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stack-edu-classifier-typescript

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metadata
base_model: bigcode/starencoder
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - accuracy
model-index:
  - name: classifier-llama3-typescript-500k
    results: []

classifier-llama3-typescript-500k

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

  • Loss: 0.3169
  • Precision: 0.7165
  • Recall: 0.3667
  • F1 Macro: 0.4017
  • Accuracy: 0.6556
  • F1 Binary Minimum3: 0.5559
  • F1 Binary Minimum2: 0.9293

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: 0.0001
  • train_batch_size: 16
  • eval_batch_size: 256
  • seed: 0
  • distributed_type: multi-GPU
  • num_devices: 8
  • total_train_batch_size: 128
  • total_eval_batch_size: 2048
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 200
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Macro Accuracy F1 Binary Minimum3 F1 Binary Minimum2
No log 0 0 4.5438 0.0358 0.2 0.0607 0.1788 0 0
0.3468 0.2960 1000 0.3515 0.4698 0.3119 0.3243 0.6325 0.4623 0.9243
0.3432 0.5921 2000 0.3465 0.5149 0.3365 0.3559 0.6356 0.5743 0.9252
0.345 0.8881 3000 0.3374 0.5098 0.3361 0.3564 0.6431 0.5591 0.9264
0.3487 1.1841 4000 0.3350 0.5081 0.3339 0.3557 0.6438 0.5224 0.9265
0.3461 1.4802 5000 0.3331 0.5103 0.3427 0.3673 0.6455 0.5533 0.9269
0.3193 1.7762 6000 0.3339 0.5122 0.3453 0.3687 0.6449 0.5696 0.9273
0.3301 2.0722 7000 0.3312 0.5107 0.3492 0.3756 0.6472 0.5585 0.9270
0.3246 2.3683 8000 0.3411 0.5137 0.3533 0.3783 0.6396 0.5934 0.9260
0.3301 2.6643 9000 0.3362 0.5139 0.3530 0.3791 0.6438 0.5876 0.9264
0.3342 2.9603 10000 0.3306 0.5019 0.3407 0.3642 0.6462 0.5157 0.9268
0.3321 3.2564 11000 0.3287 0.5076 0.3521 0.3796 0.6481 0.5594 0.9275
0.3434 3.5524 12000 0.3368 0.4982 0.3309 0.3501 0.6418 0.4749 0.9249
0.3305 3.8484 13000 0.3297 0.5043 0.3391 0.3635 0.6467 0.5192 0.9266
0.3187 4.1445 14000 0.3274 0.5044 0.3480 0.3751 0.6483 0.5470 0.9266
0.3252 4.4405 15000 0.3323 0.5137 0.3585 0.3864 0.6449 0.5870 0.9273
0.3316 4.7365 16000 0.3275 0.5032 0.3458 0.3716 0.6485 0.5302 0.9270
0.3362 5.0326 17000 0.3305 0.4999 0.3403 0.3641 0.6452 0.5011 0.9265
0.3256 5.3286 18000 0.3257 0.5044 0.3489 0.3755 0.6496 0.5446 0.9277
0.3392 5.6246 19000 0.3291 0.4991 0.3463 0.3717 0.6474 0.5152 0.9266
0.3264 5.9207 20000 0.3259 0.5120 0.3466 0.3738 0.6493 0.5481 0.9278
0.3303 6.2167 21000 0.3251 0.5138 0.3512 0.3802 0.6496 0.5513 0.9280
0.3296 6.5127 22000 0.3286 0.4984 0.3449 0.3698 0.6471 0.5119 0.9263
0.3291 6.8088 23000 0.3324 0.5159 0.3661 0.3953 0.6461 0.5937 0.9279
0.3222 7.1048 24000 0.3245 0.5127 0.3517 0.3806 0.6506 0.5544 0.9276
0.3292 7.4008 25000 0.3251 0.5130 0.3568 0.3867 0.6505 0.5573 0.9281
