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Poleval 19 ablation experiments
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Piotr Czapla committed
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,model_dir_parent,model_name,test Accuracy,test F1 score bin,test Loss,test Precision,test Recall,valid Accuracy,valid F1 score bin,valid Loss,valid Precision,valid Recall
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@@ -0,0 +1,337 @@
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13,data/hate/pl-10-reddithalftest/models/sp25k,lstm_ft6_cl6_lmseed-0-ftseed-0-clsweightseed-4-clstrainseed-0.m,0.8679999709129333,0.5600000619888306,0.41203883,0.5060241222381592,0.6268656849861145,0.878000020980835,0.5906040072441101,0.5064274,0.5365853905677795,0.6567164063453674
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||||
15,data/hate/pl-10-reddithalftest/models/sp25k,lstm_ft6_cl6_lmseed-0-ftseed-0-clsweightseed-6-clstrainseed-0.m,0.8899999856948853,0.5669291615486145,0.3896014,0.6000000238418579,0.5373134613037109,0.8939999938011169,0.5759999752044678,0.44118232,0.6206896305084229,0.5373134613037109
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||||
16,data/hate/pl-10-reddithalftest/models/sp25k,lstm_ft6_cl6_lmseed-0-ftseed-0-clsweightseed-7-clstrainseed-0.m,0.8859999775886536,0.583941638469696,0.46841207,0.5714285969734192,0.5970149040222168,0.8960000276565552,0.611940324306488,0.47333395,0.611940324306488,0.611940324306488
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17,data/hate/pl-10-reddithalftest/models/sp25k,lstm_ft6_cl6_lmseed-0-ftseed-0-clsweightseed-8-clstrainseed-0.m,0.9039999842643738,0.6521738767623901,0.3624173,0.6338028311729431,0.6716417670249939,0.8980000019073486,0.5920000672340393,0.39986846,0.6379310488700867,0.5522388219833374
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18,data/hate/pl-10-reddithalftest/models/sp25k,lstm_ft6_cl6_lmseed-0-ftseed-0-clsweightseed-9-clstrainseed-0.m,0.8999999761581421,0.5901639461517334,0.35761473,0.6545454263687134,0.5373134613037109,0.9020000100135803,0.6016260385513306,0.43883625,0.6607142686843872,0.5522388219833374
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19,data/hate/pl-10-reddithalftest/models/sp25k,lstm_ft6_cl6_lmseed-1-ftseed-0-clsweightseed-0-clstrainseed-0.m,0.8740000128746033,0.5771811604499817,0.392705,0.5243902206420898,0.641791045665741,0.878000020980835,0.5906040072441101,0.41654465,0.5365853905677795,0.6567164063453674
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20,data/hate/pl-10-reddithalftest/models/sp25k,lstm_ft6_cl6_lmseed-1-ftseed-0-clsweightseed-0-clstrainseed-1.m,0.8939999938011169,0.5546218752861023,0.47778156,0.6346153616905212,0.49253731966018677,0.8920000195503235,0.5645161271095276,0.53104866,0.6140350699424744,0.5223880410194397
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21,data/hate/pl-10-reddithalftest/models/sp25k,lstm_ft6_cl6_lmseed-1-ftseed-0-clsweightseed-0-clstrainseed-2.m,0.8980000019073486,0.5920000672340393,0.47931838,0.6379310488700867,0.5522388219833374,0.8999999761581421,0.5901639461517334,0.4851866,0.6545454263687134,0.5373134613037109
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22,data/hate/pl-10-reddithalftest/models/sp25k,lstm_ft6_cl6_lmseed-1-ftseed-0-clsweightseed-0-clstrainseed-3.m,0.8740000128746033,0.5771811604499817,0.40035722,0.5243902206420898,0.641791045665741,0.8700000047683716,0.5806452035903931,0.45984724,0.5113636255264282,0.6716417670249939
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23,data/hate/pl-10-reddithalftest/models/sp25k,lstm_ft6_cl6_lmseed-1-ftseed-0-clsweightseed-0-clstrainseed-4.m,0.8820000290870667,0.5496183633804321,0.39264694,0.5625,0.5373134613037109,0.8980000019073486,0.604651153087616,0.4343224,0.6290322542190552,0.5820895433425903
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||||
24,data/hate/pl-10-reddithalftest/models/sp25k,lstm_ft6_cl6_lmseed-1-ftseed-0-clsweightseed-0-clstrainseed-5.m,0.8679999709129333,0.5925925374031067,0.37109935,0.5052631497383118,0.7164179086685181,0.8600000143051147,0.5512821078300476,0.38006923,0.483146071434021,0.641791045665741
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||||
25,data/hate/pl-10-reddithalftest/models/sp25k,lstm_ft6_cl6_lmseed-1-ftseed-0-clsweightseed-0-clstrainseed-6.m,0.8560000061988831,0.5955056548118591,0.37936625,0.477477490901947,0.7910447716712952,0.8640000224113464,0.5853658318519592,0.41693875,0.49484536051750183,0.7164179086685181
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||||
26,data/hate/pl-10-reddithalftest/models/sp25k,lstm_ft6_cl6_lmseed-1-ftseed-0-clsweightseed-0-clstrainseed-7.m,0.8999999761581421,0.6093749403953552,0.38283503,0.6393442749977112,0.5820895433425903,0.8960000276565552,0.6060606241226196,0.42839062,0.6153846383094788,0.5970149040222168
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28,data/hate/pl-10-reddithalftest/models/sp25k,lstm_ft6_cl6_lmseed-1-ftseed-0-clsweightseed-0-clstrainseed-9.m,0.8880000114440918,0.5254237651824951,0.4819395,0.6078431606292725,0.46268656849861145,0.8960000276565552,0.5873015522956848,0.50628275,0.6271186470985413,0.5522388219833374
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29,data/hate/pl-10-reddithalftest/models/sp25k,lstm_ft6_cl6_lmseed-1-ftseed-0-clsweightseed-1-clstrainseed-0.m,0.8939999938011169,0.6131386756896973,0.41202766,0.6000000238418579,0.6268656849861145,0.8899999856948853,0.598540186882019,0.44798866,0.5857142806053162,0.611940324306488
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30,data/hate/pl-10-reddithalftest/models/sp25k,lstm_ft6_cl6_lmseed-1-ftseed-0-clsweightseed-2-clstrainseed-0.m,0.8759999871253967,0.6025640964508057,0.46333593,0.5280898809432983,0.7014925479888916,0.871999979019165,0.5789474248886108,0.4813055,0.5176470875740051,0.6567164063453674
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31,data/hate/pl-10-reddithalftest/models/sp25k,lstm_ft6_cl6_lmseed-1-ftseed-0-clsweightseed-3-clstrainseed-0.m,0.8700000047683716,0.5454545021057129,0.47112042,0.5131579041481018,0.5820895433425903,0.8820000290870667,0.5755395889282227,0.44574717,0.5555555820465088,0.5970149040222168
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||||
33,data/hate/pl-10-reddithalftest/models/sp25k,lstm_ft6_cl6_lmseed-1-ftseed-0-clsweightseed-5-clstrainseed-0.m,0.8859999775886536,0.6068965196609497,0.35671037,0.5641025900840759,0.6567164063453674,0.8799999952316284,0.6000000238418579,0.44078156,0.5421686768531799,0.6716417670249939
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34,data/hate/pl-10-reddithalftest/models/sp25k,lstm_ft6_cl6_lmseed-1-ftseed-0-clsweightseed-6-clstrainseed-0.m,0.8960000276565552,0.59375,0.4449243,0.6229507923126221,0.5671641826629639,0.8999999761581421,0.60317462682724,0.43849403,0.6440678238868713,0.5671641826629639
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35,data/hate/pl-10-reddithalftest/models/sp25k,lstm_ft6_cl6_lmseed-1-ftseed-0-clsweightseed-7-clstrainseed-0.m,0.8899999856948853,0.5454545617103577,0.48189583,0.6111111044883728,0.49253731966018677,0.8980000019073486,0.5920000672340393,0.46926725,0.6379310488700867,0.5522388219833374
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||||
36,data/hate/pl-10-reddithalftest/models/sp25k,lstm_ft6_cl6_lmseed-1-ftseed-0-clsweightseed-8-clstrainseed-0.m,0.9079999923706055,0.6290322542190552,0.42785713,0.6842105388641357,0.5820895433425903,0.8980000019073486,0.5984251499176025,0.47639287,0.6333333253860474,0.5671641826629639
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||||
37,data/hate/pl-10-reddithalftest/models/sp25k,lstm_ft6_cl6_lmseed-1-ftseed-0-clsweightseed-9-clstrainseed-0.m,0.8980000019073486,0.6277371644973755,0.40639192,0.6142857074737549,0.641791045665741,0.8939999938011169,0.6293706893920898,0.4490605,0.5921052694320679,0.6716417670249939
|
||||
|
@@ -0,0 +1,696 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from fastai.text import *"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 101,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
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|
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"<div>\n",
|
||||
"<style scoped>\n",
|
||||
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||||
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|
||||
" <thead>\n",
|
||||
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|
||||
" <th></th>\n",
|
||||
" <th>index</th>\n",
|
||||
" <th>LMseed</th>\n",
|
||||
" <th>ft</th>\n",
|
||||
" <th>clsseed</th>\n",
|
||||
" <th>dataset</th>\n",
|
||||
" <th>F1_score</th>\n",
|
||||
" <th>loss</th>\n",
|
||||
" <th>accuracy</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>ft-seed0</td>\n",
|
||||
" <td>2</td>\n",
|
||||
" <td>test</td>\n",
|
||||
" <td>0.564516</td>\n",
|
||||
" <td>0.430445</td>\n",
|
||||
" <td>0.8920</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>120</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>ft-seed0</td>\n",
|
||||
" <td>2</td>\n",
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||||
" <td>valid</td>\n",
|
||||
" <td>0.416667</td>\n",
|
||||
" <td>0.422563</td>\n",
|
||||
