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---
base_model: bigcode/starencoder
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: classifier-llama3-shell-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-shell-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: 0.3628
- Precision: 0.6557
- Recall: 0.5002
- F1 Macro: 0.5093
- Accuracy: 0.6202
- F1 Binary Minimum3: 0.8655
- F1 Binary Minimum2: 0.9307
## 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 | 6.9862 | 0.0476 | 0.2 | 0.0769 | 0.2382 | 0 | 0 |
| 0.4093 | 0.2928 | 1000 | 0.4109 | 0.5009 | 0.4593 | 0.4662 | 0.5836 | 0.8543 | 0.9213 |
| 0.4015 | 0.5857 | 2000 | 0.4013 | 0.5537 | 0.4682 | 0.4762 | 0.5887 | 0.8605 | 0.9208 |
| 0.4088 | 0.8785 | 3000 | 0.3942 | 0.5063 | 0.4684 | 0.4749 | 0.5941 | 0.8566 | 0.9245 |
| 0.399 | 1.1713 | 4000 | 0.3928 | 0.5838 | 0.4809 | 0.4902 | 0.5979 | 0.8622 | 0.9245 |
| 0.3884 | 1.4641 | 5000 | 0.3934 | 0.5752 | 0.4867 | 0.4955 | 0.6011 | 0.8632 | 0.9253 |
| 0.3962 | 1.7570 | 6000 | 0.3869 | 0.6081 | 0.4831 | 0.4927 | 0.6019 | 0.8621 | 0.9259 |
| 0.393 | 2.0498 | 7000 | 0.3999 | 0.5715 | 0.4686 | 0.4776 | 0.5826 | 0.8639 | 0.9175 |
| 0.4009 | 2.3426 | 8000 | 0.3823 | 0.6038 | 0.4872 | 0.4965 | 0.6056 | 0.8603 | 0.9271 |
| 0.3898 | 2.6354 | 9000 | 0.3895 | 0.5640 | 0.4893 | 0.4986 | 0.6018 | 0.8646 | 0.9252 |
| 0.3784 | 2.9283 | 10000 | 0.3813 | 0.6028 | 0.4817 | 0.4898 | 0.6052 | 0.8587 | 0.9278 |
| 0.4001 | 3.2211 | 11000 | 0.3806 | 0.6109 | 0.4842 | 0.4934 | 0.6051 | 0.8624 | 0.9263 |
| 0.3802 | 3.5139 | 12000 | 0.3828 | 0.6116 | 0.4782 | 0.4876 | 0.5992 | 0.8623 | 0.9244 |
| 0.3939 | 3.8067 | 13000 | 0.3832 | 0.6118 | 0.4774 | 0.4871 | 0.5980 | 0.8623 | 0.9234 |
| 0.3809 | 4.0996 | 14000 | 0.3800 | 0.6068 | 0.4884 | 0.4958 | 0.6098 | 0.8573 | 0.9287 |
| 0.3857 | 4.3924 | 15000 | 0.3786 | 0.6104 | 0.4880 | 0.4979 | 0.6063 | 0.8631 | 0.9275 |
| 0.401 | 4.6852 | 16000 | 0.3770 | 0.6241 | 0.4858 | 0.4949 | 0.6076 | 0.8613 | 0.9284 |
| 0.3737 | 4.9780 | 17000 | 0.3814 | 0.5728 | 0.4897 | 0.4991 | 0.6050 | 0.8647 | 0.9262 |
| 0.3792 | 5.2709 | 18000 | 0.3828 | 0.5930 | 0.4836 | 0.4936 | 0.6008 | 0.8651 | 0.9245 |
| 0.3882 | 5.5637 | 19000 | 0.3811 | 0.5800 | 0.4877 | 0.4973 | 0.6036 | 0.8645 | 0.9259 |
| 0.3683 | 5.8565 | 20000 | 0.3777 | 0.5816 | 0.4915 | 0.5012 | 0.6080 | 0.8643 | 0.9276 |
| 0.3816 | 6.1493 | 21000 | 0.3757 | 0.6353 | 0.4862 | 0.4945 | 0.6098 | 0.8600 | 0.9291 |