0.32 7.6969 26000 0.3245 0.5117 0.3585 0.3888 0.6505 0.5614 0.9285
0.3318 7.9929 27000 0.3243 0.5097 0.3504 0.3789 0.6507 0.5360 0.9276
0.3305 8.2889 28000 0.3237 0.5109 0.3536 0.3832 0.6502 0.5494 0.9280
0.3423 8.5850 29000 0.3314 0.4979 0.3425 0.3662 0.6464 0.4955 0.9263
0.3212 8.8810 30000 0.3236 0.5155 0.3552 0.3846 0.6509 0.5628 0.9285
0.3211 9.1770 31000 0.3231 0.5130 0.3581 0.3888 0.6510 0.5587 0.9283
0.3362 9.4731 32000 0.3238 0.5080 0.3541 0.3836 0.6506 0.5315 0.9280
0.3305 9.7691 33000 0.3261 0.5054 0.3471 0.3737 0.6498 0.5115 0.9277
0.3185 10.0651 34000 0.3232 0.5152 0.3571 0.3872 0.6520 0.5640 0.9284
0.3347 10.3612 35000 0.3255 0.5044 0.3511 0.3787 0.6505 0.5154 0.9277
0.3293 10.6572 36000 0.3262 0.7152 0.3651 0.3969 0.6487 0.5816 0.9283
0.3291 10.9532 37000 0.3256 0.5181 0.3615 0.3918 0.6497 0.5804 0.9281
0.3221 11.2493 38000 0.3239 0.7123 0.3637 0.3959 0.6491 0.5714 0.9282
0.3216 11.5453 39000 0.3299 0.5013 0.3475 0.3733 0.6481 0.4941 0.9269
0.3248 11.8413 40000 0.3219 0.5122 0.3551 0.3854 0.6519 0.5367 0.9283
0.3285 12.1374 41000 0.3232 0.5056 0.3540 0.3829 0.6516 0.5265 0.9278
0.3243 12.4334 42000 0.3260 0.7169 0.3688 0.4009 0.6493 0.5867 0.9283
0.3186 12.7294 43000 0.3220 0.7092 0.3603 0.3923 0.6513 0.5507 0.9282
0.3316 13.0255 44000 0.3220 0.5121 0.3544 0.3844 0.6525 0.5347 0.9286
0.3157 13.3215 45000 0.3217 0.5100 0.3602 0.3910 0.6528 0.5548 0.9285
0.3211 13.6175 46000 0.3226 0.7178 0.3622 0.3940 0.6524 0.5755 0.9285
0.3249 13.9136 47000 0.3235 0.7053 0.3576 0.3887 0.6516 0.5287 0.9281
0.3226 14.2096 48000 0.3211 0.7134 0.3587 0.3907 0.6522 0.5586 0.9279
0.326 14.5056 49000 0.3208 0.7141 0.3632 0.3958 0.6535 0.5641 0.9284
0.3211 14.8017 50000 0.3293 0.5021 0.3460 0.3722 0.6483 0.4897 0.9271
0.3232 15.0977 51000 0.3207 0.7174 0.3632 0.3968 0.6536 0.5650 0.9290
0.3232 15.3937 52000 0.3200 0.5125 0.3592 0.3901 0.6548 0.5483 0.9291
0.3248 15.6898 53000 0.3224 0.5108 0.3540 0.3835 0.6526 0.5195 0.9287
0.3132 15.9858 54000 0.3216 0.5151 0.3634 0.3944 0.6528 0.5765 0.9287
0.3235 16.2818 55000 0.3216 0.7181 0.3698 0.4042 0.6526 0.5777 0.9289
0.3253 16.5779 56000 0.3230 0.5082 0.3527 0.3815 0.6523 0.5142 0.9283
0.3185 16.8739 57000 0.3200 0.5145 0.3576 0.3884 0.6540 0.5569 0.9285
0.3268 17.1699 58000 0.3201 0.7159 0.3691 0.4037 0.6538 0.5689 0.9291
0.3191 17.4660 59000 0.3207 0.7187 0.3696 0.4042 0.6543 0.5763 0.9288
0.318 17.7620 60000 0.3194 0.7146 0.3598 0.3922 0.6544 0.5493 0.9288
0.3049 18.0580 61000 0.3196 0.7099 0.3601 0.3931 0.6536 0.5355 0.9287
0.3298 18.3541 62000 0.3212 0.5084 0.3563 0.3864 0.6531 0.5300 0.9285
0.3257 18.6501 63000 0.3216 0.7201 0.3682 0.4025 0.6528 0.5782 0.9285
0.3277 18.9461 64000 0.3188 0.7140 0.3595 0.3920 0.6540 0.5413 0.9291