" <td>0.8839</td>\n",
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||||
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|
||||
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||||
" <th>2</th>\n",
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||||
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||||
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|
||||
" <td>ft-seed0</td>\n",
|
||||
" <td>3</td>\n",
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||||
" <td>test</td>\n",
|
||||
" <td>0.592593</td>\n",
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||||
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|
||||
" <td>0.8900</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
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||||
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|
||||
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||||
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|
||||
" <td>ft-seed0</td>\n",
|
||||
" <td>3</td>\n",
|
||||
" <td>valid</td>\n",
|
||||
" <td>0.460674</td>\n",
|
||||
" <td>0.434416</td>\n",
|
||||
" <td>0.8863</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>12</td>\n",
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||||
" <td>0</td>\n",
|
||||
" <td>ft-seed0</td>\n",
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||||
" <td>4</td>\n",
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||||
" <td>test</td>\n",
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||||
" <td>0.606557</td>\n",
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||||
" <td>0.481438</td>\n",
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||||
" <td>0.9040</td>\n",
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||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" index LMseed ft clsseed dataset F1_score loss accuracy\n",
|
||||
"0 0 0 ft-seed0 2 test 0.564516 0.430445 0.8920\n",
|
||||
"1 120 0 ft-seed0 2 valid 0.416667 0.422563 0.8839\n",
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||||
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||||
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|
||||
"4 12 0 ft-seed0 4 test 0.606557 0.481438 0.9040"
|
||||
]
|
||||
},
|
||||
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|
||||
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|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = pd.read_csv(\"valvstest-fixed.tsv\", sep=\"\\t\")\n",
|
||||
"# fix issue in the data accuracy is actually F1_batch_avg\n",
|
||||
"#df = df.rename(columns={\"accuracy\": \"F1_batch_avg\"})\n",
|
||||
"df[\"dataset\"]=df[\"dataset\"].str.strip()\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 102,
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||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(count 240.000000\n",
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||||
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||||
" std 0.086787\n",
|
||||
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|
||||
" 25% 0.405409\n",
|
||||
" 50% 0.440903\n",
|
||||
" 75% 0.498287\n",
|
||||
" max 0.787635\n",
|
||||
" Name: loss, dtype: float64, count 240.000000\n",
|
||||
" mean 0.499472\n",
|
||||
" std 0.067876\n",
|
||||
" min 0.379310\n",
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||||
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|
||||
" 50% 0.483615\n",
|
||||
" 75% 0.565879\n",
|
||||
" max 0.636704\n",
|
||||
" Name: F1_score, dtype: float64)"
|
||||
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|
||||
},
|
||||
"execution_count": 102,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df[\"loss\"].describe(), df[\"F1_score\"].describe()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 103,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df['model_name'] = df[\"LMseed\"].map(str)+ \"-\"+df[\"ft\"]+\"-\"+df[\"clsseed\"].map(str)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 111,
|
||||
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|
||||
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|
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|
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|
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|
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|
||||
" <td>0.381395</td>\n",
|
||||
" <td>0.517359</td>\n",
|
||||
" <td>0.590351</td>\n",
|
||||
" <td>0.847</td>\n",
|
||||
" <td>0.8424</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1-noft-3</th>\n",
|
||||
" <td>0.564315</td>\n",
|
||||
" <td>0.386667</td>\n",
|
||||
" <td>0.480363</td>\n",
|
||||
" <td>0.479577</td>\n",
|
||||
" <td>0.895</td>\n",
|
||||
" <td>0.8910</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1-ft-seed1-21</th>\n",
|
||||
" <td>0.582781</td>\n",
|
||||
" <td>0.388626</td>\n",
|
||||
" <td>0.361739</td>\n",
|
||||
" <td>0.397997</td>\n",
|
||||
" <td>0.874</td>\n",
|
||||
" <td>0.8472</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1-noft-4</th>\n",
|
||||
" <td>0.582192</td>\n",
|
||||
" <td>0.393258</td>\n",
|
||||
" <td>0.398434</td>\n",
|
||||
" <td>0.410791</td>\n",
|
||||
" <td>0.878</td>\n",
|
||||
" <td>0.8720</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" F1_score loss accuracy \n",
|
||||
"dataset test valid test valid test valid\n",
|
||||
"model_name \n",
|
||||
"1-ft-seed1-4 0.583630 0.379310 0.381665 0.368908 0.883 0.8720\n",
|
||||
"0-noft-4 0.540541 0.381395 0.517359 0.590351 0.847 0.8424\n",
|
||||
"1-noft-3 0.564315 0.386667 0.480363 0.479577 0.895 0.8910\n",
|
||||
"1-ft-seed1-21 0.582781 0.388626 0.361739 0.397997 0.874 0.8472\n",
|
||||
"1-noft-4 0.582192 0.393258 0.398434 0.410791 0.878 0.8720"
|
||||
]
|
||||
},
|
||||
"execution_count": 111,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"dfp = df.pivot(index=\"model_name\", columns=\"dataset\", values=[\"F1_score\", \"loss\", \"accuracy\"]).sort_values((\"F1_score\",\"valid\"))\n",
|
||||
"dfp.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 105,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<matplotlib.axes._subplots.AxesSubplot at 0x7f10adc23550>"
|
||||
]
|
||||
},
|
||||
"execution_count": 105,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"dfp.plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 106,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<matplotlib.axes._subplots.AxesSubplot at 0x7f10adb80d30>"
|
||||
]
|
||||
},
|
||||
"execution_count": 106,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAELCAYAAADURYGZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsnXmcpFV577+n9q7q6r17Znr2fYbZYBgEZBFEBVxQFAG3CFfFJO5JuOLV657ERGMSE4Nb4q4sk4ggKHgVBQWBGZZh9n3p7ul9qX0/94+3zltv7dU91T09zPl+PnyY7n7rrVPV1ec5z+/ZhJQSjUaj0Zy92E73AjQajUZzetGGQKPRaM5ytCHQaDSasxxtCDQajeYsRxsCjUajOcvRhkCj0WjOcrQh0Gg0mrMcbQg0Go3mLEcbAo1GoznLcZzuBdRCR0eHXLJkyelehkaj0ZxRbN++fVhK2VntujPCECxZsoRt27ad7mVoNBrNGYUQ4lgt19VdGhJCXCOE2CeEOCiEuKPEzxcJIR4VQjwnhNghhHhtvdeg0Wg0mtqpqyEQQtiBrwPXAucAbxNCnFNw2aeAe6SU5wE3A/9RzzVoNBqNZnLU2yN4GXBQSnlYSpkA7gLeWHCNBJqy/24G+uq8Bo1Go9FMgnrHCOYDJyxf9wAXFlzzWeARIcSHAB/wqqk8UTKZpKenh1gsNpWHn1F4PB4WLFiA0+k83UvRaDQvQU5HsPhtwPeklP8khLgY+KEQYr2UMmO9SAhxG3AbwKJFi4pu0tPTg9/vZ8mSJQghZmLdpwUpJSMjI/T09LB06dLTvRyNRvMSpN7SUC+w0PL1guz3rLwHuAdASvkk4AE6Cm8kpfyWlHKLlHJLZ2dx9lMsFqO9vf0lbQQAhBC0t7efFZ6PRqM5PdTbEDwDrBRCLBVCuDCCwfcXXHMcuApACLEWwxAMTeXJXupGQHG2vE6NRnN6qKshkFKmgA8CDwN7MLKDdgkhPi+EuC572V8D7xNCvAD8FLhF6nmZGo1GUxEpJY/uGySdqf92Wfc6AinlQ1LKVVLK5VLKv81+79NSyvuz/94tpbxESrlJSnmulPKReq+hFj772c/yla98pezP77vvPnbv3l3X5zx69Cg/+clP6npPjUZzdrB/IMSt332Gx/ZPSUCpiO41VAZtCDQazWxiIpoEIBBL1v3eZ5Uh+Nu//VtWrVrFpZdeyr59+wD49re/zQUXXMCmTZt4y1veQiQS4YknnuD+++/n9ttv59xzz+XQoUMlrwO49957Wb9+PZs2beLyyy8HIJ1Oc/vtt3PBBRewceNGvvnNbwJwxx138Pjjj3Puuefyz//8z6fnTdBoNGcksWQagHgyU+XKKSClnPX/nX/++bKQ3bt3F32vEtu2bZPr16+X4XBYTkxMyOXLl8svf/nLcnh42Lzmk5/8pPza174mpZTy3e9+t7z33nvNn5W7bv369bKnp0dKKeXY2JiUUspvfvOb8gtf+IKUUspYLCbPP/98efjwYfnoo4/K173udZNat2Kyr1ej0by0eHjnSbn447+Q33/iSM2PAbbJGvbYM6LpXD14/PHHuf766/F6vQBcd50Ru965cyef+tSnGB8fJxQKcfXVV5d8fLnrLrnkEm655RZuvPFG3vzmNwPwyCOPsGPHDrZu3QrAxMQEBw4cwOVyTffL1Gg0L1FiKcMTmA6P4KwxBOW45ZZbuO+++9i0aRPf+973+N3vfjep677xjW/w1FNP8eCDD3L++eezfft2pJT827/9W5FRKXdvjUajqYaShtT/68lZEyO4/PLLue+++4hGowSDQR544AEAgsEg8+bNI5lM8uMf/9i83u/3EwwGza/LXXfo0CEuvPBCPv/5z9PZ2cmJEye4+uqrufPOO0kmjaDO/v37CYfDRffUaDSaWjENQar+huCs8Qg2b97MTTfdxKZNm+jq6uKCCy4A4Atf+AIXXnghnZ2dXHjhheZGffPNN/O+972Pr33ta2zdurXsdbfffjsHDhxASslVV13Fpk2b2LhxI0ePHmXz5s1IKens7OS+++5j48aN2O12Nm3axC233MLHPvax0/Z+aDSaM4ucR1B/aUjIM6CWa8uWLbJwMM2ePXtYu3btaVrRzHO2vV7NmcX2Y6NsXtSqq+Cnka/95gBf/fV+3nnRIr74pg01PUYIsV1KuaXadWeNNKTRaKaHnb0TvOXOJ3n6yOjpXspLmun0CLQh0Gg0p8R4RBU6pU7zSl7aKAOgg8UajWbWoTamRGoaCp00JipIrD0CjUYz61AbVCJd/5OqJodZWTwNWUPaEGg0mlMimjA2pmRq9ieenMlMZ4sJbQg0Gs0pYVa8prU0NJ2YMQLtEcwexsfH+Y//+I8pPfZf/uVfzKZ1Gs2ZTlzHCGYE7RHMQrQh0GgM1AaV1B7BtKIri2chd9xxB4cOHeLcc8/l1a9+NV1dXdxzzz3E43Guv/56Pve5zxEOh7nxxhvp6ekhnU7zf//v/2VgYIC+vj6uvPJKOjo6ePTRR0/3S9FoTgklWWiPYHqZzvTRl4Qh+NwDu9jdF6jrPc/pbuIzb1hX9udf+tKX2LlzJ88//zyPPPIIW7du5emnn0ZKyXXXXcdjjz3G0NAQ3d3dPPjgg4DRhbS5uZmvfvWrPProo3R0dNR1zRrN6UCnj84MOn10lvPII4/wyCOPcN5557F582b27t3LgQMH2LBhA7/+9a/5+Mc/zuOPP05zc/PpXqpGU3fUBqWloekllphc+uiJ0drl55eER1Dp5D4TSCn5xCc+wfvf//6inz377LM89NBDfOpTn+Kqq67i05/+9GlYoUYzfUQT2awh7RFMKyo7K5bMIKWs2tfpdV97vOZ7a49gilhbSl999dX813/9F6FQCIDe3l4GBwfp6+vD6/Xyzne+k9tvv51nn3226LEazZlOrqBMG4LpxBobqGZ00xlJMF57y4+XhEdwOmhvb+eSSy5h/fr1XHvttbz97W/n4osvBqCxsZEf/ehHHDx4kNtvvx2bzYbT6eTOO+8E4LbbbuOaa66hu7tbB4s1ZzwqfTSpPYJpQ0pJLJnG67ITSaSJJzN4nPay1wdjSSbTWFobglPgJz/5Sd7XH/nIR/K+Xr58ecnRlx/60If40Ic+NK1r02hmCjNrSHsE00YyLclIaGlwGoYglQacZa9XjQBrRUtDGo3mlNBZQ9OPkt+avcbc82qZQxNRbQg0Gs0MorOGph9lbFsaDC+gWlHZuDYEGo1mJlGnU501NH3EsplZLd6sIahSVDYeSUzq/toQaDSaU0JLQ9OPKQ1lPYJqRjegPQKNRjOT6F5D0496j5tr9gi0IdBoNDOIzhqaftR73NJQW7B4PJrE5yqfXlpI3Q2BEOIaIcQ+IcRBIcQdJX7+z0KI57P/7RdCjNd7DbORxsZGAPr6+rjhhhtKXnPFFVewbdu2mVyWRnPKaGlo+jGDxV4lDVX2CCaiSVNGqoW61hEIIezA14FXAz3AM0KI+6WUu9U1UsqPWa7/EHBePdcw2+nu7mbr1q2nexkaTV1IpTOkMkblUjKtJ5RNF0VZQ9U8gkjSTDWthXp7BC8DDkopD0spE8BdwBsrXP824Kd1XsOMcMcdd/D1r3/d/Pqzn/0sX/ziF7nqqqvYvHkzGzZs4Oc//3nR444ePcr69esBiEaj3Hzzzaxdu5brr7+eaDQ6Y+vXaOpBzOIFaI9g+lDvc60xgolowjQatVDvyuL5wAnL1z3AhaUuFEIsBpYCvz3lZ/3lHdD/4infJo+5G+DaL5X98U033cRHP/pRPvCBDwBwzz338PDDD/PhD3+YpqYmhoeHueiii7juuuvKNoe688478Xq97Nmzhx07drB58+b6vgaNZpqZTP8bzdRRnUdzMYLqweIVXY013/90tpi4GdgqpSz5ioQQtwG3ASxatGgm11UT5513ntlYbmhoiNbWVubOncvHPvYxHnvsMWw2G729vQwMDDB37tyS93jsscf48Ic/DMDGjRvZuHHjTL4EjeaUURuS12XXWUPTiEofzcUIqlcW3zjxX3yjxvvX2xD0AgstXy/Ifq8UNwMfKHcjKeW3gG8BbNmypbL4WOHkPp289a1vZevWrfT393PTTTfx4x//mKGhIbZv347T6WTJkiXEYrHTsjaNZiZQWnWTxznptgaa2lEG1+9xIESu0V8ppJSkohO8IvmTstcUUu8YwTPASiHEUiGEC2Ozv7/wIiHEGqAVeLLOzz+j3HTTTdx1111s3bqVt771rUxMTNDV1YXT6eTRRx/l2LFjFR9/+eWXm43rdu7cyY4dO2Zi2RpN3bBuUDp9dPpQBtfjtON22PJiM6WuPS+zCxu1/z7q6hFIKVNCiA8CDwN24L+klLuEEJ8HtkkplVG4GbhLysk0Sp19rFu3jmAwyPz585k3bx7veMc7eMMb3sCGDRvYsmULa9asqfj4v/iLv+DWW29l7dq1rF27lvPPP3+GVq7R1AdlCJoanKQzknRGYrdVHpiimTyxZBq7TeC02/A47RVjBBPRJBfbdpOyuWu+f91jBFLKh4CHCr736YKvP1vv5z1dvPhiLkjd0dHBk0+WdnLU0JolS5awc+dOABoaGrjrrrumf5EazTShTqoqZz2ZzmC31V7IpKmNWDJDQ3b+gMdhJ14hfXQ8muDltt2Mt58HPFLT/XVlsUajmTJWaQh05tB0EU2m8TiN7drttFXsPhoaHeAc2zHC3S+v+f7aEGg0mimjNqQmT84j0NSfeDKN25HzCCpJQ44TfwQgtfiymu9/RhuCMzzEUDNny+vUnHmYWUMNhkegi8qmh1gq5xF4nLaKlcWNfU8Skh7ci2qPOZ6xhsDj8TAyMvKS3ySllIyMjODxeE73UjSaInLSkOERaEMwPcQsM4rdTnvFXkPtQ3/imcxqWvy+mu9/xs4sXrBgAT09PQwNDZ3upUw7Ho+HBQsLine truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"dfp.sort_values((\"loss\",\"valid\"))[\"loss\"].plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### There is corellation Loss on valid & test"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 107,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"xy=dfp.sort_values((\"loss\",\"valid\"))\n",
|
||||
"ax = plt.plot(xy[\"loss\"]['valid'], xy[\"loss\"]['test'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 108,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"xy=dfp.sort_values((\"loss\",\"valid\"))\n",
|
||||
"ax = plt.plot(xy[\"loss\"]['valid'], xy[\"F1_score\"]['valid'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 112,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"xy=dfp.sort_values((\"accuracy\",\"valid\"))\n",
|
||||
"ax = plt.plot(xy[\"accuracy\"]['valid'], xy[\"accuracy\"]['test'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 113,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"xy=dfp.sort_values((\"accuracy\",\"valid\"))\n",
|
||||
"ax = plt.plot(xy[\"accuracy\"]['valid'], xy[\"F1_score\"]['test'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 114,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"xy=dfp.sort_values((\"accuracy\",\"valid\"))\n",
|
||||
"ax = plt.plot(xy[\"accuracy\"]['valid'], xy[\"F1_score\"]['valid'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 115,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"xy=dfp.sort_values((\"loss\",\"valid\"))\n",
|
||||
"ax = plt.plot(xy[\"loss\"]['valid'], xy[\"F1_score\"]['test'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 116,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"xy=dfp.sort_values((\"F1_score\",\"valid\"))\n",
|
||||
"ax = plt.plot(xy[\"F1_score\"]['valid'], xy[\"F1_score\"]['test'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 117,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
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|
||||
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|
||||
" }\n",
|
||||
"\n",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr>\n",
|
||||
" <th></th>\n",
|
||||
" <th colspan=\"2\" halign=\"left\">F1_score</th>\n",
|
||||
" <th colspan=\"2\" halign=\"left\">loss</th>\n",
|
||||
" <th colspan=\"2\" halign=\"left\">accuracy</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>dataset</th>\n",
|
||||
" <th>test</th>\n",
|
||||
" <th>valid</th>\n",
|
||||
" <th>test</th>\n",
|
||||
" <th>valid</th>\n",
|
||||
" <th>test</th>\n",
|
||||
" <th>valid</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>model_name</th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0-noft-20</th>\n",
|
||||
" <td>0.595156</td>\n",
|
||||
" <td>0.482759</td>\n",
|
||||
" <td>0.415361</td>\n",
|
||||
" <td>0.392013</td>\n",
|
||||
" <td>0.883</td>\n",
|
||||
" <td>0.8934</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1-ft-seed1-17</th>\n",
|
||||
" <td>0.568093</td>\n",
|
||||
" <td>0.484472</td>\n",
|
||||
" <td>0.561745</td>\n",
|
||||
" <td>0.414461</td>\n",
|
||||
" <td>0.889</td>\n",
|
||||
" <td>0.9017</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>0-ft-seed1-6</th>\n",
|
||||
" <td>0.500000</td>\n",
|
||||
" <td>0.496454</td>\n",
|
||||
" <td>0.606656</td>\n",
|
||||
" <td>0.451102</td>\n",
|
||||
" <td>0.884</td>\n",
|
||||
" <td>0.9159</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>0-ft-seed0-19</th>\n",
|
||||
" <td>0.513043</td>\n",
|
||||
" <td>0.496732</td>\n",
|
||||
" <td>0.548660</td>\n",
|
||||
" <td>0.432850</td>\n",
|
||||
" <td>0.888</td>\n",
|
||||
" <td>0.9088</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1-ft-seed1-8</th>\n",
|
||||
" <td>0.636704</td>\n",
|
||||
" <td>0.517647</td>\n",
|
||||
" <td>0.297993</td>\n",
|
||||
" <td>0.286143</td>\n",
|
||||
" <td>0.903</td>\n",
|
||||
" <td>0.9028</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" F1_score loss accuracy \n",
|
||||
"dataset test valid test valid test valid\n",
|
||||
"model_name \n",
|
||||
"0-noft-20 0.595156 0.482759 0.415361 0.392013 0.883 0.8934\n",
|
||||
"1-ft-seed1-17 0.568093 0.484472 0.561745 0.414461 0.889 0.9017\n",
|
||||
"0-ft-seed1-6 0.500000 0.496454 0.606656 0.451102 0.884 0.9159\n",
|
||||
"0-ft-seed0-19 0.513043 0.496732 0.548660 0.432850 0.888 0.9088\n",
|
||||
"1-ft-seed1-8 0.636704 0.517647 0.297993 0.286143 0.903 0.9028"
|
||||
]
|
||||
},
|
||||
"execution_count": 117,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"dfp.sort_values((\"F1_score\",\"valid\")).tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python [conda env:fastaiv1]",
|
||||
"language": "python",
|
||||
"name": "conda-env-fastaiv1-py"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
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||||
"version": 3
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||||
},
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||||
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||||
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||||
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||||
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|
||||
}
|
||||
@@ -0,0 +1,39 @@
|
||||
,model_dir_parent,model_name,test Accuracy,test F1 score bin,test Loss,test Precision,test Recall,valid Accuracy,valid F1 score bin,valid Kappa,valid Kappa Linear,valid Kappa quadratic,valid Loss,valid Matthews Correff,valid Precision,valid Recall
|
||||
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|
||||
298,data/hate/pl-10-wiki/models/sp25k,lstm_nowce_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-4-clstrainseed-0.m,0.8859999775886536,0.5043478012084961,0.44192057847976685,0.34728953,0.4497714638710022,0.6041666865348816,0.43283581733703613,0.9194029569625854,0.5714285969734192,0.5274431705474854,0.23408513,0.530713677406311,0.5192307829856873,0.6352941393852234
|
||||
299,data/hate/pl-10-wiki/models/sp25k,lstm_nowce_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-5-clstrainseed-0.m,0.8809999823570251,0.47111111879348755,0.40681517124176025,0.43768385,0.4164952337741852,0.5824176073074341,0.3955223858356476,0.9154228568077087,0.5251396894454956,0.4788457155227661,0.2790328,0.47957643866539,0.5,0.5529412031173706
|
||||
300,data/hate/pl-10-wiki/models/sp25k,lstm_nowce_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-6-clstrainseed-0.m,0.8980000019073486,0.5,0.44936299324035645,0.5335809,0.4788508117198944,0.7285714149475098,0.3805970251560211,0.9263681769371033,0.5432099103927612,0.5032727718353271,0.32624587,0.5040000081062317,0.5714285969734192,0.5176470875740051
|
||||
301,data/hate/pl-10-wiki/models/sp25k,lstm_nowce_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-7-clstrainseed-0.m,0.8880000114440918,0.4343434274196625,0.3806954026222229,0.5312314,0.4128783047199249,0.671875,0.3208955228328705,0.9333333373069763,0.556291401386261,0.5208666324615479,0.2942294,0.5257423520088196,0.6363636255264282,0.4941176474094391
|
||||
