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stack-edu-classifier-swift

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

classifier-llama3-swift-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: 0.3403
  • Precision: 0.6596
  • Recall: 0.3962
  • F1 Macro: 0.4436
  • Accuracy: 0.6112
  • F1 Binary Minimum3: 0.7282
  • F1 Binary Minimum2: 0.9673

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 7.9458 0.0159 0.2 0.0294 0.0794 0 0
0.3909 0.2932 1000 0.3869 0.6080 0.3401 0.3645 0.5781 0.7198 0.9645
0.3743 0.5863 2000 0.3686 0.6224 0.3523 0.3809 0.5910 0.7154 0.9657
0.3639 0.8795 3000 0.3666 0.6145 0.3529 0.3809 0.5920 0.7273 0.9657
0.3655 1.1727 4000 0.3594 0.6360 0.3623 0.3946 0.5962 0.7145 0.9661
0.3702 1.4658 5000 0.3585 0.6124 0.3566 0.3856 0.5960 0.7031 0.9663
0.3625 1.7590 6000 0.3558 0.6531 0.3717 0.4082 0.5999 0.7191 0.9665
0.3474 2.0522 7000 0.3542 0.6420 0.3696 0.4033 0.6004 0.7173 0.9666
0.3711 2.3454 8000 0.3536 0.6375 0.3618 0.3944 0.5999 0.7107 0.9665
0.3495 2.6385 9000 0.3522 0.6454 0.3736 0.4109 0.6021 0.7248 0.9667
0.3576 2.9317 10000 0.3536 0.6591 0.3664 0.4012 0.6015 0.7305 0.9666
0.3616 3.2249 11000 0.3558 0.6652 0.3813 0.4223 0.6011 0.7362 0.9666
0.3482 3.5180 12000 0.3504 0.6589 0.3773 0.4163 0.6046 0.7259 0.9671
0.3616 3.8112 13000 0.3550 0.6670 0.3889 0.4326 0.6036 0.7384 0.9666
0.36 4.1044 14000 0.3515 0.6646 0.3844 0.4262 0.6049 0.7346 0.9671
0.3553 4.3975 15000 0.3519 0.6779 0.3887 0.4336 0.6058 0.7369 0.9668
0.3457 4.6907 16000 0.3485 0.6668 0.3852 0.4275 0.6049 0.7199 0.9671
0.3544 4.9839 17000 0.3530 0.6510 0.3703 0.4043 0.5980 0.6881 0.9669
0.349 5.2770 18000 0.3472 0.6650 0.3785 0.4184 0.6049 0.7189 0.9671
0.3478 5.5702 19000 0.3479 0.6629 0.3709 0.4079 0.6038 0.7097 0.9669
0.3517 5.8634 20000 0.3470 0.6630 0.3830 0.4265 0.6051 0.7154 0.9669
0.3552 6.1566 21000 0.3475 0.6536 0.3767 0.4165 0.6041 0.7113 0.9667
0.3619 6.4497 22000 0.3472 0.6705 0.3739 0.4127 0.6071 0.7275 0.9667
0.3534 6.7429 23000 0.3626 0.6590 0.3978 0.4435 0.5977 0.7434 0.9667
0.3494 7.0361 24000 0.3479 0.6711 0.3905 0.4363 0.6078 0.7312 0.9668
0.3488 7.3292 25000 0.3455 0.6647 0.3836 0.4251 0.6075 0.7258 0.9671
0.3557 7.6224 26000 0.3457 0.6699 0.3890 0.4328 0.6066 0.7193 0.9671
0.3442 7.9156 27000 0.3461 0.6622 0.3726 0.4111 0.6060 0.7214 0.9671
0.3648 8.2087 28000 0.3463 0.6742 0.3834 0.4260 0.6087 0.7310 0.9667
0.35 8.5019 29000 0.3463 0.6577 0.3966 0.4434 0.6086 0.7317 0.9674
0.3539 8.7951 30000 0.3526 0.6478 0.3654 0.3975 0.5977 0.6838 0.9669
