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---
base_model: bigcode/starencoder
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: classifier-llama3-php-500k
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# classifier-llama3-php-500k
This model is a fine-tuned version of [bigcode/starencoder](https://huggingface.co/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
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