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This model is a fine-tuned version of answerdotai/ModernBERT-large on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.5042
• Classification Report: {'0': {'precision': 0.9435336976320583, 'recall': 0.9761306532663316, 'f1-score': 0.9595554183389935, 'support': 1592.0}, '1': {'precision': 0.8146341463414634, 'recall': 0.6423076923076924, 'f1-score': 0.7182795698924731, 'support': 260.0}, 'accuracy': 0.9292656587473002, 'macro avg': {'precision': 0.8790839219867608, 'recall': 0.8092191727870119, 'f1-score': 0.8389174941157334, 'support': 1852.0}, 'weighted avg': {'precision': 0.9254376483148041, 'recall': 0.9292656587473002, 'f1-score': 0.9256829990106483, 'support': 1852.0}}

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-06
• train_batch_size: 16
• eval_batch_size: 16
• seed: 52
• distributed_type: multi-GPU
• num_devices: 4
• total_train_batch_size: 64
• total_eval_batch_size: 64
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Classification Report
No log	1.0	98	0.2393	{'0': {'precision': 0.8851540616246498, 'recall': 0.992462311557789, 'f1-score': 0.9357417826473201, 'support': 1592.0}, '1': {'precision': 0.8208955223880597, 'recall': 0.21153846153846154, 'f1-score': 0.3363914373088685, 'support': 260.0}, 'accuracy': 0.8828293736501079, 'macro avg': {'precision': 0.8530247920063547, 'recall': 0.6020003865481253, 'f1-score': 0.6360666099780943, 'support': 1852.0}, 'weighted avg': {'precision': 0.8761328844100097, 'recall': 0.8828293736501079, 'f1-score': 0.8515997255263713, 'support': 1852.0}}
No log	2.0	196	0.2073	{'0': {'precision': 0.9095100864553314, 'recall': 0.9912060301507538, 'f1-score': 0.9486023444544635, 'support': 1592.0}, '1': {'precision': 0.8803418803418803, 'recall': 0.39615384615384613, 'f1-score': 0.5464190981432361, 'support': 260.0}, 'accuracy': 0.9076673866090713, 'macro avg': {'precision': 0.8949259833986059, 'recall': 0.6936799381523, 'f1-score': 0.7475107212988498, 'support': 1852.0}, 'weighted avg': {'precision': 0.9054151979080867, 'recall': 0.9076673866090713, 'f1-score': 0.8921403336332328, 'support': 1852.0}}
No log	3.0	294	0.2054	{'0': {'precision': 0.9635653871177619, 'recall': 0.9302763819095478, 'f1-score': 0.9466283157558325, 'support': 1592.0}, '1': {'precision': 0.6476190476190476, 'recall': 0.7846153846153846, 'f1-score': 0.7095652173913043, 'support': 260.0}, 'accuracy': 0.9098272138228942, 'macro avg': {'precision': 0.8055922173684047, 'recall': 0.8574458832624662, 'f1-score': 0.8280967665735683, 'support': 1852.0}, 'weighted avg': {'precision': 0.9192100694775535, 'recall': 0.9098272138228942, 'f1-score': 0.9133473192251752, 'support': 1852.0}}
No log	4.0	392	0.2393	{'0': {'precision': 0.9171974522292994, 'recall': 0.9949748743718593, 'f1-score': 0.9545043687857788, 'support': 1592.0}, '1': {'precision': 0.936, 'recall': 0.45, 'f1-score': 0.6077922077922078, 'support': 260.0}, 'accuracy': 0.9184665226781857, 'macro avg': {'precision': 0.9265987261146498, 'recall': 0.7224874371859297, 'f1-score': 0.7811482882889933, 'support': 1852.0}, 'weighted avg': {'precision': 0.9198371187629831, 'recall': 0.9184665226781857, 'f1-score': 0.905829875341757, 'support': 1852.0}}
No log	5.0	490	0.1935	{'0': {'precision': 0.9317912218268091, 'recall': 0.9868090452261307, 'f1-score': 0.9585112873703477, 'support': 1592.0}, '1': {'precision': 0.8734939759036144, 'recall': 0.5576923076923077, 'f1-score': 0.6807511737089202, 'support': 260.0}, 'accuracy': 0.9265658747300216, 'macro avg': {'precision': 0.9026425988652118, 'recall': 0.7722506764592192, 'f1-score': 0.819631230539634, 'support': 1852.0}, 'weighted avg': {'precision': 0.9236069432414794, 'recall': 0.9265658747300216, 'f1-score': 0.9195168869643159, 'support': 1852.0}}
