 27.17948717948718, 15.223463687150838, 8.128834355828221] 0.0002 844 8089 0.0040 19.0
2.211 1.1 2000 1.5980 0.0050 [548, 230, 123, 66] [855, 791, 727, 663] [64.09356725146199, 29.077117572692792, 16.91884456671252, 9.95475113122172] 0.0002 855 8089 0.0050 19.0
2.1536 1.38 2500 1.5442 0.0053 [552, 239, 137, 77] [852, 788, 724, 660] [64.78873239436619, 30.32994923857868, 18.92265193370166, 11.666666666666666] 0.0002 852 8089 0.0053 19.0
2.079 1.66 3000 1.5088 0.0055 [551, 244, 142, 84] [854, 790, 726, 662] [64.51990632318501, 30.88607594936709, 19.55922865013774, 12.688821752265861] 0.0002 854 8089 0.0055 19.0
2.0374 1.93 3500 1.4768 0.0054 [557, 259, 149, 83] [849, 785, 721, 657] [65.60659599528857, 32.99363057324841, 20.665742024965326, 12.633181126331811] 0.0002 849 8089 0.0054 19.0
2.0064 2.21 4000 1.4418 0.0054 [559, 266, 157, 91] [844, 780, 716, 652] [66.23222748815166, 34.1025641025641, 21.92737430167598, 13.957055214723926] 0.0002 844 8089 0.0054 19.0
1.9536 2.48 4500 1.4194 0.0056 [557, 260, 157, 87] [849, 785, 721, 657] [65.60659599528857, 33.12101910828026, 21.7753120665742, 13.242009132420092] 0.0002 849 8089 0.0056 19.0
1.9436 2.76 5000 1.4030 0.0051 [561, 262, 151, 85] [841, 777, 713, 649] [66.70630202140309, 33.71943371943372, 21.1781206171108, 13.097072419106317] 0.0002 841 8089 0.0051 19.0
1.8939 3.04 5500 1.3826 0.0059 [568, 277, 169, 99] [848, 784, 720, 656] [66.98113207547169, 35.33163265306123, 23.47222222222222, 15.091463414634147] 0.0002 848 8089 0.0059 19.0
1.8497 3.31 6000 1.3649 0.0059 [576, 288, 180, 107] [843, 779, 715, 651] [68.32740213523131, 36.97047496790757, 25.174825174825173, 16.43625192012289] 0.0002 843 8089 0.0059 19.0
1.8177 3.59 6500 1.3575 0.0060 [585, 285, 173, 98] [847, 783, 719, 655] [69.06729634002362, 36.39846743295019, 24.061196105702365, 14.961832061068701] 0.0002 847 8089 0.0060 19.0
1.8368 3.86 7000 1.3428 0.0061 [583, 285, 171, 95] [851, 787, 723, 659] [68.50763807285547, 36.213468869123254, 23.651452282157678, 14.41578148710167] 0.0002 851 8089 0.0061 19.0
1.7906 4.14 7500 1.3295 0.0059 [581, 284, 167, 88] [850, 786, 722, 658] [68.3529411764706, 36.1323155216285, 23.130193905817176, 13.373860182370821] 0.0002 850 8089 0.0059 19.0
1.766 4.42 8000 1.3204 0.0057 [575, 279, 161, 89] [848, 784, 720, 656] [67.80660377358491, 35.58673469387755, 22.36111111111111, 13.567073170731707] 0.0002 848 8089 0.0057 19.0
1.7615 4.69 8500 1.3124 0.0061 [590, 293, 176, 100] [848, 784, 720, 656] [69.5754716981132, 37.37244897959184, 24.444444444444443, 15.24390243902439] 0.0002 848 8089 0.0061 19.0
1.7741 4.97 9000 1.3057 0.0062 [590, 298, 180, 105] [846, 782, 718, 654] [69.73995271867612, 38.107416879795394, 25.069637883008358, 16.05504587155963] 0.0002 846 8089 0.0062 19.0
1.7266 5.24 9500 1.2969 0.0062 [592, 304, 182, 104] [846, 782, 718, 654] [69.97635933806147, 38.87468030690537, 25.348189415041784, 15.902140672782874] 0.0002 846 8089 0.0062 19.0
1.7309 5.52 10000 1.2904 0.0054 [580, 287, 166, 88] [840, 776, 712, 648] [69.04761904761905, 36.98453608247423, 23.314606741573034, 13.580246913580247] 0.0002 840 8089 0.0054 19.0
1.6973 5.79 10500 1.2818 0.0059 [591, 302, 179, 100] [842, 778, 714, 650] [70.19002375296913, 38.81748071979435, 25.07002801120448, 15.384615384615385] 0.0002 842 8089 0.0059 19.0