302,data/hate/pl-10-wiki/models/sp25k,lstm_nowce_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-8-clstrainseed-0.m,0.8830000162124634,0.43478259444236755,0.3757869601249695,0.4787235,0.3974221348762512,0.6164383292198181,0.33582088351249695,0.9213930368423462,0.5093168020248413,0.4667360186576843,0.29617715,0.4675997495651245,0.5394737124443054,0.48235294222831726
|
||||
303,data/hate/pl-10-wiki/models/sp25k,lstm_nowce_ft6_cl8_lmseed-1-ftseed-0-clsweightseed-9-clstrainseed-0.m,0.8840000033378601,0.46296295523643494,0.40213584899902344,0.4601608,0.4174059331417084,0.6097561120986938,0.3731343150138855,0.9194029569625854,0.5317918658256531,0.4877128005027771,0.27519348,0.48780059814453125,0.5227272510528564,0.5411764979362488
|
||||
304,data/hate/pl-10-wiki/models/sp25k,lstm_small_ft6_el20_lmseed-0-ftseed-0-clsweightseed-0-clstrainseed-0.m,0.8830000162124634,0.4507042169570923,,0.5523807,,0.607594907283783,0.35820895433425903,0.9263681769371033,0.593406617641449,0.5531185865402222,0.3514958,0.5545766949653625,0.5567010045051575,0.6352941393852234
|
||||
305,data/hate/pl-10-wiki/models/sp25k,lstm_small_ft6_el20_lmseed-0-ftseed-0-clsweightseed-1-clstrainseed-0.m,0.8820000290870667,0.4913793206214905,,0.51190376,,0.581632673740387,0.4253731369972229,0.9154228568077087,0.5454545617103577,0.4992526173591614,0.3744005,0.501775324344635,0.5,0.6000000238418579
|
||||
306,data/hate/pl-10-wiki/models/sp25k,lstm_small_ft6_el20_lmseed-0-ftseed-0-clsweightseed-2-clstrainseed-0.m,0.8809999823570251,0.4757709205150604,,0.5393214,,0.5806451439857483,0.4029850661754608,0.9174129366874695,0.5561497807502747,0.5110348463058472,0.37151873,0.5136171579360962,0.5098039507865906,0.6117647290229797
|
||||
307,data/hate/pl-10-wiki/models/sp25k,lstm_small_ft6_el20_lmseed-0-ftseed-0-clsweightseed-3-clstrainseed-0.m,0.8740000128746033,0.39423078298568726,,0.58161116,,0.5540540814399719,0.305970162153244,0.9243780970573425,0.5632184147834778,0.5218479633331299,0.3464281,0.5220133662223816,0.550561785697937,0.5764706134796143
|
||||
308,data/hate/pl-10-wiki/models/sp25k,lstm_small_ft6_el20_lmseed-0-ftseed-0-clsweightseed-4-clstrainseed-0.m,0.8799999952316284,0.5238094925880432,,0.4988901,,0.5593220591545105,0.49253731966018677,0.9164178967475891,0.5670102834701538,0.5215370655059814,0.3931842,0.5264796614646912,0.5045871734619141,0.6470588445663452
|
||||
309,data/hate/pl-10-wiki/models/sp25k,lstm_small_ft6_el20_lmseed-0-ftseed-0-clsweightseed-5-clstrainseed-0.m,0.8809999823570251,0.44131454825401306,,0.5540554,,0.594936728477478,0.3507462739944458,0.9223880767822266,0.5568182468414307,0.5143423676490784,0.36945158,0.5147016644477844,0.5384615659713745,0.5764706134796143
|
||||
310,data/hate/pl-10-wiki/models/sp25k,lstm_small_ft6_el20_lmseed-0-ftseed-0-clsweightseed-6-clstrainseed-0.m,0.878000020980835,0.48739495873451233,,0.5654785,,0.557692289352417,0.43283581733703613,0.9213930368423462,0.5729729533195496,0.5299985408782959,0.38207418,0.5321216583251953,0.5299999713897705,0.6235294342041016
|
||||
311,data/hate/pl-10-wiki/models/sp25k,lstm_small_ft6_el20_lmseed-0-ftseed-0-clsweightseed-7-clstrainseed-0.m,0.8809999823570251,0.4803493320941925,,0.5358918,,0.5789473652839661,0.41044774651527405,0.9174129366874695,0.5414364337921143,0.49624061584472656,0.38049248,0.49735027551651,0.5104166865348816,0.5764706134796143
|
||||
312,data/hate/pl-10-wiki/models/sp25k,lstm_small_ft6_el20_lmseed-0-ftseed-0-clsweightseed-8-clstrainseed-0.m,0.8939999938011169,0.5137615203857422,,0.56349623,,0.6666666865348816,0.41791045665740967,0.9243780970573425,0.5632184147834778,0.5218479633331299,0.34343374,0.5220133662223816,0.550561785697937,0.5764706134796143
|
||||
313,data/hate/pl-10-wiki/models/sp25k,lstm_small_ft6_el20_lmseed-0-ftseed-0-clsweightseed-9-clstrainseed-0.m,0.8830000162124634,0.440191388130188,,0.54497576,,0.6133333444595337,0.34328359365463257,0.9253731369972229,0.5562130212783813,0.515475869178772,0.37040198,0.5154865980148315,0.5595238208770752,0.5529412031173706
|
||||
314,data/hate/pl-10-wiki/models/sp25k,lstm_small_ft6_el20_lmseed-1-ftseed-0-clsweightseed-0-clstrainseed-0.m,0.8939999938011169,0.526785671710968,,0.5066931,,0.6555555462837219,0.44029849767684937,0.9243780970573425,0.5681818723678589,0.5267951488494873,0.3419854,0.5271630883216858,0.5494505763053894,0.5882353186607361
|
||||
315,data/hate/pl-10-wiki/models/sp25k,lstm_small_ft6_el20_lmseed-1-ftseed-0-clsweightseed-1-clstrainseed-0.m,0.8849999904632568,0.5064377784729004,,0.48513892,,0.5959596037864685,0.44029849767684937,0.9203979969024658,0.550561785697937,0.5069904327392578,0.39884716,0.5076076984405518,0.5268816947937012,0.5764706134796143
|
||||
316,data/hate/pl-10-wiki/models/sp25k,lstm_small_ft6_el20_lmseed-1-ftseed-0-clsweightseed-2-clstrainseed-0.m,0.8730000257492065,0.5605536103248596,,0.43568474,,0.522580623626709,0.60447758436203,0.9074627161026001,0.5714285969734192,0.5222724676132202,0.3917941,0.5381934642791748,0.469696968793869,0.729411780834198
|
||||
317,data/hate/pl-10-wiki/models/sp25k,lstm_small_ft6_el20_lmseed-1-ftseed-0-clsweightseed-3-clstrainseed-0.m,0.8769999742507935,0.5323194265365601,,0.5091572,,0.5426356792449951,0.5223880410194397,0.9134328365325928,0.5445026159286499,0.4973122477531433,0.40174288,0.5010141134262085,0.49056604504585266,0.6117647290229797
|
||||
318,data/hate/pl-10-wiki/models/sp25k,lstm_small_ft6_el20_lmseed-1-ftseed-0-clsweightseed-4-clstrainseed-0.m,0.8799999952316284,0.4957982897758484,,0.47033572,,0.567307710647583,0.44029849767684937,0.9144278764724731,0.5520833730697632,0.5054643154144287,0.36492744,0.5095584392547607,0.4953271150588989,0.6235294342041016
|
||||
319,data/hate/pl-10-wiki/models/sp25k,lstm_small_ft6_el20_lmseed-1-ftseed-0-clsweightseed-5-clstrainseed-0.m,0.8849999904632568,0.5267489552497864,,0.46872956,,0.5871559381484985,0.4776119291782379,0.9094527363777161,0.5380710959434509,0.48892122507095337,0.38632104,0.4946412444114685,0.4732142984867096,0.6235294342041016
|
||||
320,data/hate/pl-10-wiki/models/sp25k,lstm_small_ft6_el20_lmseed-1-ftseed-0-clsweightseed-6-clstrainseed-0.m,0.8859999775886536,0.4999999403953552,,0.49172023,,0.6063829660415649,0.4253731369972229,0.9194029569625854,0.5423728227615356,0.4982587695121765,0.36051777,0.49872785806655884,0.52173912525177,0.5647059082984924
|
||||
321,data/hate/pl-10-wiki/models/sp25k,lstm_small_ft6_el20_lmseed-1-ftseed-0-clsweightseed-7-clstrainseed-0.m,0.878000020980835,0.5040649771690369,,0.49449727,,0.5535714030265808,0.46268656849861145,0.9134328365325928,0.5445026159286499,0.4973122477531433,0.41053435,0.5010141134262085,0.49056604504585266,0.6117647290229797
|
||||
322,data/hate/pl-10-wiki/models/sp25k,lstm_small_ft6_el20_lmseed-1-ftseed-0-clsweightseed-8-clstrainseed-0.m,0.8859999775886536,0.5648854970932007,,0.41943383,,0.578125,0.5522388219833374,0.9074627161026001,0.5230768918991089,0.47276830673217773,0.34020123,0.47758930921554565,0.4636363685131073,0.6000000238418579
|
||||
323,data/hate/pl-10-wiki/models/sp25k,lstm_small_ft6_el20_lmseed-1-ftseed-0-clsweightseed-9-clstrainseed-0.m,0.8899999856948853,0.5416666865348816,,0.481508,,0.6132075190544128,0.48507463932037354,0.9094527363777161,0.5081080794334412,0.45860588550567627,0.4094172,0.46044301986694336,0.4699999988079071,0.5529412031173706
|
||||
324,data/hate/pl-10-wiki/models/sp25k,lstm_tmp-100_lmseed-1-ftseed-0-clsweightseed-0-clstrainseed-0.m,0.5720000267028809,0.37243402004241943,,0.8859752,,0.23175182938575745,0.9477611780166626,0.5422885417938232,0.260450154542923,0.1339777708053589,0.98535573,0.25508448481559753,0.1508379876613617,0.9529411792755127
|
||||
|
@@ -0,0 +1,241 @@
|
||||
index LMseed ft clsseed dataset F1_score loss accuracy
|
||||
0 0 ft-seed0 2 test 0.5645161271095276 0.43044528365135193 0.8920
|
||||
120 0 ft-seed0 2 valid 0.4166666567325592 0.4225634038448334 0.8839
|
||||
6 0 ft-seed0 3 test 0.5925925970077515 0.4165325462818146 0.8900
|
||||
126 0 ft-seed0 3 valid 0.4606741666793823 0.43441566824913025 0.8863
|
||||
12 0 ft-seed0 4 test 0.6065574288368225 0.4814380705356598 0.9040
|
||||
132 0 ft-seed0 4 valid 0.4285714626312256 0.5097822546958923 0.8957
|
||||
18 0 ft-seed0 5 test 0.570397138595581 0.44903358817100525 0.8810
|
||||
138 0 ft-seed0 5 valid 0.4457142949104309 0.4238314628601074 0.8851
|
||||
24 0 ft-seed0 6 test 0.5592105388641357 0.4162355363368988 0.8660
|
||||
144 0 ft-seed0 6 valid 0.42105263471603394 0.4097800552845001 0.8697
|
||||
30 0 ft-seed0 7 test 0.5714285969734192 0.4321300685405731 0.9010
|
||||
150 0 ft-seed0 7 valid 0.44871795177459717 0.4115864932537079 0.8981
|
||||
36 0 ft-seed0 8 test 0.5772358179092407 0.4818994700908661 0.8960
|
||||
156 0 ft-seed0 8 valid 0.45945945382118225 0.447449266910553 0.9052
|
||||
42 0 ft-seed0 9 test 0.5 0.45570382475852966 0.8800
|
||||
162 0 ft-seed0 9 valid 0.4571428596973419 0.327668696641922 0.9100
|
||||
48 0 ft-seed0 10 test 0.5349794626235962 0.4865730404853821 0.8870
|
||||
168 0 ft-seed0 10 valid 0.4129032492637634 0.434408038854599 0.8922
|
||||
54 0 ft-seed0 11 test 0.5433071255683899 0.4363311529159546 0.8840
|
||||
174 0 ft-seed0 11 valid 0.4615384638309479 0.38903355598449707 0.8922
|
||||
60 0 ft-seed0 12 test 0.5724906921386719 0.4853847026824951 0.8850
|
||||
