Textual Hate Content Moderation with BERTweet + LoRA Table

Medoxz543's workspace

Runs

calibration/best_threshold

calibration/best_val_mcc

eval/accuracy

eval/f1

eval/f1_class_0

eval/f1_class_1

eval/loss

eval/matthews_corrcoef

eval/precision

eval/precision_class_0

eval/precision_class_1

eval/recall

eval/recall_class_0

eval/recall_class_1

eval/runtime

eval/samples_per_second

eval/steps_per_second

hp/lora_alpha

hp/lora_r

hp/lora_ratio

test/accuracy

test/accuracy_thresh_0.5

test/calibrated_accuracy

test/calibrated_mcc

test/f1

test/f1_class_0

test/f1_class_1

1h 24m 24s

Finished

200

true

200

matthews_corrcoef

136673284

["attention.output.dense","key","query","value"]

200

0.67167

0.80359

0.63311

0.45114

0.67167

31.6876

946.742

3.724

0.839

0.8755

0.77221

2h 15m 13s

Finished

200

true

200

matthews_corrcoef

136673284

["attention.output.dense","query","key","value"]

200

0.8545

0.88858

0.7904

0.30978

0.6818

0.86069

0.91485

0.74989

0.8545

0.86377

0.83553

32.5201

922.507

3.629

0.8592

0.89272

0.79523

1h 42m 50s

Finished

200

true

200

matthews_corrcoef

136673284

["key","attention.output.dense","query","value"]

200

0.863

0.89862

0.7888

0.30842

0.68755

0.86224

0.89333

0.79865

0.863

0.90397

0.77919

33.1859

903.999

3.556

0.8647

0.90039

0.78915

1h 45s

Finished

200

true

200

matthews_corrcoef

136673284

["value","query","key","attention.output.dense"]

200

0.67167

0.80359

0.63308

0.45114

0.67167

35.3904

847.687

3.334

0.8459

0.88979

0.74389

1h 57m 24s

Finished

200

true

200

matthews_corrcoef

136673284

["key","value","query","attention.output.dense"]

200

0.85567

0.88991

0.7905

0.31032

0.68252

0.86074

0.91237

0.75513

0.85567

0.86854

0.82934

39.2156

765.001

3.009

0.8585

0.89291

0.79151

1m 55s

Finished

200

true

200

matthews_corrcoef

126418948

["query","key","value","attention.output.dense"]

200

2h 31m 46s

Finished

200

true

200

matthews_corrcoef

136673284

["query","attention.output.dense","key","value"]

200

0.84553

0.84718

0.88231

0.77533

0.33045

0.65954

0.85052

0.90356

0.74202

0.84553

0.86203

0.81178

36.0295

832.651

3.275

0.8496

0.85093

0.88592

0.77934

17s

Finished

1h 35m 41s

Finished

200

true

200

matthews_corrcoef

136673284

["query","key","value","attention.output.dense"]

200

0.84997

0.84516

0.89349

0.74629

0.33588

0.64957

0.84899

0.85391

0.83893

0.84997

0.93692

0.67208

33.8701

885.738

3.484

2h 41m 45s

Finished

200

true

200

matthews_corrcoef

136673284

["attention.output.dense","value","query","key"]

200

0.8544

0.8496

0.89677

0.75305

0.32935

0.66019

0.85395

0.85618

0.84938

0.8544

0.9414

0.67635

30.9069

970.656

3.818

0.8513

0.84621

0.89473

0.74689

2h 34m 48s

Finished

300

true

300

matthews_corrcoef

136673284

["attention.output.dense","key","value","query"]

300

0.67177

0.53987

0.80366

0.63299

0.45127

0.67177

30.7547

975.46

3.837

0.6718

0.53992

0.80368

56m 26s

Finished

100

true

100

matthews_corrcoef

136673284

["query","value","attention.output.dense","key"]

100

0.8528

0.85182

0.89173

0.77014

0.33488

0.66243

0.85133

0.88132

0.78996

0.8528

0.9024

0.75129

36.906

812.876

3.197

0.8516

0.85038

0.89114

0.76696

1h 18m 6s

Finished

100

true

100

matthews_corrcoef

136673284

["query","value","key","attention.output.dense"]

100

0.55

0.66534

0.8529

0.85204

0.89167

0.77093

0.33196

0.66303

0.85156

0.88237

0.78849

0.8529

0.90116

0.75414

37.7251

795.226

3.128

0.8541

0.8552

0.66421

0.85308

0.89276

0.77185

32m 12s

Finished

Fourth Sweep

300

false

300

136378372

["key","value","query"]

500

["query","key","value"]

0.8542

0.85344

0.89251

0.77346

0.32855

0.66632

0.85298

0.88417

0.78916

0.8542

0.90101

0.75838

10.7528

929.992

3.72

0.8542

0.85344

30m 13s

Finished

Fourth Sweep

300

false

300

136083460

["key","query"]

500

["query","key"]

0.8496

0.8489

0.889

0.76682

0.3384

0.6561

0.84845

0.88159

0.78062

0.8496

0.89655

0.7535

10.2896

971.856

3.887

1.5

0.8496

0.8489

32m 21s

Finished

Fourth Sweep

300

false

300

137263108

["key","value","query"]

500

["query","key","value"]

0.8461

0.84541

0.8864

0.76151

0.34438

0.64816

0.84494

0.87919

0.77483

0.8461

0.89372

0.74863

10.8977

917.623

3.67

0.125

0.8461

0.84541

30m 22s

Finished

Fourth Sweep

300

false

300

137852932

["key","query"]

500

["query","key"]

0.8465

0.84581

0.88669

0.76213

0.342

0.64908

0.84535

0.87948

0.77547

0.8465

0.89402

0.74924

10.3257

968.459

3.874

0.25

0.8465

0.84581

32m 17s

Finished

Fourth Sweep

300

false

300

136378372

["key","value","query"]

500

["query","key","value"]

0.8515

0.85096

0.89022

0.77059

0.33123

0.66098

0.85057

0.88427

0.78157

0.8515

0.89625

0.7599

10.806

925.413

3.702

0.5

0.8515

0.85096

34m 54s

Finished

Fourth Sweep

300

false

300

136673284

["attention.output.dense","key","value","query"]

500

["query","key","value","attention.output.dense"]

0.8562

0.85564

0.89373

0.77767

0.32371

0.6716

0.85526

0.88744

0.78939

0.8562

0.90012

0.7663

11.2851

886.126

3.545

0.75

0.8562

0.85564

34m 40s

Finished

Fourth Sweep

300

false

300

136673284

["attention.output.dense","key","value","query"]

500

["query","key","value","attention.output.dense"]

0.8479

0.84721

0.88772

0.7643

0.33899

0.65228

0.84676

0.88051

0.77767

0.8479

0.89506

0.75137

11.4621

872.437

3.49

0.25

0.8479

0.84721

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