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stack-edu-classifier-php

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

classifier-llama3-php-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: 0.3239
  • Precision: 0.4992
  • Recall: 0.3690
  • F1 Macro: 0.3961
  • Accuracy: 0.6230
  • F1 Binary Minimum3: 0.6388
  • F1 Binary Minimum2: 0.9386

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 4.9946 0.0294 0.2 0.0513 0.1471 0 0
0.3841 0.2955 1000 0.3675 0.4659 0.3109 0.3215 0.5838 0.6006 0.9302
0.3723 0.5910 2000 0.3576 0.4808 0.3176 0.3308 0.5924 0.5884 0.9318
0.3658 0.8865 3000 0.3593 0.4767 0.3245 0.3385 0.5901 0.6456 0.9307
0.3478 1.1820 4000 0.3508 0.4836 0.3289 0.3454 0.5977 0.6229 0.9327
0.3481 1.4775 5000 0.3498 0.4802 0.3316 0.3509 0.5973 0.6241 0.9323
0.3559 1.7730 6000 0.3479 0.4920 0.3262 0.3428 0.5978 0.6220 0.9325
0.3524 2.0686 7000 0.3484 0.4904 0.3310 0.3488 0.5989 0.5786 0.9337
0.3725 2.3641 8000 0.3465 0.4822 0.3322 0.3515 0.5997 0.6229 0.9326
0.3585 2.6596 9000 0.3454 0.4906 0.3338 0.3527 0.6008 0.6332 0.9332
0.3597 2.9551 10000 0.3493 0.4880 0.3279 0.3441 0.5961 0.6443 0.9316
0.348 3.2506 11000 0.3424 0.4906 0.3406 0.3611 0.6047 0.6140 0.9348
0.3573 3.5461 12000 0.3431 0.4792 0.3413 0.3613 0.6046 0.6372 0.9341
0.36 3.8416 13000 0.3477 0.4826 0.3363 0.3554 0.5962 0.6480 0.9327
0.3542 4.1371 14000 0.3421 0.4925 0.3403 0.3615 0.6045 0.5952 0.9351
0.3639 4.4326 15000 0.3414 0.4961 0.3339 0.3531 0.6029 0.6250 0.9338
0.3502 4.7281 16000 0.3406 0.4894 0.3399 0.3600 0.6047 0.6364 0.9343
0.3471 5.0236 17000 0.3392 0.4885 0.3416 0.3624 0.6061 0.6270 0.9346
0.3329 5.3191 18000 0.3410 0.4967 0.3394 0.3577 0.6062 0.5879 0.9359
0.3515 5.6147 19000 0.3387 0.4962 0.3440 0.3642 0.6087 0.6158 0.9354
0.3589 5.9102 20000 0.3397 0.4948 0.3363 0.3565 0.6033 0.6346 0.9335
0.34 6.2057 21000 0.3467 0.4957 0.3387 0.3558 0.6040 0.5632 0.9350
0.3401 6.5012 22000 0.3373 0.4937 0.3448 0.3653 0.6088 0.6132 0.9362
0.3475 6.7967 23000 0.3383 0.4882 0.3462 0.3678 0.6070 0.6411 0.9350
0.3527 7.0922 24000 0.3367 0.4965 0.3443 0.3653 0.6096 0.6080 0.9356
0.3504 7.3877 25000 0.3467 0.4845 0.3386 0.3574 0.5959 0.6589 0.9321
0.3424 7.6832 26000 0.3406 0.4888 0.3395 0.3601 0.6040 0.6384 0.9333
0.3396 7.9787 27000 0.3364 0.4971 0.3444 0.3664 0.6091 0.6122 0.9355
0.3448 8.2742 28000 0.3367 0.4884 0.3484 0.3705 0.6086 0.6438 0.9351
0.3574 8.5697 29000 0.3383 0.4852 0.3480 0.3702 0.6071 0.6506 0.9345
0.3397 8.8652 30000 0.3350 0.4943 0.3505 0.3722 0.6131 0.6247 0.9364