180 0 ft-seed0 12 valid 0.47191014885902405 0.5130544304847717 0.8886
|
||||
66 0 ft-seed0 13 test 0.5742574334144592 0.40475693345069885 0.8710
|
||||
186 0 ft-seed0 13 valid 0.45771148800849915 0.4509955048561096 0.8709
|
||||
72 0 ft-seed0 14 test 0.5230769515037537 0.44990867376327515 0.8760
|
||||
192 0 ft-seed0 14 valid 0.4642857313156128 0.4322844445705414 0.8934
|
||||
78 0 ft-seed0 15 test 0.5526315569877625 0.47065943479537964 0.8980
|
||||
198 0 ft-seed0 15 valid 0.4413793385028839 0.4142134487628937 0.9040
|
||||
84 0 ft-seed0 16 test 0.5087718963623047 0.5729814171791077 0.8880
|
||||
204 0 ft-seed0 16 valid 0.4383561909198761 0.4802896976470947 0.9028
|
||||
90 0 ft-seed0 17 test 0.560669481754303 0.47273412346839905 0.8950
|
||||
210 0 ft-seed0 17 valid 0.4055943787097931 0.4395078718662262 0.8993
|
||||
96 0 ft-seed0 18 test 0.5232067108154297 0.5001763701438904 0.8870
|
||||
216 0 ft-seed0 18 valid 0.45517241954803467 0.40583667159080505 0.9064
|
||||
102 0 ft-seed0 19 test 0.5130434632301331 0.548660397529602 0.8880
|
||||
222 0 ft-seed0 19 valid 0.49673202633857727 0.4328499138355255 0.9088
|
||||
108 0 ft-seed0 20 test 0.5132743716239929 0.5187202095985413 0.8900
|
||||
228 0 ft-seed0 20 valid 0.43609023094177246 0.407430499792099 0.9111
|
||||
114 0 ft-seed0 21 test 0.46153849363327026 0.4121854901313782 0.8810
|
||||
234 0 ft-seed0 21 valid 0.46268653869628906 0.2815968096256256 0.9147
|
||||
1 0 ft-seed1 2 test 0.4956521987915039 0.5943491458892822 0.8840
|
||||
121 0 ft-seed1 2 valid 0.4460431635379791 0.5221770405769348 0.9088
|
||||
7 0 ft-seed1 3 test 0.41624364256858826 0.7639743089675903 0.8850
|
||||
127 0 ft-seed1 3 valid 0.4137931168079376 0.4463430345058441 0.9194
|
||||
13 0 ft-seed1 4 test 0.5152838230133057 0.7346494197845459 0.8890
|
||||
133 0 ft-seed1 4 valid 0.4615384638309479 0.696619987487793 0.9088
|
||||
19 0 ft-seed1 5 test 0.580152690410614 0.44208887219429016 0.8900
|
||||
139 0 ft-seed1 5 valid 0.4588235318660736 0.5001108050346375 0.8910
|
||||
25 0 ft-seed1 6 test 0.5000000596046448 0.6066558957099915 0.8840
|
||||
145 0 ft-seed1 6 valid 0.4964538514614105 0.4511015713214874 0.9159
|
||||
31 0 ft-seed1 7 test 0.5522388219833374 0.6283032298088074 0.8800
|
||||
151 0 ft-seed1 7 valid 0.4528301954269409 0.6680119037628174 0.8969
|
||||
37 0 ft-seed1 8 test 0.4888888895511627 0.6613699793815613 0.8850
|
||||
157 0 ft-seed1 8 valid 0.43609023094177246 0.4850305914878845 0.9111
|
||||
43 0 ft-seed1 9 test 0.5316455960273743 0.5851835012435913 0.8890
|
||||
163 0 ft-seed1 9 valid 0.4217686951160431 0.5449877381324768 0.8993
|
||||
49 0 ft-seed1 10 test 0.4651162922382355 0.7876351475715637 0.8850
|
||||
169 0 ft-seed1 10 valid 0.40875911712646484 0.6061959862709045 0.9040
|
||||
55 0 ft-seed1 11 test 0.568965494632721 0.5870274901390076 0.9000
|
||||
175 0 ft-seed1 11 valid 0.46043166518211365 0.49774301052093506 0.9111
|
||||
61 0 ft-seed1 12 test 0.5461847186088562 0.5820209383964539 0.8870
|
||||
181 0 ft-seed1 12 valid 0.4458598792552948 0.5988472104072571 0.8969
|
||||
67 0 ft-seed1 13 test 0.44131454825401306 0.6617334485054016 0.8810
|
||||
187 0 ft-seed1 13 valid 0.46715328097343445 0.4917261302471161 0.9135
|
||||
73 0 ft-seed1 14 test 0.582456111907959 0.5261735916137695 0.8810
|
||||
193 0 ft-seed1 14 valid 0.4687500298023224 0.5817894339561462 0.8791
|
||||
79 0 ft-seed1 15 test 0.5431034564971924 0.5236736536026001 0.8940
|
||||
199 0 ft-seed1 15 valid 0.4615384638309479 0.4515714943408966 0.9088
|
||||
85 0 ft-seed1 16 test 0.6300365924835205 0.46109575033187866 0.8990
|
||||
205 0 ft-seed1 16 valid 0.43478259444236755 0.49476781487464905 0.8768
|
||||
91 0 ft-seed1 17 test 0.49122804403305054 0.5814564228057861 0.8840
|
||||
211 0 ft-seed1 17 valid 0.4142857491970062 0.4819534420967102 0.9028
|
||||
97 0 ft-seed1 18 test 0.48510637879371643 0.4999200999736786 0.8790
|
||||
217 0 ft-seed1 18 valid 0.4400000274181366 0.39687463641166687 0.9005
|
||||
103 0 ft-seed1 19 test 0.5736434459686279 0.5443088412284851 0.8900
|
||||
223 0 ft-seed1 19 valid 0.44171783328056335 0.5909873843193054 0.8922
|
||||
109 0 ft-seed1 20 test 0.4864864647388458 0.5205869674682617 0.8860
|
||||
229 0 ft-seed1 20 valid 0.4117646813392639 0.3999001383781433 0.9052
|
||||
115 0 ft-seed1 21 test 0.6058091521263123 0.4227277338504791 0.9050
|
||||
235 0 ft-seed1 21 valid 0.4675324559211731 0.3723865747451782 0.9028
|
||||
2 0 noft 2 test 0.5433962941169739 0.4767005145549774 0.8790
|
||||
122 0 noft 2 valid 0.4457831382751465 0.4171012043952942 0.8910
|
||||
8 0 noft 3 test 0.5438596606254578 0.37441006302833557 0.8440
|
||||
128 0 noft 3 valid 0.4421052634716034 0.36205780506134033 0.8744
|
||||
14 0 noft 4 test 0.5405405163764954 0.5173587799072266 0.8470
|
||||
134 0 noft 4 valid 0.3813953697681427 0.5903511047363281 0.8424
|
||||
20 0 noft 5 test 0.5266667008399963 0.4419504404067993 0.8580
|
||||
140 0 noft 5 valid 0.4293193817138672 0.4399767816066742 0.8709
|
||||
26 0 noft 6 test 0.5324674844741821 0.41715914011001587 0.8560
|
||||
146 0 noft 6 valid 0.44897958636283875 0.4160920977592468 0.8720
|
||||
32 0 noft 7 test 0.5679012537002563 0.4777500629425049 0.8950
|
||||
152 0 noft 7 valid 0.4142857491970062 0.42049285769462585 0.9028
|
||||
38 0 noft 8 test 0.5747800469398499 0.3598693311214447 0.8550
|
||||
158 0 noft 8 valid 0.44144144654273987 0.405771940946579 0.8531
|
||||
44 0 noft 9 test 0.5108224749565125 0.5093541145324707 0.8870
|
||||
164 0 noft 9 valid 0.4413793385028839 0.3927517235279083 0.9040
|
||||
50 0 noft 10 test 0.5108224749565125 0.5246335864067078 0.8870
|
||||
170 0 noft 10 valid 0.447761207818985 0.4109422266483307 0.9123
|
||||
56 0 noft 11 test 0.5603448152542114 0.5133260488510132 0.8980
|
||||
176 0 noft 11 valid 0.4661653935909271 0.38057011365890503 0.9159
|
||||
62 0 noft 12 test 0.5597269535064697 0.49656251072883606 0.8710
|
||||
182 0 noft 12 valid 0.44680851697921753 0.48734134435653687 0.8768
|
||||
68 0 noft 13 test 0.5232067108154297 0.5087217688560486 0.8870
|
||||
188 0 noft 13 valid 0.45255473256111145 0.3679719567298889 0.9111
|
||||
74 0 noft 14 test 0.536796510219574 0.454653263092041 0.8930
|
||||
194 0 noft 14 valid 0.4755244553089142 0.34815725684165955 0.9111
|
||||
80 0 noft 15 test 0.49339210987091064 0.4725494980812073 0.8850
|
||||
200 0 noft 15 valid 0.4571428596973419 0.35398390889167786 0.9100
|
||||
86 0 noft 16 test 0.5022830963134766 0.4819789230823517 0.8910
|
||||
206 0 noft 16 valid 0.3999999761581421 0.41223767399787903 0.9040
|
||||
92 0 noft 17 test 0.5546218752861023 0.48606786131858826 0.8940
|
||||
212 0 noft 17 valid 0.4313725531101227 0.4288560152053833 0.8969
|
||||
98 0 noft 18 test 0.565517246723175 0.4054659307003021 0.8740
|
||||
218 0 noft 18 valid 0.4421052634716034 0.4349640905857086 0.8744
|
||||
104 0 noft 19 test 0.493506520986557 0.5580055117607117 0.8830
|
||||
224 0 noft 19 valid 0.44285714626312256 0.44473594427108765 0.9076
|
||||
110 0 noft 20 test 0.5951557159423828 0.415360689163208 0.8830
|
||||
230 0 noft 20 valid 0.48275861144065857 0.39201340079307556 0.8934
|
||||
116 0 noft 21 test 0.5362318754196167 0.47137773036956787 0.8720
|
||||
236 0 noft 21 valid 0.4550897777080536 0.4185858368873596 0.8922
|
||||
3 1 ft-seed0 2 test 0.5490196347236633 0.5724288821220398 0.8620
|
||||
123 1 ft-seed0 2 valid 0.44117647409439087 0.6496979594230652 0.8649
|
||||
9 1 ft-seed0 3 test 0.5754386186599731 0.4020021855831146 0.8790
|
||||
129 1 ft-seed0 3 valid 0.4422110617160797 0.42443081736564636 0.8685
|
||||
15 1 ft-seed0 4 test 0.6111111044883728 0.4388059079647064 0.9020
|
||||
135 1 ft-seed0 4 valid 0.44871795177459717 0.3783545196056366 0.8981
|
||||
21 1 ft-seed0 5 test 0.5679012537002563 0.49716848134994507 0.8950
|
||||
141 1 ft-seed0 5 valid 0.46357616782188416 0.3597354590892792 0.9040
|
||||
27 1 ft-seed0 6 test 0.5486725568771362 0.43432965874671936 0.8980
|
||||
147 1 ft-seed0 6 valid 0.43939390778541565 0.3266245424747467 0.9123
|
||||
33 1 ft-seed0 7 test 0.6108786463737488 0.4717611074447632 0.9070
|
||||
153 1 ft-seed0 7 valid 0.4313725531101227 0.43972310423851013 0.8969
|
||||
39 1 ft-seed0 8 test 0.5909091234207153 0.29768291115760803 0.8920
|
||||
159 1 ft-seed0 8 valid 0.4550897777080536 0.3085547983646393 0.8922
|
||||
45 1 ft-seed0 9 test 0.560669481754303 0.4620136320590973 0.8950
|
||||
165 1 ft-seed0 9 valid 0.44897958636283875 0.37488117814064026 0.9040
|
||||
51 1 ft-seed0 10 test 0.6086956858634949 0.42837387323379517 0.9010
|
||||
171 1 ft-seed0 10 valid 0.43478259444236755 0.44988685846328735 0.8922
|
||||
57 1 ft-seed0 11 test 0.5660377740859985 0.49595555663108826 0.8850
|
||||
177 1 ft-seed0 11 valid 0.4177215099334717 0.41470322012901306 0.8910
|
||||
63 1 ft-seed0 12 test 0.5942028760910034 0.44078487157821655 0.8880
|
||||
183 1 ft-seed0 12 valid 0.44692739844322205 0.43711891770362854 0.8827
|
||||
69 1 ft-seed0 13 test 0.5753424763679504 0.5054553747177124 0.8760
|
||||
189 1 ft-seed0 13 valid 0.4333333671092987 0.42465242743492126 0.8791
|
||||
75 1 ft-seed0 14 test 0.607692301273346 0.36369848251342773 0.8980
|
||||
195 1 ft-seed0 14 valid 0.4585987329483032 0.32929185032844543 0.8993
|
||||
