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Keithulu/distilgpt2-finetuned-python-stack-clean-answers-e200

sourceHugging Faceapache-2.0updated 3y agoView on Hugging Face
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Model Card

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distilgpt2-finetuned-python-stack-clean-answers-e200

This model is a fine-tuned version of distilgpt2 on the None dataset. It achieves the following results on the evaluation set:

  • —Loss: 0.0700

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: 2e-05
  • —trainbatchsize: 8
  • —evalbatchsize: 8
  • —seed: 42
  • —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • —lrschedulertype: linear
  • —num_epochs: 200

Training results

Training LossEpochStepValidation Loss
No log1.0283.2510
No log2.0563.1681
No log3.0843.0891
No log4.01123.0233
No log5.01402.9563
No log6.01682.8967
No log7.01962.8380
No log8.02242.7777
No log9.02522.7218
No log10.02802.6671
No log11.03082.6158
No log12.03362.5594
No log13.03642.5105
No log14.03922.4551
No log15.04202.4029
No log16.04482.3500
No log17.04762.2973
3.01618.05042.2479
3.01619.05322.1940
3.01620.05602.1436
3.01621.05882.0926
3.01622.06162.0419
3.01623.06441.9912
3.01624.06721.9435
3.01625.07001.8982
3.01626.07281.8483
3.01627.07561.7974
3.01628.07841.7525
3.01629.08121.7082
3.01630.08401.6610
3.01631.08681.6108
3.01632.08961.5655
3.01633.09241.5193
3.01634.09521.4757
3.01635.09801.4342
2.241136.010081.3863
2.241137.010361.3433
2.241138.010641.3095
2.241139.010921.2757
2.241140.011201.2278
2.241141.011481.1887
2.241142.011761.1481
2.241143.012041.1193
2.241144.012321.0711
2.241145.012601.0332
2.241146.012881.0062
2.241147.013160.9696
2.241148.013440.9358
2.241149.013720.9109
2.241150.014000.8690
2.241151.014280.8420
2.241152.014560.8111
2.241153.014840.7848
1.579954.015120.7596
1.579955.015400.7361
1.579956.015680.7081
1.579957.015960.6818
1.579958.016240.6601
1.579959.016520.6351
1.579960.016800.6145
1.579961.017080.5926
1.579962.017360.5711
1.579963.017640.5492
1.579964.017920.5251
1.579965.018200.5114
1.579966.018480.4946
1.579967.018760.4758
1.579968.019040.4628
1.579969.019320.4435
1.579970.019600.4325
1.579971.019880.4168
1.086372.020160.4025
1.086373.020440.3904
1.086374.020720.3731
1.086375.021000.3606
1.086376.021280.3451
1.086377.021560.3387
1.086378.021840.3277
1.086379.022120.3160
1.086380.022400.3108
1.086381.022680.2980
1.086382.022960.2897
1.086383.023240.2814
1.086384.023520.2715
1.086385.023800.2607
1.086386.024080.2521
1.086387.024360.2482
1.086388.024640.2386
1.086389.024920.2347
0.754390.025200.2231
0.754391.025480.2205
0.754392.025760.2135
0.754393.026040.2081
0.754394.026320.2018
0.754395.026600.1956
0.754396.026880.1910
0.754397.027160.1855
0.754398.027440.1806
0.754399.027720.1768
0.7543100.028000.1715
0.7543101.028280.1687
0.7543102.028560.1649
0.7543103.028840.1629
0.7543104.029120.1570
0.7543105.029400.1563
0.7543106.029680.1502
0.7543107.029960.1486
0.5478108.030240.1443
0.5478109.030520.1408
0.5478110.030800.1389
0.5478111.031080.1366
0.5478112.031360.1338
0.5478113.031640.1304
0.5478114.031920.1290
0.5478115.032200.1264
0.5478116.032480.1234
0.5478117.032760.1212
0.5478118.033040.1197
0.5478119.033320.1185
0.5478120.033600.1159
0.5478121.033880.1130
0.5478122.034160.1125
0.5478123.034440.1106
0.5478124.034720.1087
0.4258125.035000.1077
0.4258126.035280.1068
0.4258127.035560.1048
0.4258128.035840.1039
0.4258129.036120.1022
0.4258130.036400.1002
0.4258131.036680.0987
0.4258132.036960.0980
0.4258133.037240.0973
0.4258134.037520.0955
0.4258135.037800.0951
0.4258136.038080.0937
0.4258137.038360.0932
0.4258138.038640.0920
0.4258139.038920.0908
0.4258140.039200.0903
0.4258141.039480.0889
0.4258142.039760.0883
0.3496143.040040.0879
0.3496144.040320.0872
0.3496145.040600.0865
0.3496146.040880.0852
0.3496147.041160.0849
0.3496148.041440.0843
0.3496149.041720.0836
0.3496150.042000.0832
0.3496151.042280.0822
0.3496152.042560.0817
0.3496153.042840.0813
0.3496154.043120.0805
0.3496155.043400.0799
0.3496156.043680.0796
0.3496157.043960.0789
0.3496158.044240.0784
0.3496159.044520.0781
0.3496160.044800.0777
0.3045161.045080.0776
0.3045162.045360.0771
0.3045163.045640.0762
0.3045164.045920.0762
0.3045165.046200.0763
0.3045166.046480.0758
0.3045167.046760.0754
0.3045168.047040.0750
0.3045169.047320.0748
0.3045170.047600.0746
0.3045171.047880.0742
0.3045172.048160.0740
0.3045173.048440.0735
0.3045174.048720.0735
0.3045175.049000.0732
0.3045176.049280.0728
0.3045177.049560.0724
0.3045178.049840.0723
0.2786179.050120.0721
0.2786180.050400.0719
0.2786181.050680.0717
0.2786182.050960.0715
0.2786183.051240.0714
0.2786184.051520.0713
0.2786185.051800.0712
0.2786186.052080.0710
0.2786187.052360.0707
0.2786188.052640.0705
0.2786189.052920.0704
0.2786190.053200.0704
0.2786191.053480.0704
0.2786192.053760.0702
0.2786193.054040.0703
0.2786194.054320.0702
0.2786195.054600.0702
0.2786196.054880.0701
0.2633197.055160.0701
0.2633198.055440.0701
0.2633199.055720.0700
0.2633200.056000.0700

Framework versions

  • —Transformers 4.30.2
  • —Pytorch 2.0.1+cu118
  • —Datasets 2.13.1
  • —Tokenizers 0.13.3