0.3187 19.2422 65000 0.3189 0.7147 0.3654 0.3999 0.6540 0.5593 0.9287
0.319 19.5382 66000 0.3204 0.5114 0.3550 0.3853 0.6534 0.5199 0.9291
0.3125 19.8342 67000 0.3198 0.5149 0.3602 0.3914 0.6553 0.5636 0.9286
0.3114 20.1303 68000 0.3185 0.5150 0.3590 0.3903 0.6550 0.5508 0.9289
0.3163 20.4263 69000 0.3187 0.7171 0.3688 0.4036 0.6550 0.5685 0.9290
0.3146 20.7223 70000 0.3184 0.7171 0.3673 0.4021 0.6556 0.5613 0.9293
0.3223 21.0184 71000 0.3203 0.5083 0.3570 0.3869 0.6538 0.5281 0.9287
0.3209 21.3144 72000 0.3187 0.7155 0.3700 0.4050 0.6551 0.5671 0.9290
0.3111 21.6104 73000 0.3182 0.7131 0.3656 0.3998 0.6552 0.5537 0.9292
0.3173 21.9065 74000 0.3187 0.7184 0.3690 0.4050 0.6547 0.5688 0.9290
0.3304 22.2025 75000 0.3181 0.7117 0.3628 0.3966 0.6550 0.5463 0.9293
0.3235 22.4985 76000 0.3212 0.7214 0.3728 0.4089 0.6542 0.5811 0.9286
0.3196 22.7946 77000 0.3179 0.7138 0.3620 0.3959 0.6550 0.5459 0.9290
0.3089 23.0906 78000 0.3193 0.7196 0.3730 0.4082 0.6553 0.5781 0.9292
0.3129 23.3866 79000 0.3227 0.6800 0.3785 0.4156 0.6514 0.5868 0.9288
0.3149 23.6827 80000 0.3178 0.7180 0.3658 0.4005 0.6561 0.5608 0.9290
0.3164 23.9787 81000 0.3179 0.7176 0.3698 0.4060 0.6557 0.5660 0.9289
0.3157 24.2747 82000 0.3195 0.7200 0.3726 0.4089 0.6551 0.5771 0.9290
0.3144 24.5708 83000 0.3183 0.7130 0.3612 0.3951 0.6547 0.5369 0.9293
0.3131 24.8668 84000 0.3179 0.7146 0.3610 0.3949 0.6553 0.5384 0.9295
0.3087 25.1628 85000 0.3172 0.7169 0.3638 0.3982 0.6559 0.5540 0.9294
0.3227 25.4589 86000 0.3177 0.7176 0.3733 0.4098 0.6558 0.5698 0.9292
0.3202 25.7549 87000 0.3176 0.7184 0.3659 0.4008 0.6555 0.5586 0.9291
0.3279 26.0509 88000 0.3176 0.7178 0.3706 0.4071 0.6557 0.5627 0.9293
0.3212 26.3470 89000 0.3175 0.7179 0.3668 0.4016 0.6554 0.5638 0.9290
0.3186 26.6430 90000 0.3172 0.7150 0.3652 0.3999 0.6559 0.5497 0.9294
0.3186 26.9390 91000 0.3171 0.7163 0.3648 0.3996 0.6556 0.5496 0.9293
0.3133 27.2351 92000 0.3185 0.7100 0.3618 0.3953 0.6549 0.5324 0.9293
0.3148 27.5311 93000 0.3176 0.7187 0.3711 0.4075 0.6561 0.5679 0.9292
0.3201 27.8271 94000 0.3170 0.7173 0.3681 0.4033 0.6558 0.5587 0.9293
0.321 28.1231 95000 0.3173 0.7141 0.3654 0.4000 0.6556 0.5476 0.9292
0.3169 28.4192 96000 0.3171 0.7177 0.3682 0.4034 0.6559 0.5597 0.9294
0.3231 28.7152 97000 0.3169 0.7154 0.3651 0.3998 0.6556 0.5523 0.9293
0.3181 29.0112 98000 0.3169 0.7164 0.3672 0.4022 0.6556 0.5572 0.9293
0.3261 29.3073 99000 0.3173 0.7181 0.3700 0.4063 0.6560 0.5659 0.9291
0.3181 29.6033 100000 0.3170 0.7177 0.3695 0.4058 0.6558 0.5615 0.9292
0.3149 29.8993 101000 0.3169 0.7165 0.3667 0.4017 0.6556 0.5559 0.9293

Framework versions

  • Transformers 4.43.4
  • Pytorch 2.4.0+cu121
  • Datasets 2.21.0
  • Tokenizers 0.19.1