| 0.3857 | 6.4422 | 22000 | 0.3751 | 0.5801 | 0.4950 | 0.5039 | 0.6120 | 0.8627 | 0.9291 |
| 0.3948 | 6.7350 | 23000 | 0.3742 | 0.6232 | 0.4909 | 0.5004 | 0.6104 | 0.8617 | 0.9285 |
| 0.3817 | 7.0278 | 24000 | 0.3754 | 0.5829 | 0.4954 | 0.5041 | 0.6119 | 0.8626 | 0.9291 |
| 0.375 | 7.3206 | 25000 | 0.3735 | 0.6155 | 0.4916 | 0.4993 | 0.6129 | 0.8611 | 0.9291 |
| 0.3773 | 7.6135 | 26000 | 0.3730 | 0.6185 | 0.4923 | 0.5014 | 0.6119 | 0.8610 | 0.9291 |
| 0.3774 | 7.9063 | 27000 | 0.3735 | 0.6334 | 0.4879 | 0.4970 | 0.6100 | 0.8625 | 0.9288 |
| 0.3788 | 8.1991 | 28000 | 0.3736 | 0.6012 | 0.4934 | 0.5028 | 0.6114 | 0.8634 | 0.9288 |
| 0.3601 | 8.4919 | 29000 | 0.3771 | 0.5904 | 0.4921 | 0.5022 | 0.6081 | 0.8641 | 0.9276 |
| 0.3779 | 8.7848 | 30000 | 0.3724 | 0.6157 | 0.4934 | 0.5019 | 0.6133 | 0.8626 | 0.9293 |
| 0.3732 | 9.0776 | 31000 | 0.3820 | 0.5773 | 0.4939 | 0.5038 | 0.6067 | 0.8656 | 0.9272 |
| 0.3729 | 9.3704 | 32000 | 0.3725 | 0.6023 | 0.4934 | 0.5030 | 0.6117 | 0.8637 | 0.9289 |
| 0.378 | 9.6633 | 33000 | 0.3742 | 0.6092 | 0.4903 | 0.5002 | 0.6089 | 0.8638 | 0.9279 |
| 0.3794 | 9.9561 | 34000 | 0.3713 | 0.6021 | 0.4971 | 0.5063 | 0.6146 | 0.8628 | 0.9297 |
| 0.3703 | 10.2489 | 35000 | 0.3718 | 0.6222 | 0.4923 | 0.5016 | 0.6121 | 0.8642 | 0.9288 |
| 0.3614 | 10.5417 | 36000 | 0.3711 | 0.6300 | 0.4930 | 0.5023 | 0.6125 | 0.8633 | 0.9289 |
| 0.3822 | 10.8346 | 37000 | 0.3729 | 0.6324 | 0.4881 | 0.4951 | 0.6127 | 0.8591 | 0.9299 |
| 0.3775 | 11.1274 | 38000 | 0.3914 | 0.5661 | 0.4887 | 0.4986 | 0.5967 | 0.8657 | 0.9234 |
| 0.3709 | 11.4202 | 39000 | 0.3741 | 0.6303 | 0.4897 | 0.4958 | 0.6138 | 0.8569 | 0.9301 |
| 0.3749 | 11.7130 | 40000 | 0.3770 | 0.5928 | 0.4919 | 0.5021 | 0.6071 | 0.8650 | 0.9269 |
| 0.3683 | 12.0059 | 41000 | 0.3707 | 0.6075 | 0.4951 | 0.5037 | 0.6141 | 0.8645 | 0.9295 |
| 0.3772 | 12.2987 | 42000 | 0.3756 | 0.5952 | 0.4942 | 0.5042 | 0.6096 | 0.8655 | 0.9279 |
| 0.3829 | 12.5915 | 43000 | 0.3705 | 0.5929 | 0.4980 | 0.5070 | 0.6147 | 0.8643 | 0.9293 |
| 0.3736 | 12.8843 | 44000 | 0.3688 | 0.6151 | 0.4971 | 0.5059 | 0.6161 | 0.8636 | 0.9300 |
| 0.3795 | 13.1772 | 45000 | 0.3704 | 0.6011 | 0.4975 | 0.5071 | 0.6141 | 0.864 | 0.9295 |
| 0.3756 | 13.4700 | 46000 | 0.3704 | 0.6181 | 0.4967 | 0.5041 | 0.6168 | 0.8621 | 0.9296 |
| 0.3711 | 13.7628 | 47000 | 0.3684 | 0.6255 | 0.4955 | 0.5038 | 0.6160 | 0.8626 | 0.9301 |