0.2091	6.0	588	0.2311	{'0': {'precision': 0.9330568720379147, 'recall': 0.989321608040201, 'f1-score': 0.9603658536585366, 'support': 1592.0}, '1': {'precision': 0.8963414634146342, 'recall': 0.5653846153846154, 'f1-score': 0.6933962264150944, 'support': 260.0}, 'accuracy': 0.9298056155507559, 'macro avg': {'precision': 0.9146991677262744, 'recall': 0.7773531117124082, 'f1-score': 0.8268810400368154, 'support': 1852.0}, 'weighted avg': {'precision': 0.9279024410216873, 'recall': 0.9298056155507559, 'f1-score': 0.9228863163565414, 'support': 1852.0}}
0.2091	7.0	686	0.2382	{'0': {'precision': 0.9474006116207951, 'recall': 0.9729899497487438, 'f1-score': 0.9600247908273939, 'support': 1592.0}, '1': {'precision': 0.8018433179723502, 'recall': 0.6692307692307692, 'f1-score': 0.7295597484276729, 'support': 260.0}, 'accuracy': 0.9303455723542117, 'macro avg': {'precision': 0.8746219647965727, 'recall': 0.8211103594897564, 'f1-score': 0.8447922696275334, 'support': 1852.0}, 'weighted avg': {'precision': 0.9269660023612942, 'recall': 0.9303455723542117, 'f1-score': 0.9276700872507592, 'support': 1852.0}}
0.2091	8.0	784	0.2971	{'0': {'precision': 0.947112462006079, 'recall': 0.9786432160804021, 'f1-score': 0.9626197096076614, 'support': 1592.0}, '1': {'precision': 0.8357487922705314, 'recall': 0.6653846153846154, 'f1-score': 0.7408993576017131, 'support': 260.0}, 'accuracy': 0.9346652267818575, 'macro avg': {'precision': 0.8914306271383052, 'recall': 0.8220139157325087, 'f1-score': 0.8517595336046873, 'support': 1852.0}, 'weighted avg': {'precision': 0.9314782535118876, 'recall': 0.9346652267818575, 'f1-score': 0.9314926623498069, 'support': 1852.0}}
0.2091	9.0	882	0.3370	{'0': {'precision': 0.9486552567237164, 'recall': 0.9748743718592965, 'f1-score': 0.9615861214374225, 'support': 1592.0}, '1': {'precision': 0.8148148148148148, 'recall': 0.676923076923077, 'f1-score': 0.7394957983193278, 'support': 260.0}, 'accuracy': 0.9330453563714903, 'macro avg': {'precision': 0.8817350357692656, 'recall': 0.8258987243911867, 'f1-score': 0.8505409598783751, 'support': 1852.0}, 'weighted avg': {'precision': 0.9298655618552961, 'recall': 0.9330453563714903, 'f1-score': 0.9304071343906056, 'support': 1852.0}}
0.2091	10.0	980	0.3887	{'0': {'precision': 0.9488286066584464, 'recall': 0.9667085427135679, 'f1-score': 0.9576851275668948, 'support': 1592.0}, '1': {'precision': 0.7695652173913043, 'recall': 0.6807692307692308, 'f1-score': 0.7224489795918367, 'support': 260.0}, 'accuracy': 0.9265658747300216, 'macro avg': {'precision': 0.8591969120248754, 'recall': 0.8237388867413993, 'f1-score': 0.8400670535793657, 'support': 1852.0}, 'weighted avg': {'precision': 0.923662040130662, 'recall': 0.9265658747300216, 'f1-score': 0.9246606143522538, 'support': 1852.0}}
0.0381	11.0	1078	0.3918	{'0': {'precision': 0.9510532837670385, 'recall': 0.9641959798994975, 'f1-score': 0.9575795383655645, 'support': 1592.0}, '1': {'precision': 0.7605042016806722, 'recall': 0.6961538461538461, 'f1-score': 0.7269076305220884, 'support': 260.0}, 'accuracy': 0.9265658747300216, 'macro avg': {'precision': 0.8557787427238553, 'recall': 0.8301749130266718, 'f1-score': 0.8422435844438265, 'support': 1852.0}, 'weighted avg': {'precision': 0.9243023327181965, 'recall': 0.9265658747300216, 'f1-score': 0.9251957932039534, 'support': 1852.0}}