81 1 ft-seed0 15 test 0.5620437860488892 0.3254164457321167 0.8800
|
||||
201 1 ft-seed0 15 valid 0.4497041702270508 0.2714768946170807 0.8898
|
||||
87 1 ft-seed0 16 test 0.6101694703102112 0.3997952938079834 0.8850
|
||||
207 1 ft-seed0 16 valid 0.4419889450073242 0.36827152967453003 0.8803
|
||||
93 1 ft-seed0 17 test 0.5723905563354492 0.4410214424133301 0.8730
|
||||
213 1 ft-seed0 17 valid 0.4365482032299042 0.4674760699272156 0.8685
|
||||
99 1 ft-seed0 18 test 0.5785713791847229 0.346145898103714 0.8820
|
||||
219 1 ft-seed0 18 valid 0.4134078323841095 0.33745241165161133 0.8756
|
||||
105 1 ft-seed0 19 test 0.5414847135543823 0.505227267742157 0.8950
|
||||
225 1 ft-seed0 19 valid 0.4217686951160431 0.427654892206192 0.8993
|
||||
111 1 ft-seed0 20 test 0.5779467225074768 0.5614640712738037 0.8890
|
||||
231 1 ft-seed0 20 valid 0.45000001788139343 0.45731329917907715 0.8957
|
||||
117 1 ft-seed0 21 test 0.5573770403862 0.516575813293457 0.8920
|
||||
237 1 ft-seed0 21 valid 0.4026845395565033 0.4152399003505707 0.8945
|
||||
4 1 ft-seed1 2 test 0.5914397239685059 0.5607386231422424 0.8950
|
||||
124 1 ft-seed1 2 valid 0.43589746952056885 0.4342404901981354 0.8957
|
||||
10 1 ft-seed1 3 test 0.5737704634666443 0.518389880657196 0.8960
|
||||
130 1 ft-seed1 3 valid 0.42953020334243774 0.3975204527378082 0.8993
|
||||
16 1 ft-seed1 4 test 0.5836299061775208 0.3816649615764618 0.8830
|
||||
136 1 ft-seed1 4 valid 0.37931036949157715 0.36890846490859985 0.8720
|
||||
22 1 ft-seed1 5 test 0.6038960814476013 0.4200931787490845 0.8780
|
||||
142 1 ft-seed1 5 valid 0.45000001788139343 0.39361807703971863 0.8697
|
||||
28 1 ft-seed1 6 test 0.5573770999908447 0.4661884605884552 0.8650
|
||||
148 1 ft-seed1 6 valid 0.4565217196941376 0.38068053126335144 0.8815
|
||||
34 1 ft-seed1 7 test 0.5991902351379395 0.5403699278831482 0.9010
|
||||
154 1 ft-seed1 7 valid 0.4556961953639984 0.4898127019405365 0.8981
|
||||
40 1 ft-seed1 8 test 0.63670414686203 0.29799285531044006 0.9030
|
||||
160 1 ft-seed1 8 valid 0.5176470279693604 0.2861432135105133 0.9028
|
||||
46 1 ft-seed1 9 test 0.5886792540550232 0.392250120639801 0.8910
|
||||
166 1 ft-seed1 9 valid 0.44720497727394104 0.34671708941459656 0.8945
|
||||
52 1 ft-seed1 10 test 0.5737704634666443 0.5335988402366638 0.8960
|
||||
172 1 ft-seed1 10 valid 0.4109589159488678 0.43126046657562256 0.8981
|
||||
58 1 ft-seed1 11 test 0.5517241954803467 0.40790465474128723 0.8830
|
||||
178 1 ft-seed1 11 valid 0.43113774061203003 0.3307510316371918 0.8874
|
||||
64 1 ft-seed1 12 test 0.5423728823661804 0.5214505195617676 0.8920
|
||||
184 1 ft-seed1 12 valid 0.46979862451553345 0.40523862838745117 0.9064
|
||||
70 1 ft-seed1 13 test 0.5725191235542297 0.42663702368736267 0.8880
|
||||
190 1 ft-seed1 13 valid 0.44692739844322205 0.4057123363018036 0.8827
|
||||
76 1 ft-seed1 14 test 0.6165413856506348 0.4142192006111145 0.8980
|
||||
196 1 ft-seed1 14 valid 0.4431818127632141 0.35879600048065186 0.8839
|
||||
82 1 ft-seed1 15 test 0.5925925970077515 0.31346964836120605 0.8900
|
||||
202 1 ft-seed1 15 valid 0.44171783328056335 0.27289724349975586 0.8922
|
||||
88 1 ft-seed1 16 test 0.599303126335144 0.4012908637523651 0.8850
|
||||
208 1 ft-seed1 16 valid 0.4093567132949829 0.3552548289299011 0.8803
|
||||
94 1 ft-seed1 17 test 0.5680934190750122 0.5617448687553406 0.8890
|
||||
214 1 ft-seed1 17 valid 0.48447200655937195 0.4144614338874817 0.9017
|
||||
100 1 ft-seed1 18 test 0.5746268630027771 0.5116666555404663 0.8860
|
||||
220 1 ft-seed1 18 valid 0.43023255467414856 0.4778071343898773 0.8839
|
||||
106 1 ft-seed1 19 test 0.5517240762710571 0.6141948103904724 0.8440
|
||||
226 1 ft-seed1 19 valid 0.42396309971809387 0.6560723185539246 0.8519
|
||||
112 1 ft-seed1 20 test 0.5658263564109802 0.4831385910511017 0.8450
|
||||
232 1 ft-seed1 20 valid 0.43103447556495667 0.5641922950744629 0.8436
|
||||
118 1 ft-seed1 21 test 0.5827814340591431 0.3617390990257263 0.8740
|
||||
238 1 ft-seed1 21 valid 0.3886255919933319 0.39799734950065613 0.8472
|
||||
5 1 noft 2 test 0.5281385183334351 0.4756642282009125 0.8910
|
||||
125 1 noft 2 valid 0.40287768840789795 0.3857330083847046 0.9017
|
||||
11 1 noft 3 test 0.5643153190612793 0.4803626239299774 0.8950
|
||||
131 1 noft 3 valid 0.3866666555404663 0.47957706451416016 0.8910
|
||||
17 1 noft 4 test 0.5821918249130249 0.3984341025352478 0.8780
|
||||
137 1 noft 4 valid 0.3932584226131439 0.41079074144363403 0.8720
|
||||
23 1 noft 5 test 0.5921052694320679 0.4569582939147949 0.8760
|
||||
143 1 noft 5 valid 0.46632125973701477 0.41115298867225647 0.8780
|
||||
29 1 noft 6 test 0.5925925970077515 0.411027729511261 0.9010
|
||||
149 1 noft 6 valid 0.4296296536922455 0.3237917125225067 0.9088
|
||||
35 1 noft 7 test 0.5934960246086121 0.47999921441078186 0.9000
|
||||
155 1 noft 7 valid 0.45945945382118225 0.34085509181022644 0.9052
|
||||
41 1 noft 8 test 0.5552050471305847 0.4669162631034851 0.8590
|
||||
161 1 noft 8 valid 0.44897958636283875 0.4272935688495636 0.8720
|
||||
47 1 noft 9 test 0.5791505575180054 0.45815664529800415 0.8910
|
||||
167 1 noft 9 valid 0.4125000238418579 0.39577150344848633 0.8886
|
||||
53 1 noft 10 test 0.5773195624351501 0.44457459449768066 0.8770
|
||||
173 1 noft 10 valid 0.41111111640930176 0.4423538148403168 0.8744
|
||||
59 1 noft 11 test 0.563265323638916 0.534534752368927 0.8930
|
||||
179 1 noft 11 valid 0.41134753823280334 0.43552181124687195 0.9017
|
||||
65 1 noft 12 test 0.5795053243637085 0.5075364708900452 0.8810
|
||||
185 1 noft 12 valid 0.45901644229888916 0.4783734381198883 0.8827
|
||||
71 1 noft 13 test 0.5666667222976685 0.48282700777053833 0.8960
|
||||
191 1 noft 13 valid 0.4647887349128723 0.3755517303943634 0.9100
|
||||
77 1 noft 14 test 0.579365074634552 0.4776264429092407 0.8940
|
||||
197 1 noft 14 valid 0.40522876381874084 0.4339909851551056 0.8922
|
||||
83 1 noft 15 test 0.6067415475845337 0.28845077753067017 0.8950
|
||||
203 1 noft 15 valid 0.43589746952056885 0.28395193815231323 0.8957
|
||||
89 1 noft 16 test 0.607692301273346 0.3595740497112274 0.8980
|
||||
209 1 noft 16 valid 0.4268292486667633 0.35805508494377136 0.8886
|
||||
95 1 noft 17 test 0.6209386587142944 0.5146023035049438 0.8950
|
||||
215 1 noft 17 valid 0.43023255467414856 0.495402067899704 0.8839
|
||||
101 1 noft 18 test 0.5602836608886719 0.46523019671440125 0.8760
|
||||
221 1 noft 18 valid 0.4482758641242981 0.43405812978744507 0.8863
|
||||
107 1 noft 19 test 0.5919539928436279 0.4383227527141571 0.8580
|
||||
227 1 noft 19 valid 0.4107142686843872 0.4886990487575531 0.8436
|
||||
113 1 noft 20 test 0.6204081773757935 0.41948041319847107 0.9070
|
||||
233 1 noft 20 valid 0.47887325286865234 0.33019527792930603 0.9123
|
||||
119 1 noft 21 test 0.5813148617744446 0.5301401019096375 0.8790
|
||||
239 1 noft 21 valid 0.40229886770248413 0.5155616402626038 0.8768
|
||||
|
@@ -0,0 +1,241 @@
|
||||
index LMseed ft clsseed dataset F1_score loss accuracy
|
||||
0 0 ft-seed0 2 test 0.5645161271095276 0.43044528365135193 0.5435997247695923
|
||||
120 0 ft-seed0 2 valid 0.4166666567325592 0.4225634038448334 0.40362587571144104
|
||||
6 0 ft-seed0 3 test 0.5925925970077515 0.4165325462818146 0.5603249073028564
|
||||
126 0 ft-seed0 3 valid 0.4606741666793823 0.43441566824913025 0.46482881903648376
|
||||
12 0 ft-seed0 4 test 0.6065574288368225 0.4814380705356598 0.6015315651893616
|
||||
132 0 ft-seed0 4 valid 0.4285714626312256 0.5097822546958923 0.3987082242965698
|
||||
18 0 ft-seed0 5 test 0.570397138595581 0.44903358817100525 0.5506796836853027
|
||||
138 0 ft-seed0 5 valid 0.4457142949104309 0.4238314628601074 0.4373311400413513
|
||||
24 0 ft-seed0 6 test 0.5592105388641357 0.4162355363368988 0.5435535311698914
|
||||
144 0 ft-seed0 6 valid 0.42105263471603394 0.4097800552845001 0.40014225244522095
|
||||
30 0 ft-seed0 7 test 0.5714285969734192 0.4321300685405731 0.57682865858078
|
||||
150 0 ft-seed0 7 valid 0.44871795177459717 0.4115864932537079 0.44700318574905396
|
||||
36 0 ft-seed0 8 test 0.5772358179092407 0.4818994700908661 0.5596418380737305
|
||||
156 0 ft-seed0 8 valid 0.45945945382118225 0.447449266910553 0.43708524107933044
|
||||
42 0 ft-seed0 9 test 0.5 0.45570382475852966 0.45577582716941833
|
||||
162 0 ft-seed0 9 valid 0.4571428596973419 0.327668696641922 0.45067551732063293
|
||||
48 0 ft-seed0 10 test 0.5349794626235962 0.4865730404853821 0.5182504057884216
|
||||
168 0 ft-seed0 10 valid 0.4129032492637634 0.434408038854599 0.4094066023826599
|
||||
54 0 ft-seed0 11 test 0.5433071255683899 0.4363311529159546 0.5251346230506897
|
||||
174 0 ft-seed0 11 valid 0.4615384638309479 0.38903355598449707 0.46455758810043335
|
||||
60 0 ft-seed0 12 test 0.5724906921386719 0.4853847026824951 0.5576902627944946
|
||||
180 0 ft-seed0 12 valid 0.47191014885902405 0.5130544304847717 0.47807759046554565
|
||||
66 0 ft-seed0 13 test 0.5742574334144592 0.40475693345069885 0.5624763369560242
|
||||
186 0 ft-seed0 13 valid 0.45771148800849915 0.4509955048561096 0.46506598591804504
|
||||
72 0 ft-seed0 14 test 0.5230769515037537 0.44990867376327515 0.4929400682449341
|
||||
192 0 ft-seed0 14 valid 0.4642857313156128 0.4322844445705414 0.46598318219184875