0.3475 9.0882 31000 0.3454 0.6761 0.3898 0.4352 0.6090 0.7326 0.9670
0.3535 9.3814 32000 0.3447 0.6719 0.3895 0.4346 0.6092 0.7291 0.9671
0.3514 9.6746 33000 0.3445 0.6660 0.3918 0.4380 0.6088 0.7281 0.9671
0.3504 9.9678 34000 0.3455 0.6698 0.3914 0.4363 0.6094 0.7349 0.9672
0.3629 10.2609 35000 0.3550 0.6620 0.3935 0.4389 0.6036 0.7441 0.9666
0.3436 10.5541 36000 0.3491 0.6607 0.3959 0.4422 0.6074 0.7403 0.9672
0.3516 10.8473 37000 0.3461 0.6641 0.3792 0.4166 0.6051 0.7062 0.9670
0.3483 11.1404 38000 0.3435 0.6705 0.3894 0.4348 0.6071 0.7214 0.9671
0.3506 11.4336 39000 0.3450 0.6595 0.3969 0.4447 0.6101 0.7344 0.9672
0.3484 11.7268 40000 0.3440 0.6641 0.3934 0.4380 0.6086 0.7273 0.9674
0.3491 12.0199 41000 0.3589 0.6601 0.3959 0.4408 0.6001 0.7454 0.9668
0.3468 12.3131 42000 0.3456 0.6731 0.3895 0.4345 0.6087 0.7371 0.9674
0.3461 12.6063 43000 0.3431 0.6730 0.3897 0.4357 0.6096 0.7274 0.9675
0.355 12.8994 44000 0.3440 0.6672 0.3812 0.4221 0.6069 0.7141 0.9672
0.347 13.1926 45000 0.3469 0.6574 0.3804 0.4180 0.6039 0.7030 0.9669
0.3453 13.4858 46000 0.3443 0.6736 0.3882 0.4326 0.6102 0.7350 0.9669
0.3463 13.7790 47000 0.3428 0.6640 0.3877 0.4308 0.6085 0.7186 0.9673
0.3485 14.0721 48000 0.3433 0.6667 0.3965 0.4438 0.6097 0.7308 0.9673
0.3539 14.3653 49000 0.3429 0.6570 0.3900 0.4343 0.6079 0.7214 0.9672
0.3541 14.6585 50000 0.3487 0.6623 0.3950 0.4412 0.6066 0.7417 0.9671
0.3523 14.9516 51000 0.3455 0.6512 0.3869 0.4283 0.6048 0.7027 0.9668
0.3372 15.2448 52000 0.3453 0.6706 0.3975 0.4449 0.6089 0.7375 0.9673
0.3522 15.5380 53000 0.3428 0.6680 0.3964 0.4441 0.6112 0.7326 0.9672
0.3457 15.8311 54000 0.3441 0.6738 0.3914 0.4382 0.6099 0.7363 0.9668
0.3403 16.1243 55000 0.3443 0.6643 0.3958 0.4432 0.6097 0.7364 0.9672
0.3448 16.4175 56000 0.3436 0.6703 0.3819 0.4262 0.6066 0.7182 0.9667
0.3316 16.7106 57000 0.3419 0.6640 0.3941 0.4405 0.6092 0.7223 0.9672
0.3436 17.0038 58000 0.3496 0.6626 0.4037 0.4526 0.6073 0.7431 0.9672
0.3507 17.2970 59000 0.3478 0.6624 0.4038 0.4530 0.6093 0.7424 0.9670
0.3407 17.5901 60000 0.3416 0.6654 0.3921 0.4380 0.6099 0.7268 0.9672
0.3483 17.8833 61000 0.3427 0.6679 0.3878 0.4317 0.6100 0.7336 0.9671
0.3436 18.1765 62000 0.3436 0.6702 0.3910 0.4377 0.6099 0.7340 0.9668
0.3474 18.4697 63000 0.3473 0.6560 0.4018 0.4501 0.6102 0.7429 0.9669
0.3429 18.7628 64000 0.3416 0.6609 0.3927 0.4375 0.6096 0.7244 0.9673
0.3533 19.0560 65000 0.3417 0.6696 0.3896 0.4348 0.6104 0.7284 0.9673
0.3375 19.3492 66000 0.3419 0.6621 0.3924 0.4377 0.6084 0.7154 0.9672
0.3428 19.6423 67000 0.3415 0.6700 0.3900 0.4357 0.6107 0.7301 0.9673