0.0381	12.0	1176	0.4461	{'0': {'precision': 0.9425981873111783, 'recall': 0.9798994974874372, 'f1-score': 0.9608869725900832, 'support': 1592.0}, '1': {'precision': 0.8375634517766497, 'recall': 0.6346153846153846, 'f1-score': 0.7221006564551422, 'support': 260.0}, 'accuracy': 0.9314254859611231, 'macro avg': {'precision': 0.890080819543914, 'recall': 0.8072574410514108, 'f1-score': 0.8414938145226127, 'support': 1852.0}, 'weighted avg': {'precision': 0.9278524900979076, 'recall': 0.9314254859611231, 'f1-score': 0.9273640556380935, 'support': 1852.0}}
0.0381	13.0	1274	0.4299	{'0': {'precision': 0.9456322541233965, 'recall': 0.9723618090452262, 'f1-score': 0.9588107773304428, 'support': 1592.0}, '1': {'precision': 0.7953488372093023, 'recall': 0.6576923076923077, 'f1-score': 0.72, 'support': 260.0}, 'accuracy': 0.9281857451403888, 'macro avg': {'precision': 0.8704905456663494, 'recall': 0.8150270583687669, 'f1-score': 0.8394053886652214, 'support': 1852.0}, 'weighted avg': {'precision': 0.9245341502369685, 'recall': 0.9281857451403888, 'f1-score': 0.925284426301331, 'support': 1852.0}}
0.0381	14.0	1372	0.4699	{'0': {'precision': 0.9418181818181818, 'recall': 0.9761306532663316, 'f1-score': 0.958667489204195, 'support': 1592.0}, '1': {'precision': 0.8118811881188119, 'recall': 0.6307692307692307, 'f1-score': 0.70995670995671, 'support': 260.0}, 'accuracy': 0.927645788336933, 'macro avg': {'precision': 0.8768496849684968, 'recall': 0.8034499420177812, 'f1-score': 0.8343120995804525, 'support': 1852.0}, 'weighted avg': {'precision': 0.9235764872383566, 'recall': 0.927645788336933, 'f1-score': 0.9237512890938568, 'support': 1852.0}}
0.0381	15.0	1470	0.4570	{'0': {'precision': 0.9456322541233965, 'recall': 0.9723618090452262, 'f1-score': 0.9588107773304428, 'support': 1592.0}, '1': {'precision': 0.7953488372093023, 'recall': 0.6576923076923077, 'f1-score': 0.72, 'support': 260.0}, 'accuracy': 0.9281857451403888, 'macro avg': {'precision': 0.8704905456663494, 'recall': 0.8150270583687669, 'f1-score': 0.8394053886652214, 'support': 1852.0}, 'weighted avg': {'precision': 0.9245341502369685, 'recall': 0.9281857451403888, 'f1-score': 0.925284426301331, 'support': 1852.0}}
0.0016	16.0	1568	0.4601	{'0': {'precision': 0.9429957550030321, 'recall': 0.9767587939698492, 'f1-score': 0.9595803764270286, 'support': 1592.0}, '1': {'precision': 0.8177339901477833, 'recall': 0.6384615384615384, 'f1-score': 0.7170626349892009, 'support': 260.0}, 'accuracy': 0.9292656587473002, 'macro avg': {'precision': 0.8803648725754076, 'recall': 0.8076101662156938, 'f1-score': 0.8383215057081148, 'support': 1852.0}, 'weighted avg': {'precision': 0.9254104100449518, 'recall': 0.9292656587473002, 'f1-score': 0.9255336092705302, 'support': 1852.0}}
0.0016	17.0	1666	0.4693	{'0': {'precision': 0.9478527607361963, 'recall': 0.9704773869346733, 'f1-score': 0.9590316573556797, 'support': 1592.0}, '1': {'precision': 0.7882882882882883, 'recall': 0.6730769230769231, 'f1-score': 0.7261410788381742, 'support': 260.0}, 'accuracy': 0.9287257019438445, 'macro avg': {'precision': 0.8680705245122423, 'recall': 0.8217771550057982, 'f1-score': 0.842586368096927, 'support': 1852.0}, 'weighted avg': {'precision': 0.9254517008892977, 'recall': 0.9287257019438445, 'f1-score': 0.9263364357495505, 'support': 1852.0}}
0.0016	18.0	1764	0.4702	{'0': {'precision': 0.9457317073170731, 'recall': 0.9742462311557789, 'f1-score': 0.9597772277227723, 'support': 1592.0}, '1': {'precision': 0.8066037735849056, 'recall': 0.6576923076923077, 'f1-score': 0.7245762711864406, 'support': 260.0}, 'accuracy': 0.9298056155507559, 'macro avg': {'precision': 0.8761677404509893, 'recall': 0.8159692694240432, 'f1-score': 0.8421767494546064, 'support': 1852.0}, 'weighted avg': {'precision': 0.9261997079810237, 'recall': 0.9298056155507559, 'f1-score': 0.9267576549908899, 'support': 1852.0}}
