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Token Classification
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+ # Model Training Metrics
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+
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+ | Step | Training Loss | Validation Loss | Precision | Recall | F1 | Accuracy |
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+ |------|---------------|-----------------|-----------|---------|---------|----------|
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+ | 100 | No log | 0.420836 | 0.508333 | 0.053090| 0.096139| 0.953116 |
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+ | 200 | No log | 0.314840 | 0.500000 | 0.053090| 0.095987| 0.953139 |
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+ | 300 | No log | 0.284791 | 0.285714 | 0.079199| 0.124020| 0.956575 |
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+ | 400 | No log | 0.261631 | 0.291358 | 0.102698| 0.151866| 0.959082 |
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+ | 500 | 0.453300 | 0.245147 | 0.325613 | 0.104003| 0.157652| 0.958553 |
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+ | 600 | 0.453300 | 0.237948 | 0.322857 | 0.098346| 0.150767| 0.959459 |
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+ | 700 | 0.453300 | 0.217630 | 0.324943 | 0.123586| 0.179067| 0.960306 |
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+ | 800 | 0.453300 | 0.205889 | 0.351826 | 0.218016| 0.269210| 0.965566 |
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+ | 900 | 0.453300 | 0.202052 | 0.350676 | 0.225849| 0.274749| 0.965096 |
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+ | 1000 | 0.205300 | 0.179783 | 0.384560 | 0.234117| 0.291047| 0.968744 |
15
+ | 1100 | 0.205300 | 0.166758 | 0.429212 | 0.291558| 0.347240| 0.971262 |
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+ | 1200 | 0.205300 | 0.161507 | 0.417052 | 0.274587| 0.331147| 0.970897 |
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+ | 1300 | 0.205300 | 0.154404 | 0.424658 | 0.337250| 0.375940| 0.971556 |
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+ | 1400 | 0.205300 | 0.144692 | 0.434426 | 0.299826| 0.354789| 0.972969 |
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+ | 1500 | 0.163300 | 0.142567 | 0.447467 | 0.415144| 0.430700| 0.974487 |
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+ | 1600 | 0.163300 | 0.135387 | 0.454927 | 0.377720| 0.412744| 0.975063 |
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+ | 1700 | 0.163300 | 0.130163 | 0.457328 | 0.429069| 0.442748| 0.974863 |
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+ | 1800 | 0.163300 | 0.121182 | 0.479126 | 0.429504| 0.452960| 0.977123 |
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+ | 1900 | 0.163300 | 0.120131 | 0.504061 | 0.459095| 0.480528| 0.977229 |
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+ | 2000 | 0.116000 | 0.121453 | 0.482711 | 0.455614| 0.468771| 0.976570 |
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+ | 2100 | 0.116000 | 0.126089 | 0.460177 | 0.475196| 0.467566| 0.975510 |
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+ | 2200 | 0.116000 | 0.114434 | 0.503229 | 0.474761| 0.488580| 0.978994 |