0.3452 9.1608 31000 0.3334 0.5022 0.3518 0.3740 0.6147 0.6212 0.9366
0.3383 9.4563 32000 0.3435 0.4979 0.3473 0.3626 0.6072 0.5652 0.9361
0.3362 9.7518 33000 0.3338 0.4904 0.3504 0.3731 0.6104 0.6353 0.9357
0.3427 10.0473 34000 0.3345 0.4992 0.3487 0.3712 0.6105 0.6439 0.9353
0.339 10.3428 35000 0.3333 0.4928 0.3589 0.3816 0.6144 0.6212 0.9373
0.3376 10.6383 36000 0.3324 0.4996 0.3511 0.3743 0.6134 0.6178 0.9364
0.3365 10.9338 37000 0.3332 0.4955 0.3521 0.3748 0.6132 0.6449 0.9359
0.3326 11.2293 38000 0.3337 0.4911 0.3528 0.3760 0.6104 0.6447 0.9359
0.3335 11.5248 39000 0.3324 0.4962 0.3579 0.3813 0.6157 0.6464 0.9367
0.3338 11.8203 40000 0.3313 0.4950 0.3562 0.3795 0.6155 0.6387 0.9368
0.3387 12.1158 41000 0.3307 0.4980 0.3594 0.3826 0.6184 0.6344 0.9373
0.3353 12.4113 42000 0.3322 0.4926 0.3578 0.3818 0.6150 0.6495 0.9365
0.3291 12.7069 43000 0.3315 0.4913 0.3582 0.3831 0.6141 0.6390 0.9367
0.3404 13.0024 44000 0.3316 0.4936 0.3563 0.3780 0.6156 0.6064 0.9377
0.3389 13.2979 45000 0.3314 0.4910 0.3582 0.3823 0.6157 0.6489 0.9365
0.334 13.5934 46000 0.3301 0.4919 0.3610 0.3857 0.6172 0.6438 0.9373
0.3406 13.8889 47000 0.3423 0.4899 0.3539 0.3765 0.6007 0.6663 0.9342
0.3385 14.1844 48000 0.3340 0.5004 0.3552 0.3754 0.6140 0.5808 0.9377
0.3398 14.4799 49000 0.3290 0.4982 0.3539 0.3772 0.6167 0.6144 0.9374
0.3278 14.7754 50000 0.3319 0.4941 0.3597 0.3810 0.6164 0.5968 0.9376
0.3389 15.0709 51000 0.3296 0.4973 0.3542 0.3791 0.6153 0.6232 0.9366
0.3381 15.3664 52000 0.3286 0.4965 0.3599 0.3832 0.6200 0.6228 0.9382
0.3318 15.6619 53000 0.3288 0.4967 0.3579 0.3824 0.6169 0.6417 0.9370
0.331 15.9574 54000 0.3314 0.4932 0.3575 0.3829 0.6129 0.6418 0.9365
0.3451 16.2530 55000 0.3316 0.4940 0.3574 0.3820 0.6130 0.6557 0.9358
0.3393 16.5485 56000 0.3308 0.4934 0.3616 0.3868 0.6145 0.6563 0.9363
0.3392 16.8440 57000 0.3287 0.4951 0.3653 0.3907 0.6177 0.6490 0.9377
0.3308 17.1395 58000 0.3271 0.4937 0.3669 0.3925 0.6199 0.6374 0.9380
0.3261 17.4350 59000 0.3311 0.4967 0.3587 0.3840 0.6138 0.6554 0.9357
0.3326 17.7305 60000 0.3283 0.4965 0.3590 0.3840 0.6170 0.6455 0.9370
0.3413 18.0260 61000 0.3273 0.4968 0.3626 0.3879 0.6192 0.6471 0.9375
0.326 18.3215 62000 0.3280 0.4913 0.3672 0.3935 0.6185 0.6471 0.9377
0.3371 18.6170 63000 0.3268 0.4989 0.3644 0.3889 0.6211 0.6185 0.9385
0.3433 18.9125 64000 0.3262 0.4990 0.3621 0.3865 0.6204 0.6264 0.9382
0.3406 19.2080 65000 0.3262 0.4961 0.3646 0.3902 0.6209 0.6402 0.9376
0.3284 19.5035 66000 0.3265 0.4959 0.3628 0.3887 0.6194 0.6379 0.9373