0.0016	19.0	1862	0.4698	{'0': {'precision': 0.9435679611650486, 'recall': 0.9767587939698492, 'f1-score': 0.9598765432098766, 'support': 1592.0}, '1': {'precision': 0.8186274509803921, 'recall': 0.6423076923076924, 'f1-score': 0.7198275862068966, 'support': 260.0}, 'accuracy': 0.9298056155507559, 'macro avg': {'precision': 0.8810977060727203, 'recall': 0.8095332431387707, 'f1-score': 0.8398520647083866, 'support': 1852.0}, 'weighted avg': {'precision': 0.9260277167546757, 'recall': 0.9298056155507559, 'f1-score': 0.926176365660862, 'support': 1852.0}}
0.0016	20.0	1960	0.4940	{'0': {'precision': 0.9408569704284853, 'recall': 0.9792713567839196, 'f1-score': 0.9596799015081564, 'support': 1592.0}, '1': {'precision': 0.8307692307692308, 'recall': 0.6230769230769231, 'f1-score': 0.7120879120879121, 'support': 260.0}, 'accuracy': 0.9292656587473002, 'macro avg': {'precision': 0.8858131005988581, 'recall': 0.8011741399304213, 'f1-score': 0.8358839067980343, 'support': 1852.0}, 'weighted avg': {'precision': 0.9254018881869053, 'recall': 0.9292656587473002, 'f1-score': 0.9249207669243208, 'support': 1852.0}}
0.0015	21.0	2058	0.4787	{'0': {'precision': 0.944647201946472, 'recall': 0.9755025125628141, 'f1-score': 0.9598269468479604, 'support': 1592.0}, '1': {'precision': 0.8125, 'recall': 0.65, 'f1-score': 0.7222222222222222, 'support': 260.0}, 'accuracy': 0.9298056155507559, 'macro avg': {'precision': 0.878573600973236, 'recall': 0.812751256281407, 'f1-score': 0.8410245845350913, 'support': 1852.0}, 'weighted avg': {'precision': 0.9260952189518269, 'recall': 0.9298056155507559, 'f1-score': 0.9264699120732888, 'support': 1852.0}}
0.0015	22.0	2156	0.4901	{'0': {'precision': 0.9396863691194209, 'recall': 0.9786432160804021, 'f1-score': 0.9587692307692308, 'support': 1592.0}, '1': {'precision': 0.8247422680412371, 'recall': 0.6153846153846154, 'f1-score': 0.7048458149779736, 'support': 260.0}, 'accuracy': 0.927645788336933, 'macro avg': {'precision': 0.8822143185803291, 'recall': 0.7970139157325087, 'f1-score': 0.8318075228736022, 'support': 1852.0}, 'weighted avg': {'precision': 0.9235495082769113, 'recall': 0.927645788336933, 'f1-score': 0.9231212350317972, 'support': 1852.0}}
0.0015	23.0	2254	0.4844	{'0': {'precision': 0.9436021831412977, 'recall': 0.9773869346733668, 'f1-score': 0.9601974699166924, 'support': 1592.0}, '1': {'precision': 0.8226600985221675, 'recall': 0.6423076923076924, 'f1-score': 0.7213822894168467, 'support': 260.0}, 'accuracy': 0.9303455723542117, 'macro avg': {'precision': 0.8831311408317326, 'recall': 0.8098473134905295, 'f1-score': 0.8407898796667695, 'support': 1852.0}, 'weighted avg': {'precision': 0.9266232727736013, 'recall': 0.9303455723542117, 'f1-score': 0.9266705007320487, 'support': 1852.0}}
0.0015	24.0	2352	0.4912	{'0': {'precision': 0.9431336963097399, 'recall': 0.9792713567839196, 'f1-score': 0.9608628659476117, 'support': 1592.0}, '1': {'precision': 0.8341708542713567, 'recall': 0.6384615384615384, 'f1-score': 0.7233115468409586, 'support': 260.0}, 'accuracy': 0.9314254859611231, 'macro avg': {'precision': 0.8886522752905484, 'recall': 0.808866447622729, 'f1-score': 0.8420872063942851, 'support': 1852.0}, 'weighted avg': {'precision': 0.9278365370602909, 'recall': 0.9314254859611231, 'f1-score': 0.9275133287080167, 'support': 1852.0}}