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+ | 2300 | 0.116000 | 0.108219 | 0.544110 | 0.464317| 0.501057| 0.979488 |
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+ | 2400 | 0.116000 | 0.110805 | 0.525301 | 0.474326| 0.498514| 0.977994 |
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+ | 2500 | 0.090900 | 0.116738 | 0.554128 | 0.487815| 0.518861| 0.978653 |
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+ | 2600 | 0.090900 | 0.110969 | 0.564193 | 0.489556| 0.524231| 0.979982 |
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+ | 2700 | 0.090900 | 0.106849 | 0.538219 | 0.520888| 0.529412| 0.979264 |
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+ | 2800 | 0.090900 | 0.104990 | 0.526362 | 0.525674| 0.526018| 0.979253 |
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+ | 2900 | 0.090900 | 0.101015 | 0.553152 | 0.523064| 0.537687| 0.980618 |
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+ | 3000 | 0.080000 | 0.109505 | 0.532439 | 0.535683| 0.534056| 0.979335 |
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+ | 3100 | 0.080000 | 0.104904 | 0.562973 | 0.550479| 0.556656| 0.980736 |
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+ | 3200 | 0.080000 | 0.098502 | 0.554962 | 0.542646| 0.548735| 0.980688 |
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+ | 3300 | 0.080000 | 0.101882 | 0.541367 | 0.523934| 0.532508| 0.980312 |
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+ | 3400 | 0.080000 | 0.094882 | 0.550420 | 0.570061| 0.560068| 0.981159 |
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+ | 3500 | 0.067100 | 0.090791 | 0.579903 | 0.570061| 0.574940| 0.982348 |
40
+ | 3600 | 0.067100 | 0.086182 | 0.612403 | 0.584421| 0.598085| 0.984219 |
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+ | 3700 | 0.067100 | 0.093917 | 0.549979 | 0.572237| 0.560887| 0.981771 |
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+ | 3800 | 0.067100 | 0.096534 | 0.597186 | 0.590949| 0.594051| 0.982489 |
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+ | 3900 | 0.067100 | 0.094493 | 0.589077 | 0.591384| 0.590228| 0.982101 |
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+ | 4000 | 0.059000 | 0.090813 | 0.587505 | 0.597476| 0.592449| 0.982171 |
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+ | 4100 | 0.059000 | 0.089240 | 0.592105 | 0.587467| 0.589777| 0.983030 |
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+ | 4200 | 0.059000 | 0.090156 | 0.596514 | 0.610531| 0.603441| 0.983701 |
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+ | 4300 | 0.059000 | 0.085989 | 0.620291 | 0.630548| 0.625378| 0.984384 |
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+ | 4400 | 0.059000 | 0.088271 | 0.605035 | 0.606614| 0.605824| 0.984431 |
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+
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+ | 4500 | 0.051600 | 0.090031 | 0.595145 | 0.597476 | 0.596308 | 0.984089 |
51
+ | 4600 | 0.051600 | 0.088728 | 0.600799 | 0.588773 | 0.594725 | 0.984254 |
52
+ | 4700 | 0.051600 | 0.083724 | 0.624070 | 0.620540 | 0.622300 | 0.984184 |
53
+ | 4800 | 0.051600 | 0.089457 | 0.634303 | 0.645344 | 0.639776 | 0.984478 |
54
+ | 4900 | 0.051600 | 0.087367 | 0.626459 | 0.630548 | 0.628497 | 0.984054 |
55
+ | 5000 | 0.049400 | 0.084299 | 0.617598 | 0.644473 | 0.630750 | 0.983748 |