1.6613 6.07 11000 1.2757 0.0058 [596, 302, 185, 102] [840, 776, 712, 648] [70.95238095238095, 38.91752577319588, 25.98314606741573, 15.74074074074074] 0.0002 840 8089 0.0058 19.0
1.6699 6.35 11500 1.2689 0.0063 [600, 316, 197, 113] [842, 778, 714, 650] [71.25890736342043, 40.616966580976865, 27.591036414565828, 17.384615384615383] 0.0002 842 8089 0.0063 19.0
1.6566 6.62 12000 1.2630 0.0064 [610, 320, 194, 109] [844, 780, 716, 652] [72.27488151658768, 41.02564102564103, 27.094972067039105, 16.717791411042946] 0.0002 844 8089 0.0064 19.0
1.6417 6.9 12500 1.2592 0.0065 [606, 325, 201, 116] [843, 779, 715, 651] [71.88612099644128, 41.7201540436457, 28.111888111888113, 17.81874039938556] 0.0002 843 8089 0.0065 19.0
1.6703 7.17 13000 1.2531 0.0072 [616, 325, 198, 113] [855, 791, 727, 663] [72.046783625731, 41.08723135271808, 27.235213204951858, 17.043740573152338] 0.0002 855 8089 0.0072 19.0
1.6283 7.45 13500 1.2508 0.0069 [614, 334, 209, 122] [846, 782, 718, 654] [72.57683215130024, 42.710997442455245, 29.108635097493035, 18.654434250764528] 0.0002 846 8089 0.0069 19.0
1.6139 7.73 14000 1.2485 0.0056 [595, 315, 192, 111] [833, 769, 705, 641] [71.42857142857143, 40.96228868660598, 27.23404255319149, 17.316692667706707] 0.0002 833 8089 0.0056 19.0
1.6203 8.0 14500 1.2425 0.0067 [613, 329, 203, 119] [845, 781, 717, 653] [72.54437869822485, 42.12548015364917, 28.312412831241282, 18.223583460949463] 0.0002 845 8089 0.0067 19.0
1.6289 8.28 15000 1.2414 0.0061 [603, 322, 200, 119] [837, 773, 709, 645] [72.04301075268818, 41.65588615782665, 28.208744710860366, 18.449612403100776] 0.0002 837 8089 0.0061 19.0
1.6301 8.55 15500 1.2386 0.0063 [610, 328, 205, 123] [838, 774, 710, 646] [72.79236276849642, 42.377260981912144, 28.87323943661972, 19.040247678018577] 0.0002 838 8089 0.0063 19.0
1.5992 8.83 16000 1.2379 0.0061 [603, 323, 200, 119] [837, 773, 709, 645] [72.04301075268818, 41.785252263906855, 28.208744710860366, 18.449612403100776] 0.0002 837 8089 0.0061 19.0
1.5984 9.11 16500 1.2367 0.0060 [597, 317, 195, 116] [837, 773, 709, 645] [71.32616487455198, 41.00905562742562, 27.50352609308886, 17.984496124031008] 0.0002 837 8089 0.0060 19.0
1.6026 9.38 17000 1.2336 0.0063 [606, 326, 204, 124] [838, 774, 710, 646] [72.31503579952268, 42.11886304909561, 28.732394366197184, 19.195046439628484] 0.0002 838 8089 0.0063 19.0
1.6059 9.66 17500 1.2319 0.0061 [606, 330, 206, 123] [835, 771, 707, 643] [72.57485029940119, 42.80155642023346, 29.13719943422914, 19.12908242612753] 0.0002 835 8089 0.0061 19.0
1.6227 9.93 18000 1.2294 0.0063 [609, 334, 209, 122] [837, 773, 709, 645] [72.75985663082437, 43.20827943078913, 29.478138222849083, 18.914728682170544] 0.0002 837 8089 0.0063 19.0
1.6031 10.21 18500 1.2300 0.0060 [605, 328, 203, 120] [835, 771, 707, 643] [72.45508982035928, 42.54215304798962, 28.712871287128714, 18.662519440124417] 0.0002 835 8089 0.0060 19.0
1.5746 10.49 19000 1.2301 0.0064 [612, 335, 209, 123] [838, 774, 710, 646] [73.0310262529833, 43.281653746770026, 29.43661971830986, 19.040247678018577] 0.0002 838 8089 0.0064 19.0
1.5689 10.76 19500 1.2288 0.0063 [609, 331, 205, 120] [838, 774, 710, 646] [72.67303102625299, 42.76485788113695, 28.87323943661972, 18.575851393188856] 0.0002 838 8089 0.0063 19.0
1.5928 11.04 20000 1.2288 0.0063 [609, 331, 205, 120] [838, 774, 710, 646] [72.67303102625299, 42.76485788113695, 28.87323943661972, 18.575851393188856] 0.0002 838 8089 0.0063 19.0

Framework versions