0.0015	25.0	2450	0.5239	{'0': {'precision': 0.9409638554216867, 'recall': 0.9811557788944724, 'f1-score': 0.9606396063960639, 'support': 1592.0}, '1': {'precision': 0.84375, 'recall': 0.6230769230769231, 'f1-score': 0.7168141592920354, 'support': 260.0}, 'accuracy': 0.9308855291576674, 'macro avg': {'precision': 0.8923569277108434, 'recall': 0.8021163509856977, 'f1-score': 0.8387268828440497, 'support': 1852.0}, 'weighted avg': {'precision': 0.9273161219391605, 'recall': 0.9308855291576674, 'f1-score': 0.926409252051006, 'support': 1852.0}}
0.0011	26.0	2548	0.5042	{'0': {'precision': 0.9414604707302353, 'recall': 0.9798994974874372, 'f1-score': 0.9602954755309326, 'support': 1592.0}, '1': {'precision': 0.8358974358974359, 'recall': 0.6269230769230769, 'f1-score': 0.7164835164835165, 'support': 260.0}, 'accuracy': 0.9303455723542117, 'macro avg': {'precision': 0.8886789533138356, 'recall': 0.803411287205257, 'f1-score': 0.8383894960072246, 'support': 1852.0}, 'weighted avg': {'precision': 0.9266406062288703, 'recall': 0.9303455723542117, 'f1-score': 0.9260670147575373, 'support': 1852.0}}
0.0011	27.0	2646	0.4792	{'0': {'precision': 0.9449877750611247, 'recall': 0.9711055276381909, 'f1-score': 0.9578686493184635, 'support': 1592.0}, '1': {'precision': 0.7870370370370371, 'recall': 0.6538461538461539, 'f1-score': 0.7142857142857143, 'support': 260.0}, 'accuracy': 0.9265658747300216, 'macro avg': {'precision': 0.8660124060490809, 'recall': 0.8124758407421724, 'f1-score': 0.8360771818020889, 'support': 1852.0}, 'weighted avg': {'precision': 0.9228132654033154, 'recall': 0.9265658747300216, 'f1-score': 0.9236723409445354, 'support': 1852.0}}
0.0011	28.0	2744	0.5223	{'0': {'precision': 0.9392663860493085, 'recall': 0.9811557788944724, 'f1-score': 0.9597542242703533, 'support': 1592.0}, '1': {'precision': 0.8412698412698413, 'recall': 0.6115384615384616, 'f1-score': 0.7082405345211581, 'support': 260.0}, 'accuracy': 0.9292656587473002, 'macro avg': {'precision': 0.8902681136595749, 'recall': 0.7963471202164669, 'f1-score': 0.8339973793957557, 'support': 1852.0}, 'weighted avg': {'precision': 0.9255087717714136, 'recall': 0.9292656587473002, 'f1-score': 0.9244445270053475, 'support': 1852.0}}
0.0011	29.0	2842	0.4942	{'0': {'precision': 0.9425287356321839, 'recall': 0.9786432160804021, 'f1-score': 0.9602465331278891, 'support': 1592.0}, '1': {'precision': 0.8291457286432161, 'recall': 0.6346153846153846, 'f1-score': 0.7189542483660131, 'support': 260.0}, 'accuracy': 0.9303455723542117, 'macro avg': {'precision': 0.8858372321376999, 'recall': 0.8066293003478933, 'f1-score': 0.8396003907469511, 'support': 1852.0}, 'weighted avg': {'precision': 0.926611034866994, 'recall': 0.9303455723542117, 'f1-score': 0.9263718063254659, 'support': 1852.0}}
0.0011	30.0	2940	0.5047	{'0': {'precision': 0.9413897280966768, 'recall': 0.9786432160804021, 'f1-score': 0.9596550662149677, 'support': 1592.0}, '1': {'precision': 0.8274111675126904, 'recall': 0.6269230769230769, 'f1-score': 0.7133479212253829, 'support': 260.0}, 'accuracy': 0.9292656587473002, 'macro avg': {'precision': 0.8844004478046836, 'recall': 0.8027831465017394, 'f1-score': 0.8365014937201753, 'support': 1852.0}, 'weighted avg': {'precision': 0.9253884182954693, 'recall': 0.9292656587473002, 'f1-score': 0.9250763093589783, 'support': 1852.0}}
0.001	31.0	3038	0.5236	{'0': {'precision': 0.9403254972875226, 'recall': 0.9798994974874372, 'f1-score': 0.9597047062442325, 'support': 1592.0}, '1': {'precision': 0.8341968911917098, 'recall': 0.6192307692307693, 'f1-score': 0.7108167770419426, 'support': 260.0}, 'accuracy': 0.9292656587473002, 'macro avg': {'precision': 0.8872611942396162, 'recall': 0.7995651333591032, 'f1-score': 0.8352607416430875, 'support': 1852.0}, 'weighted avg': {'precision': 0.9254262329328189, 'recall': 0.9292656587473002, 'f1-score': 0.9247636362698289, 'support': 1852.0}}