|
||||
78 0 ft-seed0 15 test 0.5526315569877625 0.47065943479537964 0.517307698726654
|
||||
198 0 ft-seed0 15 valid 0.4413793385028839 0.4142134487628937 0.4313492178916931
|
||||
84 0 ft-seed0 16 test 0.5087718963623047 0.5729814171791077 0.47096720337867737
|
||||
204 0 ft-seed0 16 valid 0.4383561909198761 0.4802896976470947 0.4158690273761749
|
||||
90 0 ft-seed0 17 test 0.560669481754303 0.47273412346839905 0.5466279983520508
|
||||
210 0 ft-seed0 17 valid 0.4055943787097931 0.4395078718662262 0.4165821969509125
|
||||
96 0 ft-seed0 18 test 0.5232067108154297 0.5001763701438904 0.48940691351890564
|
||||
216 0 ft-seed0 18 valid 0.45517241954803467 0.40583667159080505 0.44013768434524536
|
||||
102 0 ft-seed0 19 test 0.5130434632301331 0.548660397529602 0.5009177923202515
|
||||
222 0 ft-seed0 19 valid 0.49673202633857727 0.4328499138355255 0.4777645766735077
|
||||
108 0 ft-seed0 20 test 0.5132743716239929 0.5187202095985413 0.5138627886772156
|
||||
228 0 ft-seed0 20 valid 0.43609023094177246 0.407430499792099 0.42027491331100464
|
||||
114 0 ft-seed0 21 test 0.46153849363327026 0.4121854901313782 0.43544453382492065
|
||||
234 0 ft-seed0 21 valid 0.46268653869628906 0.2815968096256256 0.44372183084487915
|
||||
1 0 ft-seed1 2 test 0.4956521987915039 0.5943491458892822 0.47801804542541504
|
||||
121 0 ft-seed1 2 valid 0.4460431635379791 0.5221770405769348 0.42610034346580505
|
||||
7 0 ft-seed1 3 test 0.41624364256858826 0.7639743089675903 0.39021721482276917
|
||||
127 0 ft-seed1 3 valid 0.4137931168079376 0.4463430345058441 0.3821505904197693
|
||||
13 0 ft-seed1 4 test 0.5152838230133057 0.7346494197845459 0.4891136884689331
|
||||
133 0 ft-seed1 4 valid 0.4615384638309479 0.696619987487793 0.44672226905822754
|
||||
19 0 ft-seed1 5 test 0.580152690410614 0.44208887219429016 0.5483294129371643
|
||||
139 0 ft-seed1 5 valid 0.4588235318660736 0.5001108050346375 0.46335646510124207
|
||||
25 0 ft-seed1 6 test 0.5000000596046448 0.6066558957099915 0.494425892829895
|
||||
145 0 ft-seed1 6 valid 0.4964538514614105 0.4511015713214874 0.4882415533065796
|
||||
31 0 ft-seed1 7 test 0.5522388219833374 0.6283032298088074 0.5173004865646362
|
||||
151 0 ft-seed1 7 valid 0.4528301954269409 0.6680119037628174 0.4440968930721283
|
||||
37 0 ft-seed1 8 test 0.4888888895511627 0.6613699793815613 0.4632968306541443
|
||||
157 0 ft-seed1 8 valid 0.43609023094177246 0.4850305914878845 0.4160941541194916
|
||||
43 0 ft-seed1 9 test 0.5316455960273743 0.5851835012435913 0.5189670920372009
|
||||
163 0 ft-seed1 9 valid 0.4217686951160431 0.5449877381324768 0.41501468420028687
|
||||
49 0 ft-seed1 10 test 0.4651162922382355 0.7876351475715637 0.4422152638435364
|
||||
169 0 ft-seed1 10 valid 0.40875911712646484 0.6061959862709045 0.4077857434749603
|
||||
55 0 ft-seed1 11 test 0.568965494632721 0.5870274901390076 0.55535888671875
|
||||
175 0 ft-seed1 11 valid 0.46043166518211365 0.49774301052093506 0.4526840150356293
|
||||
61 0 ft-seed1 12 test 0.5461847186088562 0.5820209383964539 0.5405717492103577
|
||||
181 0 ft-seed1 12 valid 0.4458598792552948 0.5988472104072571 0.442074179649353
|
||||
67 0 ft-seed1 13 test 0.44131454825401306 0.6617334485054016 0.42308545112609863
|
||||
187 0 ft-seed1 13 valid 0.46715328097343445 0.4917261302471161 0.4438578188419342
|
||||
73 0 ft-seed1 14 test 0.582456111907959 0.5261735916137695 0.5650691390037537
|
||||
193 0 ft-seed1 14 valid 0.4687500298023224 0.5817894339561462 0.46887174248695374
|
||||
79 0 ft-seed1 15 test 0.5431034564971924 0.5236736536026001 0.5130492448806763
|
||||
199 0 ft-seed1 15 valid 0.4615384638309479 0.4515714943408966 0.45928627252578735
|
||||
85 0 ft-seed1 16 test 0.6300365924835205 0.46109575033187866 0.607157289981842
|
||||
205 0 ft-seed1 16 valid 0.43478259444236755 0.49476781487464905 0.4346143901348114
|
||||
91 0 ft-seed1 17 test 0.49122804403305054 0.5814564228057861 0.45914334058761597
|
||||
211 0 ft-seed1 17 valid 0.4142857491970062 0.4819534420967102 0.408165842294693
|
||||
97 0 ft-seed1 18 test 0.48510637879371643 0.4999200999736786 0.45851048827171326
|
||||
217 0 ft-seed1 18 valid 0.4400000274181366 0.39687463641166687 0.4465416967868805
|
||||
103 0 ft-seed1 19 test 0.5736434459686279 0.5443088412284851 0.5560984015464783
|
||||
223 0 ft-seed1 19 valid 0.44171783328056335 0.5909873843193054 0.4301799535751343
|
||||
109 0 ft-seed1 20 test 0.4864864647388458 0.5205869674682617 0.48399099707603455
|
||||
229 0 ft-seed1 20 valid 0.4117646813392639 0.3999001383781433 0.3924347460269928
|
||||
115 0 ft-seed1 21 test 0.6058091521263123 0.4227277338504791 0.5824961066246033
|
||||
235 0 ft-seed1 21 valid 0.4675324559211731 0.3723865747451782 0.46422505378723145
|
||||
2 0 noft 2 test 0.5433962941169739 0.4767005145549774 0.5214850902557373
|
||||
122 0 noft 2 valid 0.4457831382751465 0.4171012043952942 0.4381187856197357
|
||||
8 0 noft 3 test 0.5438596606254578 0.37441006302833557 0.5402586460113525
|
||||
128 0 noft 3 valid 0.4421052634716034 0.36205780506134033 0.44779273867607117
|
||||
14 0 noft 4 test 0.5405405163764954 0.5173587799072266 0.5371392965316772
|
||||
134 0 noft 4 valid 0.3813953697681427 0.5903511047363281 0.39343199133872986
|
||||
20 0 noft 5 test 0.5266667008399963 0.4419504404067993 0.5080550312995911
|
||||
140 0 noft 5 valid 0.4293193817138672 0.4399767816066742 0.435255229473114
|
||||
26 0 noft 6 test 0.5324674844741821 0.41715914011001587 0.5118011236190796
|
||||
146 0 noft 6 valid 0.44897958636283875 0.4160920977592468 0.4528375566005707
|
||||
32 0 noft 7 test 0.5679012537002563 0.4777500629425049 0.5508254766464233
|
||||
152 0 noft 7 valid 0.4142857491970062 0.42049285769462585 0.4008720815181732
|
||||
38 0 noft 8 test 0.5747800469398499 0.3598693311214447 0.5545601844787598
|
||||
158 0 noft 8 valid 0.44144144654273987 0.405771940946579 0.4474172294139862
|
||||
44 0 noft 9 test 0.5108224749565125 0.5093541145324707 0.48221442103385925
|
||||
164 0 noft 9 valid 0.4413793385028839 0.3927517235279083 0.426912397146225
|
||||
50 0 noft 10 test 0.5108224749565125 0.5246335864067078 0.48768067359924316
|
||||
170 0 noft 10 valid 0.447761207818985 0.4109422266483307 0.424713671207428
|
||||
56 0 noft 11 test 0.5603448152542114 0.5133260488510132 0.5482234358787537
|
||||
176 0 noft 11 valid 0.4661653935909271 0.38057011365890503 0.45219504833221436
|
||||
62 0 noft 12 test 0.5597269535064697 0.49656251072883606 0.5423819422721863
|
||||
182 0 noft 12 valid 0.44680851697921753 0.48734134435653687 0.4495908319950104
|
||||
68 0 noft 13 test 0.5232067108154297 0.5087217688560486 0.5083507895469666
|
||||
188 0 noft 13 valid 0.45255473256111145 0.3679719567298889 0.4423004686832428
|
||||
74 0 noft 14 test 0.536796510219574 0.454653263092041 0.4843226969242096
|
||||
194 0 noft 14 valid 0.4755244553089142 0.34815725684165955 0.46753519773483276
|
||||
80 0 noft 15 test 0.49339210987091064 0.4725494980812073 0.47351354360580444
|
||||
200 0 noft 15 valid 0.4571428596973419 0.35398390889167786 0.45776131749153137
|
||||
86 0 noft 16 test 0.5022830963134766 0.4819789230823517 0.46512266993522644
|
||||
206 0 noft 16 valid 0.3999999761581421 0.41223767399787903 0.38923001289367676
|
||||
92 0 noft 17 test 0.5546218752861023 0.48606786131858826 0.5333790183067322
|
||||
212 0 noft 17 valid 0.4313725531101227 0.4288560152053833 0.4281012713909149
|
||||
98 0 noft 18 test 0.565517246723175 0.4054659307003021 0.5478171706199646
|
||||
218 0 noft 18 valid 0.4421052634716034 0.4349640905857086 0.46671339869499207
|
||||
104 0 noft 19 test 0.493506520986557 0.5580055117607117 0.45603320002555847
|
||||
224 0 noft 19 valid 0.44285714626312256 0.44473594427108765 0.43967923521995544
|
||||
110 0 noft 20 test 0.5951557159423828 0.415360689163208 0.5806995630264282
|
||||
230 0 noft 20 valid 0.48275861144065857 0.39201340079307556 0.4733225107192993
|
||||
116 0 noft 21 test 0.5362318754196167 0.47137773036956787 0.5331786274909973
|
||||
236 0 noft 21 valid 0.4550897777080536 0.4185858368873596 0.4586920738220215
|
||||
3 1 ft-seed0 2 test 0.5490196347236633 0.5724288821220398 0.5155833959579468
|
||||
123 1 ft-seed0 2 valid 0.44117647409439087 0.6496979594230652 0.45821937918663025
|
||||
9 1 ft-seed0 3 test 0.5754386186599731 0.4020021855831146 0.566059410572052
|
||||
129 1 ft-seed0 3 valid 0.4422110617160797 0.42443081736564636 0.44480353593826294
|
||||
15 1 ft-seed0 4 test 0.6111111044883728 0.4388059079647064 0.5846359133720398
|
||||
135 1 ft-seed0 4 valid 0.44871795177459717 0.3783545196056366 0.4501579701900482
|
||||
21 1 ft-seed0 5 test 0.5679012537002563 0.49716848134994507 0.5324816703796387
|
||||
141 1 ft-seed0 5 valid 0.46357616782188416 0.3597354590892792 0.44924965500831604
|
||||
27 1 ft-seed0 6 test 0.5486725568771362 0.43432965874671936 0.5344046354293823
|
||||
147 1 ft-seed0 6 valid 0.43939390778541565 0.3266245424747467 0.4417502284049988
|
||||
33 1 ft-seed0 7 test 0.6108786463737488 0.4717611074447632 0.6045064926147461
|
||||
153 1 ft-seed0 7 valid 0.4313725531101227 0.43972310423851013 0.4156699478626251
|
||||
39 1 ft-seed0 8 test 0.5909091234207153 0.29768291115760803 0.5749288201332092
|
||||