0.3248 19.7991 67000 0.3265 0.4975 0.3629 0.3887 0.6199 0.6316 0.9377
0.3207 20.0946 68000 0.3261 0.4963 0.3657 0.3904 0.6210 0.6180 0.9386
0.335 20.3901 69000 0.3270 0.4968 0.3662 0.3927 0.6194 0.6496 0.9376
0.3249 20.6856 70000 0.3264 0.5001 0.3618 0.3881 0.6190 0.6398 0.9375
0.336 20.9811 71000 0.3277 0.4930 0.3627 0.3862 0.6183 0.5992 0.9384
0.3422 21.2766 72000 0.3254 0.4970 0.3631 0.3884 0.6209 0.6273 0.9382
0.3344 21.5721 73000 0.3259 0.5004 0.3616 0.3879 0.6199 0.6366 0.9374
0.3229 21.8676 74000 0.3250 0.4985 0.3678 0.3937 0.6236 0.6308 0.9387
0.3287 22.1631 75000 0.3249 0.4997 0.3656 0.3913 0.6223 0.6247 0.9387
0.3395 22.4586 76000 0.3257 0.4986 0.3654 0.3923 0.6192 0.6408 0.9377
0.3143 22.7541 77000 0.3256 0.4955 0.3678 0.3942 0.6213 0.6443 0.9382
0.327 23.0496 78000 0.3270 0.4981 0.3638 0.3903 0.6178 0.6498 0.9371
0.329 23.3452 79000 0.3253 0.5005 0.3655 0.3917 0.6218 0.6223 0.9384
0.3306 23.6407 80000 0.3246 0.4972 0.3647 0.3901 0.6220 0.6317 0.9385
0.3301 23.9362 81000 0.3245 0.4977 0.3689 0.3951 0.6229 0.6369 0.9386
0.3208 24.2317 82000 0.3250 0.4969 0.3668 0.3935 0.6207 0.6415 0.9380
0.3328 24.5272 83000 0.3266 0.4954 0.3662 0.3924 0.6188 0.6535 0.9375
0.3266 24.8227 84000 0.3245 0.4995 0.3688 0.3954 0.6226 0.6397 0.9385
0.3407 25.1182 85000 0.3243 0.5003 0.3669 0.3938 0.6220 0.6314 0.9385
0.3277 25.4137 86000 0.3242 0.4993 0.3683 0.3950 0.6228 0.6350 0.9386
0.3278 25.7092 87000 0.3242 0.4980 0.3706 0.3972 0.6241 0.6377 0.9389
0.3151 26.0047 88000 0.3247 0.4958 0.3688 0.3953 0.6224 0.6429 0.9383
0.3306 26.3002 89000 0.3257 0.4970 0.3668 0.3936 0.6198 0.6489 0.9378
0.3344 26.5957 90000 0.3239 0.4999 0.3695 0.3965 0.6239 0.6308 0.9391
0.3345 26.8913 91000 0.3242 0.4995 0.3648 0.3916 0.6219 0.6333 0.9380
0.3289 27.1868 92000 0.3245 0.4969 0.3679 0.3948 0.6213 0.6409 0.9382
0.3218 27.4823 93000 0.3239 0.4986 0.3686 0.3955 0.6223 0.6335 0.9387
0.324 27.7778 94000 0.3243 0.4980 0.3670 0.3940 0.6215 0.6402 0.9381
0.3286 28.0733 95000 0.3237 0.5003 0.3691 0.3960 0.6235 0.6313 0.9389
0.3383 28.3688 96000 0.3240 0.4996 0.3683 0.3954 0.6226 0.6397 0.9384
0.3289 28.6643 97000 0.3236 0.4988 0.3695 0.3964 0.6233 0.6352 0.9388
0.3184 28.9598 98000 0.3240 0.4984 0.3687 0.3957 0.6224 0.6400 0.9385
0.329 29.2553 99000 0.3239 0.4980 0.3687 0.3957 0.6223 0.6396 0.9384
0.3301 29.5508 100000 0.3237 0.5000 0.3684 0.3953 0.6232 0.6291 0.9389
0.326 29.8463 101000 0.3239 0.4992 0.3690 0.3961 0.6230 0.6388 0.9386

Framework versions

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