0.001	32.0	3136	0.5206	{'0': {'precision': 0.9403254972875226, 'recall': 0.9798994974874372, 'f1-score': 0.9597047062442325, 'support': 1592.0}, '1': {'precision': 0.8341968911917098, 'recall': 0.6192307692307693, 'f1-score': 0.7108167770419426, 'support': 260.0}, 'accuracy': 0.9292656587473002, 'macro avg': {'precision': 0.8872611942396162, 'recall': 0.7995651333591032, 'f1-score': 0.8352607416430875, 'support': 1852.0}, 'weighted avg': {'precision': 0.9254262329328189, 'recall': 0.9292656587473002, 'f1-score': 0.9247636362698289, 'support': 1852.0}}
0.001	33.0	3234	0.5022	{'0': {'precision': 0.9425287356321839, 'recall': 0.9786432160804021, 'f1-score': 0.9602465331278891, 'support': 1592.0}, '1': {'precision': 0.8291457286432161, 'recall': 0.6346153846153846, 'f1-score': 0.7189542483660131, 'support': 260.0}, 'accuracy': 0.9303455723542117, 'macro avg': {'precision': 0.8858372321376999, 'recall': 0.8066293003478933, 'f1-score': 0.8396003907469511, 'support': 1852.0}, 'weighted avg': {'precision': 0.926611034866994, 'recall': 0.9303455723542117, 'f1-score': 0.9263718063254659, 'support': 1852.0}}
0.001	34.0	3332	0.4933	{'0': {'precision': 0.9435336976320583, 'recall': 0.9761306532663316, 'f1-score': 0.9595554183389935, 'support': 1592.0}, '1': {'precision': 0.8146341463414634, 'recall': 0.6423076923076924, 'f1-score': 0.7182795698924731, 'support': 260.0}, 'accuracy': 0.9292656587473002, 'macro avg': {'precision': 0.8790839219867608, 'recall': 0.8092191727870119, 'f1-score': 0.8389174941157334, 'support': 1852.0}, 'weighted avg': {'precision': 0.9254376483148041, 'recall': 0.9292656587473002, 'f1-score': 0.9256829990106483, 'support': 1852.0}}
0.001	35.0	3430	0.4717	{'0': {'precision': 0.9484346224677717, 'recall': 0.9704773869346733, 'f1-score': 0.9593294008072027, 'support': 1592.0}, '1': {'precision': 0.7892376681614349, 'recall': 0.676923076923077, 'f1-score': 0.7287784679089027, 'support': 260.0}, 'accuracy': 0.9292656587473002, 'macro avg': {'precision': 0.8688361453146033, 'recall': 0.8237002319288751, 'f1-score': 0.8440539343580526, 'support': 1852.0}, 'weighted avg': {'precision': 0.9260851580403161, 'recall': 0.9292656587473002, 'f1-score': 0.9269626391692125, 'support': 1852.0}}
0.0009	36.0	3528	0.4830	{'0': {'precision': 0.9451553930530164, 'recall': 0.9742462311557789, 'f1-score': 0.9594803587998763, 'support': 1592.0}, '1': {'precision': 0.8056872037914692, 'recall': 0.6538461538461539, 'f1-score': 0.721868365180467, 'support': 260.0}, 'accuracy': 0.9292656587473002, 'macro avg': {'precision': 0.8754212984222428, 'recall': 0.8140461925009663, 'f1-score': 0.8406743619901716, 'support': 1852.0}, 'weighted avg': {'precision': 0.9255756256620864, 'recall': 0.9292656587473002, 'f1-score': 0.9261223035401319, 'support': 1852.0}}
0.0009	37.0	3626	0.4925	{'0': {'precision': 0.9429265330904675, 'recall': 0.9755025125628141, 'f1-score': 0.9589379438098179, 'support': 1592.0}, '1': {'precision': 0.8097560975609757, 'recall': 0.6384615384615384, 'f1-score': 0.7139784946236559, 'support': 260.0}, 'accuracy': 0.9281857451403888, 'macro avg': {'precision': 0.8763413153257216, 'recall': 0.8069820255121762, 'f1-score': 0.8364582192167369, 'support': 1852.0}, 'weighted avg': {'precision': 0.9242308995928066, 'recall': 0.9281857451403888, 'f1-score': 0.9245483883085209, 'support': 1852.0}}
