| 0.2023 |
0.09 |
1000 |
0.1868 |
{'accuracy': 0.9415010561710566} |
{'recall': 0.9389451805663809} |
{'precision': 0.943274752044545} |
{'f1': 0.9411049867627274} |
| 0.1792 |
0.17 |
2000 |
0.1465 |
{'accuracy': 0.9528387291460103} |
{'recall': 0.9615484541439335} |
{'precision': 0.9446949714966392} |
{'f1': 0.9530472103004292} |
| 0.1596 |
0.26 |
3000 |
0.1871 |
{'accuracy': 0.9523645298961072} |
{'recall': 0.9399844115354637} |
{'precision': 0.9634297887448962} |
{'f1': 0.9515627054749485} |
| 0.1534 |
0.34 |
4000 |
0.1563 |
{'accuracy': 0.9518041126007674} |
{'recall': 0.974971854161254} |
{'precision': 0.9314139157772814} |
{'f1': 0.9526952695269527} |
| 0.1553 |
0.43 |
5000 |
0.1691 |
{'accuracy': 0.9513730223735828} |
{'recall': 0.93141075604053} |
{'precision': 0.9697051663510955} |
{'f1': 0.950172276702889} |
| 0.1537 |
0.52 |
6000 |
0.1347 |
{'accuracy': 0.9568478682588266} |
{'recall': 0.9644063393089114} |
{'precision': 0.9496844618795839} |
{'f1': 0.9569887852876723} |
| 0.1515 |
0.6 |
7000 |
0.1276 |
{'accuracy': 0.9565461050997974} |
{'recall': 0.9426690915389279} |
{'precision': 0.9691924138545098} |
{'f1': 0.9557467732022126} |
| 0.1453 |
0.69 |
8000 |
0.1351 |
{'accuracy': 0.960210372030866} |
{'recall': 0.9589503767212263} |
{'precision': 0.961031070994619} |
{'f1': 0.959989596428107} |
| 0.1526 |
0.78 |
9000 |
0.1423 |
{'accuracy': 0.9610725524852352} |
{'recall': 0.9612020438209059} |
{'precision': 0.9606196988056085} |
{'f1': 0.9609107830829834} |
| 0.1437 |
0.86 |
10000 |
0.1365 |
{'accuracy': 0.9599948269172738} |
{'recall': 0.9625010825322594} |
{'precision': 0.9573606684468946} |
{'f1': 0.9599239937813093} |
| 0.1317 |
0.95 |
11000 |
0.1275 |
{'accuracy': 0.9616760788032935} |
{'recall': 0.9653589676972374} |
{'precision': 0.9579752492265383} |
{'f1': 0.9616529353405513} |
| 0.125 |
1.03 |
12000 |
0.1428 |
{'accuracy': 0.9608138983489244} |
{'recall': 0.9522819780029445} |
{'precision': 0.9684692619341201} |
{'f1': 0.9603074101567617} |
| 0.1135 |
1.12 |
13000 |
0.1627 |
{'accuracy': 0.960770789326206} |
{'recall': 0.9544470425218672} |
{'precision': 0.966330556773345} |
{'f1': 0.9603520390379923} |
| 0.1096 |
1.21 |
14000 |
0.1240 |
{'accuracy': 0.9624520412122257} |
{'recall': 0.9566987096215467} |
{'precision': 0.9675074443860571} |
{'f1': 0.962072719355541} |
| 0.1213 |
1.29 |
15000 |
0.1502 |
{'accuracy': 0.9616760788032935} |
{'recall': 0.9659651857625358} |
{'precision': 0.9574248927038627} |
{'f1': 0.9616760788032936} |
| 0.1166 |
1.38 |
16000 |
0.1574 |
{'accuracy': 0.958873992326594} |
{'recall': 0.9438815276695246} |
{'precision': 0.9726907630522088} |
{'f1': 0.9580696202531646} |
| 0.1214 |
1.47 |
17000 |
0.1626 |
{'accuracy': 0.9562443419407682} |
{'recall': 0.9773101238416905} |
{'precision': 0.9374480810765908} |
{'f1': 0.9569641721433114} |
| 0.1064 |
1.55 |
18000 |
0.1653 |