159 1 ft-seed0 8 valid 0.4550897777080536 0.3085547983646393 0.4633788466453552
|
||||
45 1 ft-seed0 9 test 0.560669481754303 0.4620136320590973 0.5490953922271729
|
||||
165 1 ft-seed0 9 valid 0.44897958636283875 0.37488117814064026 0.4432413876056671
|
||||
51 1 ft-seed0 10 test 0.6086956858634949 0.42837387323379517 0.5826467275619507
|
||||
171 1 ft-seed0 10 valid 0.43478259444236755 0.44988685846328735 0.43672603368759155
|
||||
57 1 ft-seed0 11 test 0.5660377740859985 0.49595555663108826 0.5521074533462524
|
||||
177 1 ft-seed0 11 valid 0.4177215099334717 0.41470322012901306 0.420681893825531
|
||||
63 1 ft-seed0 12 test 0.5942028760910034 0.44078487157821655 0.5803970098495483
|
||||
183 1 ft-seed0 12 valid 0.44692739844322205 0.43711891770362854 0.4508112668991089
|
||||
69 1 ft-seed0 13 test 0.5753424763679504 0.5054553747177124 0.5615649223327637
|
||||
189 1 ft-seed0 13 valid 0.4333333671092987 0.42465242743492126 0.44402429461479187
|
||||
75 1 ft-seed0 14 test 0.607692301273346 0.36369848251342773 0.593707799911499
|
||||
195 1 ft-seed0 14 valid 0.4585987329483032 0.32929185032844543 0.4540887176990509
|
||||
81 1 ft-seed0 15 test 0.5620437860488892 0.3254164457321167 0.5418208837509155
|
||||
201 1 ft-seed0 15 valid 0.4497041702270508 0.2714768946170807 0.45483002066612244
|
||||
87 1 ft-seed0 16 test 0.6101694703102112 0.3997952938079834 0.5970664024353027
|
||||
207 1 ft-seed0 16 valid 0.4419889450073242 0.36827152967453003 0.44359642267227173
|
||||
93 1 ft-seed0 17 test 0.5723905563354492 0.4410214424133301 0.5432559251785278
|
||||
213 1 ft-seed0 17 valid 0.4365482032299042 0.4674760699272156 0.4319121241569519
|
||||
99 1 ft-seed0 18 test 0.5785713791847229 0.346145898103714 0.537955105304718
|
||||
219 1 ft-seed0 18 valid 0.4134078323841095 0.33745241165161133 0.4208257496356964
|
||||
105 1 ft-seed0 19 test 0.5414847135543823 0.505227267742157 0.5272364020347595
|
||||
225 1 ft-seed0 19 valid 0.4217686951160431 0.427654892206192 0.41587772965431213
|
||||
111 1 ft-seed0 20 test 0.5779467225074768 0.5614640712738037 0.5631630420684814
|
||||
231 1 ft-seed0 20 valid 0.45000001788139343 0.45731329917907715 0.44137561321258545
|
||||
117 1 ft-seed0 21 test 0.5573770403862 0.516575813293457 0.5341829657554626
|
||||
237 1 ft-seed0 21 valid 0.4026845395565033 0.4152399003505707 0.4038563370704651
|
||||
4 1 ft-seed1 2 test 0.5914397239685059 0.5607386231422424 0.5583197474479675
|
||||
124 1 ft-seed1 2 valid 0.43589746952056885 0.4342404901981354 0.4398580491542816
|
||||
10 1 ft-seed1 3 test 0.5737704634666443 0.518389880657196 0.5634279847145081
|
||||
130 1 ft-seed1 3 valid 0.42953020334243774 0.3975204527378082 0.4253101348876953
|
||||
16 1 ft-seed1 4 test 0.5836299061775208 0.3816649615764618 0.5631790161132812
|
||||
136 1 ft-seed1 4 valid 0.37931036949157715 0.36890846490859985 0.38074082136154175
|
||||
22 1 ft-seed1 5 test 0.6038960814476013 0.4200931787490845 0.5767818093299866
|
||||
142 1 ft-seed1 5 valid 0.45000001788139343 0.39361807703971863 0.4524843394756317
|
||||
28 1 ft-seed1 6 test 0.5573770999908447 0.4661884605884552 0.5439198613166809
|
||||
148 1 ft-seed1 6 valid 0.4565217196941376 0.38068053126335144 0.4747180938720703
|
||||
34 1 ft-seed1 7 test 0.5991902351379395 0.5403699278831482 0.5718095898628235
|
||||
154 1 ft-seed1 7 valid 0.4556961953639984 0.4898127019405365 0.4610288441181183
|
||||
40 1 ft-seed1 8 test 0.63670414686203 0.29799285531044006 0.6160865426063538
|
||||
160 1 ft-seed1 8 valid 0.5176470279693604 0.2861432135105133 0.5251745581626892
|
||||
46 1 ft-seed1 9 test 0.5886792540550232 0.392250120639801 0.5767463445663452
|
||||
166 1 ft-seed1 9 valid 0.44720497727394104 0.34671708941459656 0.44917047023773193
|
||||
52 1 ft-seed1 10 test 0.5737704634666443 0.5335988402366638 0.55939781665802
|
||||
172 1 ft-seed1 10 valid 0.4109589159488678 0.43126046657562256 0.4084997773170471
|
||||
58 1 ft-seed1 11 test 0.5517241954803467 0.40790465474128723 0.5228269696235657
|
||||
178 1 ft-seed1 11 valid 0.43113774061203003 0.3307510316371918 0.43079525232315063
|
||||
64 1 ft-seed1 12 test 0.5423728823661804 0.5214505195617676 0.5331392288208008
|
||||
184 1 ft-seed1 12 valid 0.46979862451553345 0.40523862838745117 0.45541146397590637
|
||||
70 1 ft-seed1 13 test 0.5725191235542297 0.42663702368736267 0.5290747284889221
|
||||
190 1 ft-seed1 13 valid 0.44692739844322205 0.4057123363018036 0.43778395652770996
|
||||
76 1 ft-seed1 14 test 0.6165413856506348 0.4142192006111145 0.6040186882019043
|
||||
196 1 ft-seed1 14 valid 0.4431818127632141 0.35879600048065186 0.4440614879131317
|
||||
82 1 ft-seed1 15 test 0.5925925970077515 0.31346964836120605 0.5623492002487183
|
||||
202 1 ft-seed1 15 valid 0.44171783328056335 0.27289724349975586 0.4450274705886841
|
||||
88 1 ft-seed1 16 test 0.599303126335144 0.4012908637523651 0.5711842179298401
|
||||
208 1 ft-seed1 16 valid 0.4093567132949829 0.3552548289299011 0.4148019850254059
|
||||
94 1 ft-seed1 17 test 0.5680934190750122 0.5617448687553406 0.5498369932174683
|
||||
214 1 ft-seed1 17 valid 0.48447200655937195 0.4144614338874817 0.4857429265975952
|
||||
100 1 ft-seed1 18 test 0.5746268630027771 0.5116666555404663 0.5738064646720886
|
||||
220 1 ft-seed1 18 valid 0.43023255467414856 0.4778071343898773 0.4372516870498657
|
||||
106 1 ft-seed1 19 test 0.5517240762710571 0.6141948103904724 0.5578478574752808
|
||||
226 1 ft-seed1 19 valid 0.42396309971809387 0.6560723185539246 0.43690624833106995
|
||||
112 1 ft-seed1 20 test 0.5658263564109802 0.4831385910511017 0.5478011965751648
|
||||
232 1 ft-seed1 20 valid 0.43103447556495667 0.5641922950744629 0.4400008022785187
|
||||
118 1 ft-seed1 21 test 0.5827814340591431 0.3617390990257263 0.5641347765922546
|
||||
238 1 ft-seed1 21 valid 0.3886255919933319 0.39799734950065613 0.39502865076065063
|
||||
5 1 noft 2 test 0.5281385183334351 0.4756642282009125 0.5024991631507874
|
||||
125 1 noft 2 valid 0.40287768840789795 0.3857330083847046 0.4048222303390503
|
||||
11 1 noft 3 test 0.5643153190612793 0.4803626239299774 0.561382532119751
|
||||
131 1 noft 3 valid 0.3866666555404663 0.47957706451416016 0.3874838650226593
|
||||
17 1 noft 4 test 0.5821918249130249 0.3984341025352478 0.5677289962768555
|
||||
137 1 noft 4 valid 0.3932584226131439 0.41079074144363403 0.41720184683799744
|
||||
23 1 noft 5 test 0.5921052694320679 0.4569582939147949 0.5805506110191345
|
||||
143 1 noft 5 valid 0.46632125973701477 0.41115298867225647 0.47134506702423096
|
||||
29 1 noft 6 test 0.5925925970077515 0.411027729511261 0.556318998336792
|
||||
149 1 noft 6 valid 0.4296296536922455 0.3237917125225067 0.43004703521728516
|
||||
35 1 noft 7 test 0.5934960246086121 0.47999921441078186 0.5628717541694641
|
||||
155 1 noft 7 valid 0.45945945382118225 0.34085509181022644 0.45467492938041687
|
||||
41 1 noft 8 test 0.5552050471305847 0.4669162631034851 0.5426645874977112
|
||||
161 1 noft 8 valid 0.44897958636283875 0.4272935688495636 0.46510761976242065
|
||||
47 1 noft 9 test 0.5791505575180054 0.45815664529800415 0.5680941939353943
|
||||
167 1 noft 9 valid 0.4125000238418579 0.39577150344848633 0.43273958563804626
|
||||
53 1 noft 10 test 0.5773195624351501 0.44457459449768066 0.5565718412399292
|
||||
173 1 noft 10 valid 0.41111111640930176 0.4423538148403168 0.42672690749168396
|
||||
59 1 noft 11 test 0.563265323638916 0.534534752368927 0.547646701335907
|
||||
179 1 noft 11 valid 0.41134753823280334 0.43552181124687195 0.40916135907173157
|
||||
65 1 noft 12 test 0.5795053243637085 0.5075364708900452 0.5634565949440002
|
||||
185 1 noft 12 valid 0.45901644229888916 0.4783734381198883 0.46437951922416687
|
||||
71 1 noft 13 test 0.5666667222976685 0.48282700777053833 0.5505626201629639
|
||||
191 1 noft 13 valid 0.4647887349128723 0.3755517303943634 0.4725068211555481
|
||||
77 1 noft 14 test 0.579365074634552 0.4776264429092407 0.5561549663543701
|
||||
197 1 noft 14 valid 0.40522876381874084 0.4339909851551056 0.40936824679374695
|
||||
83 1 noft 15 test 0.6067415475845337 0.28845077753067017 0.5883947610855103
|
||||
203 1 noft 15 valid 0.43589746952056885 0.28395193815231323 0.43057218194007874
|
||||
89 1 noft 16 test 0.607692301273346 0.3595740497112274 0.5877529382705688
|
||||
209 1 noft 16 valid 0.4268292486667633 0.35805508494377136 0.4262784719467163
|
||||
95 1 noft 17 test 0.6209386587142944 0.5146023035049438 0.6097524762153625
|
||||
215 1 noft 17 valid 0.43023255467414856 0.495402067899704 0.442810595035553
|
||||
101 1 noft 18 test 0.5602836608886719 0.46523019671440125 0.5336311459541321
|
||||
221 1 noft 18 valid 0.4482758641242981 0.43405812978744507 0.45807918906211853
|
||||
107 1 noft 19 test 0.5919539928436279 0.4383227527141571 0.5782817006111145
|
||||
227 1 noft 19 valid 0.4107142686843872 0.4886990487575531 0.4262794852256775
|
||||
113 1 noft 20 test 0.6204081773757935 0.41948041319847107 0.6076886057853699
|
||||
233 1 noft 20 valid 0.47887325286865234 0.33019527792930603 0.467338502407074
|
||||
119 1 noft 21 test 0.5813148617744446 0.5301401019096375 0.5666437745094299
|
||||
239 1 noft 21 valid 0.40229886770248413 0.5155616402626038 0.41759130358695984
|
||||
|
Reference in new issue
Block a user