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+ | 5100 | 0.049400 | 0.086022 | 0.631395 | 0.628372 | 0.629880 | 0.984454 |
57
+ | 5200 | 0.049400 | 0.086624 | 0.631912 | 0.651436 | 0.641526 | 0.984184 |
58
+ | 5300 | 0.049400 | 0.087849 | 0.641918 | 0.658399 | 0.650054 | 0.984607 |
59
+ | 5400 | 0.049400 | 0.090842 | 0.648156 | 0.634900 | 0.641460 | 0.984254 |
60
+ | 5500 | 0.047100 | 0.089992 | 0.627105 | 0.664491 | 0.645257 | 0.984019 |
61
+ | 5600 | 0.047100 | 0.085330 | 0.638217 | 0.654047 | 0.646035 | 0.984560 |
62
+ | 5700 | 0.047100 | 0.084110 | 0.644924 | 0.649695 | 0.647301 | 0.983984 |
63
+ | 5800 | 0.047100 | 0.085505 | 0.642038 | 0.657963 | 0.649903 | 0.984584 |
64
+ | 5900 | 0.047100 | 0.086117 | 0.629599 | 0.670148 | 0.649241 | 0.984572 |
65
+ | 6000 | 0.037200 | 0.087370 | 0.640464 | 0.648825 | 0.644617 | 0.984337 |
66
+ | 6100 | 0.037200 | 0.084265 | 0.624186 | 0.667102 | 0.644931 | 0.984442 |
67
+ | 6200 | 0.037200 | 0.084804 | 0.636364 | 0.676240 | 0.655696 | 0.984678 |
68
+ | 6300 | 0.037200 | 0.083215 | 0.638559 | 0.655788 | 0.647059 | 0.984560 |
69
+ | 6400 | 0.037200 | 0.083421 | 0.658244 | 0.668842 | 0.663501 | 0.985078 |
70
+ | 6500 | 0.037600 | 0.085991 | 0.637644 | 0.650131 | 0.643827 | 0.984478 |
71
+ | 6600 | 0.037600 | 0.086048 | 0.675829 | 0.674064 | 0.674946 | 0.985184 |
72
+ | 6700 | 0.037600 | 0.085995 | 0.660017 | 0.662315 | 0.661164 | 0.985113 |
73
+ | 6800 | 0.037600 | 0.085964 | 0.663314 | 0.660139 | 0.661723 | 0.985549 |
74
+ | 6900 | 0.037600 | 0.084587 | 0.655518 | 0.682332 | 0.668657 | 0.985643 |
75
+ | 7000 | 0.035200 | 0.084067 | 0.656785 | 0.684508 | 0.670360 | 0.985325 |
76
+ | 7100 | 0.035200 | 0.083450 | 0.658670 | 0.681027 | 0.669662 | 0.985631 |
77
+ | 7200 | 0.035200 | 0.087981 | 0.665669 | 0.677546 | 0.671555 | 0.985243 |
78
+ | 7300 | 0.035200 | 0.081362 | 0.659746 | 0.677546 | 0.668527 | 0.985972 |
79
+ | 7400 | 0.035200 | 0.083816 | 0.646620 | 0.678416 | 0.662136 | 0.985678 |
80
+ | 7500 | 0.031600 | 0.084738 | 0.658670 | 0.681027 | 0.669662 | 0.985572 |
81
+ | 7600 | 0.031600 | 0.084099 | 0.669345 | 0.688860 | 0.678962 | 0.985725 |
82
+ | 7700 | 0.031600 | 0.085109 | 0.673250 | 0.682332 | 0.677761 | 0.985855 |
83
+ | 7800 | 0.031600 | 0.085043 | 0.650648 | 0.677546 | 0.663824 | 0.985219 |
84
+ | 7900 | 0.031600 | 0.086399 | 0.671350 | 0.686249 | 0.678717 | 0.985513 |
85
+ | 8000 | 0.032300 | 0.084272 | 0.668826 | 0.674064 | 0.671435 | 0.985961 |
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+ | 8100 | 0.032300 | 0.086206 | 0.656171 | 0.680157 | 0.667949 | 0.985337 |
87
+ | 8200 | 0.032300 | 0.085437 | 0.667521 | 0.679721 | 0.673566 | 0.985972 |
88
+ | 8300 | 0.032300 | 0.084903 | 0.679565 | 0.680157 | 0.679861 | 0.986090 |
89
+ | 8400 | 0.032300 | 0.084208 | 0.661384 | 0.690165 | 0.675468 | 0.985890 |
90
+ | 8500 | 0.030700 | 0.084594 | 0.674033 | 0.690165 | 0.682004 | 0.986149 |
91
+ | 8600 | 0.030700 | 0.085151 | 0.678280 | 0.686249 | 0.682241 | 0.986337 |
92
+ | 8700 | 0.030700 | 0.085606 | 0.669635 | 0.687119 | 0.678265 | 0.986008 |
93
+ | 8800 | 0.030700 | 0.085640 | 0.669499 | 0.685814 | 0.677558 | 0.985902 |
94
+ | 8900 | 0.030700 | 0.086006 | 0.671496 | 0.685814 | 0.678579 | 0.985855 |
95
+ | 9000 | 0.028000 | 0.085893 | 0.672208 | 0.686249 | 0.679156 | 0.985925 |