0.0009	38.0	3724	0.4895	{'0': {'precision': 0.9462102689486552, 'recall': 0.9723618090452262, 'f1-score': 0.9591078066914498, 'support': 1592.0}, '1': {'precision': 0.7962962962962963, 'recall': 0.6615384615384615, 'f1-score': 0.7226890756302521, 'support': 260.0}, 'accuracy': 0.9287257019438445, 'macro avg': {'precision': 0.8712532826224757, 'recall': 0.8169501352918438, 'f1-score': 0.8408984411608509, 'support': 1852.0}, 'weighted avg': {'precision': 0.9251640308873089, 'recall': 0.9287257019438445, 'f1-score': 0.9259172720932254, 'support': 1852.0}}
0.0009	39.0	3822	0.5265	{'0': {'precision': 0.9402895054282268, 'recall': 0.9792713567839196, 'f1-score': 0.9593846153846154, 'support': 1592.0}, '1': {'precision': 0.8298969072164949, 'recall': 0.6192307692307693, 'f1-score': 0.7092511013215859, 'support': 260.0}, 'accuracy': 0.9287257019438445, 'macro avg': {'precision': 0.8850932063223609, 'recall': 0.7992510630073444, 'f1-score': 0.8343178583531006, 'support': 1852.0}, 'weighted avg': {'precision': 0.9247916244697763, 'recall': 0.9287257019438445, 'f1-score': 0.924268679285054, 'support': 1852.0}}
0.0009	40.0	3920	0.5155	{'0': {'precision': 0.9402895054282268, 'recall': 0.9792713567839196, 'f1-score': 0.9593846153846154, 'support': 1592.0}, '1': {'precision': 0.8298969072164949, 'recall': 0.6192307692307693, 'f1-score': 0.7092511013215859, 'support': 260.0}, 'accuracy': 0.9287257019438445, 'macro avg': {'precision': 0.8850932063223609, 'recall': 0.7992510630073444, 'f1-score': 0.8343178583531006, 'support': 1852.0}, 'weighted avg': {'precision': 0.9247916244697763, 'recall': 0.9287257019438445, 'f1-score': 0.924268679285054, 'support': 1852.0}}
0.001	41.0	4018	0.4915	{'0': {'precision': 0.9445460085313833, 'recall': 0.9736180904522613, 'f1-score': 0.9588617383235385, 'support': 1592.0}, '1': {'precision': 0.8009478672985783, 'recall': 0.65, 'f1-score': 0.7176220806794055, 'support': 260.0}, 'accuracy': 0.9281857451403888, 'macro avg': {'precision': 0.8727469379149808, 'recall': 0.8118090452261306, 'f1-score': 0.8382419095014719, 'support': 1852.0}, 'weighted avg': {'precision': 0.9243864422675985, 'recall': 0.9281857451403888, 'f1-score': 0.9249943997773858, 'support': 1852.0}}
0.001	42.0	4116	0.5136	{'0': {'precision': 0.941354292623942, 'recall': 0.9780150753768844, 'f1-score': 0.9593345656192237, 'support': 1592.0}, '1': {'precision': 0.8232323232323232, 'recall': 0.6269230769230769, 'f1-score': 0.7117903930131004, 'support': 260.0}, 'accuracy': 0.9287257019438445, 'macro avg': {'precision': 0.8822933079281325, 'recall': 0.8024690761499806, 'f1-score': 0.835562479316162, 'support': 1852.0}, 'weighted avg': {'precision': 0.9247712947611877, 'recall': 0.9287257019438445, 'f1-score': 0.9245821439790551, 'support': 1852.0}}
0.001	43.0	4214	0.5052	{'0': {'precision': 0.9434306569343066, 'recall': 0.9742462311557789, 'f1-score': 0.9585908529048207, 'support': 1592.0}, '1': {'precision': 0.8028846153846154, 'recall': 0.6423076923076924, 'f1-score': 0.7136752136752137, 'support': 260.0}, 'accuracy': 0.927645788336933, 'macro avg': {'precision': 0.8731576361594611, 'recall': 0.8082769617317356, 'f1-score': 0.8361330332900172, 'support': 1852.0}, 'weighted avg': {'precision': 0.9236995711875897, 'recall': 0.927645788336933, 'f1-score': 0.92420744782939, 'support': 1852.0}}
0.001	44.0	4312	0.4930	{'0': {'precision': 0.9450884685784015, 'recall': 0.9729899497487438, 'f1-score': 0.9588362735995049, 'support': 1592.0}, '1': {'precision': 0.7981220657276995, 'recall': 0.6538461538461539, 'f1-score': 0.718816067653277, 'support': 260.0}, 'accuracy': 0.9281857451403888, 'macro avg': {'precision': 0.8716052671530505, 'recall': 0.8134180517974487, 'f1-score': 0.8388261706263909, 'support': 1852.0}, 'weighted avg': {'precision': 0.9244560362127521, 'recall': 0.9281857451403888, 'f1-score': 0.9251401323759524, 'support': 1852.0}}