0.35 19.9355 68000 0.3424 0.6553 0.3817 0.4225 0.6074 0.7131 0.9673
0.3499 20.2287 69000 0.3478 0.6581 0.4031 0.4515 0.6090 0.7430 0.9674
0.3429 20.5218 70000 0.3567 0.6619 0.3969 0.4427 0.6030 0.7470 0.9661
0.3342 20.8150 71000 0.3415 0.6677 0.3920 0.4388 0.6107 0.7302 0.9674
0.3381 21.1082 72000 0.3482 0.6587 0.4022 0.4509 0.6078 0.7428 0.9668
0.3524 21.4013 73000 0.3439 0.6600 0.4036 0.4533 0.6116 0.7395 0.9672
0.3428 21.6945 74000 0.3423 0.6564 0.3889 0.4329 0.6074 0.7111 0.9671
0.3447 21.9877 75000 0.3416 0.6644 0.3929 0.4397 0.6108 0.7321 0.9671
0.3436 22.2809 76000 0.3412 0.6552 0.4009 0.4488 0.6098 0.7265 0.9674
0.3484 22.5740 77000 0.3430 0.6585 0.3976 0.4459 0.6110 0.7365 0.9670
0.3401 22.8672 78000 0.3423 0.6594 0.3920 0.4378 0.6080 0.7101 0.9671
0.3394 23.1604 79000 0.3416 0.6606 0.3901 0.4349 0.6088 0.7145 0.9674
0.3331 23.4535 80000 0.3413 0.6627 0.3844 0.4273 0.6098 0.7214 0.9672
0.3587 23.7467 81000 0.3413 0.6593 0.3904 0.4351 0.6087 0.7157 0.9673
0.3518 24.0399 82000 0.3419 0.6641 0.3962 0.4437 0.6112 0.7352 0.9672
0.3442 24.3330 83000 0.3406 0.6626 0.3959 0.4432 0.6105 0.7277 0.9673
0.3386 24.6262 84000 0.3452 0.6607 0.3984 0.4464 0.6100 0.7409 0.9669
0.3418 24.9194 85000 0.3409 0.6655 0.3919 0.4385 0.6098 0.7195 0.9673
0.3426 25.2125 86000 0.3420 0.6513 0.4021 0.4514 0.6109 0.7351 0.9672
0.3293 25.5057 87000 0.3406 0.6714 0.3926 0.4397 0.6101 0.7289 0.9672
0.3437 25.7989 88000 0.3413 0.6506 0.4038 0.4529 0.6104 0.7303 0.9673
0.341 26.0921 89000 0.3407 0.6663 0.3965 0.4449 0.6108 0.7298 0.9673
0.3284 26.3852 90000 0.3407 0.6615 0.3958 0.4435 0.6103 0.7293 0.9673
0.3463 26.6784 91000 0.3408 0.6625 0.3916 0.4375 0.6098 0.7180 0.9673
0.3423 26.9716 92000 0.3406 0.6652 0.3927 0.4393 0.6104 0.7222 0.9672
0.3408 27.2647 93000 0.3405 0.6669 0.3925 0.4393 0.6100 0.7274 0.9672
0.3431 27.5579 94000 0.3409 0.6686 0.3921 0.4392 0.6102 0.7305 0.9672
0.3424 27.8511 95000 0.3409 0.6664 0.3964 0.4445 0.6105 0.7318 0.9672
0.3438 28.1442 96000 0.3405 0.6650 0.3942 0.4414 0.6109 0.7290 0.9672
0.3328 28.4374 97000 0.3404 0.6640 0.3934 0.4401 0.6101 0.7220 0.9673
0.3341 28.7306 98000 0.3405 0.6602 0.3951 0.4425 0.6107 0.7288 0.9673
0.3373 29.0237 99000 0.3403 0.6602 0.3957 0.4428 0.6110 0.7243 0.9673
0.3377 29.3169 100000 0.3404 0.6614 0.3948 0.4422 0.6104 0.7278 0.9672
0.3376 29.6101 101000 0.3404 0.6622 0.3962 0.4437 0.6110 0.7288 0.9673
0.3404 29.9033 102000 0.3403 0.6596 0.3962 0.4436 0.6112 0.7282 0.9673

Framework versions

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