{'accuracy': 0.9624089321895073} |
{'recall': 0.9622412747899888} |
{'precision': 0.9622412747899888} |
{'f1': 0.9622412747899888} |
| 0.1046 |
1.64 |
19000 |
0.1608 |
{'accuracy': 0.9640039660300901} |
{'recall': 0.9697756993158396} |
{'precision': 0.9584046559397467} |
{'f1': 0.9640566484438896} |
| 0.1043 |
1.72 |
20000 |
0.1556 |
{'accuracy': 0.960770789326206} |
{'recall': 0.9493374902572097} |
{'precision': 0.9712058119961017} |
{'f1': 0.9601471489883507} |
| 0.0995 |
1.81 |
21000 |
0.1646 |
{'accuracy': 0.9602534810535845} |
{'recall': 0.9752316619035247} |
{'precision': 0.9465411448264268} |
{'f1': 0.9606722402320423} |
| 0.1065 |
1.9 |
22000 |
0.1721 |
{'accuracy': 0.9627106953485365} |
{'recall': 0.9710747380271932} |
{'precision': 0.9547854223433242} |
{'f1': 0.9628611910179897} |
| 0.1204 |
1.98 |
23000 |
0.1214 |
{'accuracy': 0.9629693494848471} |
{'recall': 0.961028838659392} |
{'precision': 0.9644533286980705} |
{'f1': 0.9627380384331756} |
| 0.0852 |
2.07 |
24000 |
0.1583 |
{'accuracy': 0.9643919472345562} |
{'recall': 0.9624144799515025} |
{'precision': 0.9659278574532811} |
{'f1': 0.9641679680721846} |
| 0.0812 |
2.16 |
25000 |
0.1594 |
{'accuracy': 0.9635728758029055} |
{'recall': 0.9572183251060882} |
{'precision': 0.9692213258505787} |
{'f1': 0.9631824321380331} |
| 0.0803 |
2.24 |
26000 |
0.1629 |
{'accuracy': 0.9639177479846532} |
{'recall': 0.9608556334978783} |
{'precision': 0.9664634146341463} |
{'f1': 0.963651365787988} |
| 0.0832 |
2.33 |
27000 |
0.1570 |
{'accuracy': 0.9631417855757209} |
{'recall': 0.9658785831817788} |
{'precision': 0.9603065266058206} |
{'f1': 0.9630844954881052} |
| 0.0887 |
2.41 |
28000 |
0.1551 |
{'accuracy': 0.9623227141440703} |
{'recall': 0.9669178141508616} |
{'precision': 0.9577936004117698} |
{'f1': 0.9623340803309774} |
| 0.084 |
2.5 |
29000 |
0.1585 |
{'accuracy': 0.9644350562572747} |
{'recall': 0.9613752489824197} |
{'precision': 0.96698606271777} |
{'f1': 0.9641724931602031} |
| 0.0807 |
2.59 |
30000 |
0.1601 |
{'accuracy': 0.9639177479846532} |
{'recall': 0.9699489044773534} |
{'precision': 0.9580838323353293} |
{'f1': 0.9639798597065025} |
| 0.079 |
2.67 |
31000 |
0.1645 |
{'accuracy': 0.9628400224166919} |
{'recall': 0.9558326838139777} |
{'precision': 0.9690929844586882} |
{'f1': 0.9624171607952564} |
| 0.0913 |
2.76 |
32000 |
0.1560 |
{'accuracy': 0.9642626201664009} |
{'recall': 0.964752749631939} |
{'precision': 0.9635011243729459} |
{'f1': 0.9641265307888701} |
| 0.0927 |
2.85 |
33000 |
0.1491 |
{'accuracy': 0.9649523645298961} |
{'recall': 0.9659651857625358} |
{'precision': 0.9637117677553136} |
{'f1': 0.9648371610224472} |
| 0.0882 |
2.93 |
34000 |
0.1543 |
{'accuracy': 0.9652972367116438} |
{'recall': 0.966571403827834} |
{'precision': 0.9638169257340242} |
{'f1': 0.9651921995935487} |
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Transformers 複数言語対応