0.001	45.0	4410	0.4930	{'0': {'precision': 0.9451219512195121, 'recall': 0.9736180904522613, 'f1-score': 0.9591584158415841, 'support': 1592.0}, '1': {'precision': 0.8018867924528302, 'recall': 0.6538461538461539, 'f1-score': 0.7203389830508474, 'support': 260.0}, 'accuracy': 0.9287257019438445, 'macro avg': {'precision': 0.8735043718361712, 'recall': 0.8137321221492075, 'f1-score': 0.8397486994462158, 'support': 1852.0}, 'weighted avg': {'precision': 0.9250133436172782, 'recall': 0.9287257019438445, 'f1-score': 0.9256308496830573, 'support': 1852.0}}
0.0009	46.0	4508	0.4989	{'0': {'precision': 0.9440389294403893, 'recall': 0.9748743718592965, 'f1-score': 0.9592088998763906, 'support': 1592.0}, '1': {'precision': 0.8076923076923077, 'recall': 0.6461538461538462, 'f1-score': 0.717948717948718, 'support': 260.0}, 'accuracy': 0.9287257019438445, 'macro avg': {'precision': 0.8758656185663485, 'recall': 0.8105141090065713, 'f1-score': 0.8385788089125543, 'support': 1852.0}, 'weighted avg': {'precision': 0.9248973950697083, 'recall': 0.9287257019438445, 'f1-score': 0.9253386799513394, 'support': 1852.0}}
0.0009	47.0	4606	0.4990	{'0': {'precision': 0.9434650455927052, 'recall': 0.9748743718592965, 'f1-score': 0.9589125733704047, 'support': 1592.0}, '1': {'precision': 0.8067632850241546, 'recall': 0.6423076923076924, 'f1-score': 0.715203426124197, 'support': 260.0}, 'accuracy': 0.9281857451403888, 'macro avg': {'precision': 0.8751141653084299, 'recall': 0.8085910320834944, 'f1-score': 0.8370579997473009, 'support': 1852.0}, 'weighted avg': {'precision': 0.9242736537202305, 'recall': 0.9281857451403888, 'f1-score': 0.9246985462192091, 'support': 1852.0}}
0.0009	48.0	4704	0.4996	{'0': {'precision': 0.9435336976320583, 'recall': 0.9761306532663316, 'f1-score': 0.9595554183389935, 'support': 1592.0}, '1': {'precision': 0.8146341463414634, 'recall': 0.6423076923076924, 'f1-score': 0.7182795698924731, 'support': 260.0}, 'accuracy': 0.9292656587473002, 'macro avg': {'precision': 0.8790839219867608, 'recall': 0.8092191727870119, 'f1-score': 0.8389174941157334, 'support': 1852.0}, 'weighted avg': {'precision': 0.9254376483148041, 'recall': 0.9292656587473002, 'f1-score': 0.9256829990106483, 'support': 1852.0}}
0.0009	49.0	4802	0.5021	{'0': {'precision': 0.9429611650485437, 'recall': 0.9761306532663316, 'f1-score': 0.9592592592592593, 'support': 1592.0}, '1': {'precision': 0.8137254901960784, 'recall': 0.6384615384615384, 'f1-score': 0.7155172413793104, 'support': 260.0}, 'accuracy': 0.9287257019438445, 'macro avg': {'precision': 0.878343327622311, 'recall': 0.807296095863935, 'f1-score': 0.8373882503192849, 'support': 1852.0}, 'weighted avg': {'precision': 0.9248179277582407, 'recall': 0.9287257019438445, 'f1-score': 0.92504061744026, 'support': 1852.0}}
0.0009	50.0	4900	0.5042	{'0': {'precision': 0.9435336976320583, 'recall': 0.9761306532663316, 'f1-score': 0.9595554183389935, 'support': 1592.0}, '1': {'precision': 0.8146341463414634, 'recall': 0.6423076923076924, 'f1-score': 0.7182795698924731, 'support': 260.0}, 'accuracy': 0.9292656587473002, 'macro avg': {'precision': 0.8790839219867608, 'recall': 0.8092191727870119, 'f1-score': 0.8389174941157334, 'support': 1852.0}, 'weighted avg': {'precision': 0.9254376483148041, 'recall': 0.9292656587473002, 'f1-score': 0.9256829990106483, 'support': 1852.0}}

Framework versions

• Transformers 4.53.1
• Pytorch 2.6.0+cu124
• Datasets 3.5.0
• Tokenizers 0.21.1