| 0.3764 | 14.0556 | 48000 | 0.3696 | 0.6068 | 0.4973 | 0.5069 | 0.6150 | 0.8643 | 0.9295 |
| 0.3666 | 14.3485 | 49000 | 0.3688 | 0.6085 | 0.4982 | 0.5071 | 0.6158 | 0.8642 | 0.9298 |
| 0.3617 | 14.6413 | 50000 | 0.3738 | 0.6002 | 0.4948 | 0.5048 | 0.6104 | 0.8657 | 0.9282 |
| 0.381 | 14.9341 | 51000 | 0.3681 | 0.6233 | 0.4978 | 0.5074 | 0.6158 | 0.8642 | 0.9299 |
| 0.3678 | 15.2269 | 52000 | 0.3674 | 0.6279 | 0.4985 | 0.5076 | 0.6172 | 0.8645 | 0.9300 |
| 0.3818 | 15.5198 | 53000 | 0.3684 | 0.6307 | 0.4966 | 0.5043 | 0.6175 | 0.8626 | 0.9297 |
| 0.3831 | 15.8126 | 54000 | 0.3674 | 0.6156 | 0.4979 | 0.5067 | 0.6166 | 0.8639 | 0.9301 |
| 0.3737 | 16.1054 | 55000 | 0.3694 | 0.5929 | 0.4994 | 0.5086 | 0.6156 | 0.8651 | 0.9300 |
| 0.3603 | 16.3982 | 56000 | 0.3675 | 0.6336 | 0.4958 | 0.5053 | 0.6161 | 0.8643 | 0.9296 |
| 0.3696 | 16.6911 | 57000 | 0.3676 | 0.6210 | 0.4972 | 0.5068 | 0.6155 | 0.8648 | 0.9296 |
| 0.3726 | 16.9839 | 58000 | 0.3671 | 0.6501 | 0.4952 | 0.5035 | 0.6169 | 0.8627 | 0.9302 |
| 0.3817 | 17.2767 | 59000 | 0.3696 | 0.6388 | 0.4924 | 0.5023 | 0.6119 | 0.8648 | 0.9282 |
| 0.3651 | 17.5695 | 60000 | 0.3660 | 0.6414 | 0.4972 | 0.5061 | 0.6183 | 0.8639 | 0.9303 |
| 0.3649 | 17.8624 | 61000 | 0.3667 | 0.6346 | 0.4984 | 0.5061 | 0.6187 | 0.8628 | 0.9297 |
| 0.3767 | 18.1552 | 62000 | 0.3658 | 0.6303 | 0.5000 | 0.5095 | 0.6186 | 0.8641 | 0.9304 |
| 0.3619 | 18.4480 | 63000 | 0.3675 | 0.6397 | 0.4933 | 0.5000 | 0.6170 | 0.8608 | 0.9302 |
| 0.3735 | 18.7408 | 64000 | 0.3708 | 0.6168 | 0.4959 | 0.5060 | 0.6122 | 0.8663 | 0.9283 |
| 0.3673 | 19.0337 | 65000 | 0.3679 | 0.6050 | 0.5000 | 0.5093 | 0.6165 | 0.8654 | 0.9298 |
| 0.3671 | 19.3265 | 66000 | 0.3671 | 0.6489 | 0.4945 | 0.5040 | 0.6148 | 0.8649 | 0.9294 |
| 0.372 | 19.6193 | 67000 | 0.3654 | 0.6490 | 0.4975 | 0.5064 | 0.6183 | 0.8650 | 0.9302 |
| 0.3711 | 19.9122 | 68000 | 0.3649 | 0.6327 | 0.4997 | 0.5085 | 0.6188 | 0.8639 | 0.9304 |
| 0.36 | 20.2050 | 69000 | 0.3648 | 0.6307 | 0.4995 | 0.5077 | 0.6190 | 0.8648 | 0.9303 |
| 0.3718 | 20.4978 | 70000 | 0.3651 | 0.6370 | 0.4985 | 0.5079 | 0.6183 | 0.8650 | 0.9304 |
| 0.3762 | 20.7906 | 71000 | 0.3656 | 0.6448 | 0.4976 | 0.5067 | 0.6172 | 0.8653 | 0.9299 |
| 0.3602 | 21.0835 | 72000 | 0.3645 | 0.6468 | 0.4976 | 0.5059 | 0.6187 | 0.8640 | 0.9303 |
| 0.3624 | 21.3763 | 73000 | 0.3679 | 0.6015 | 0.5009 | 0.5105 | 0.6165 | 0.8662 | 0.9296 |
| 0.3774 | 21.6691 | 74000 | 0.3649 | 0.6171 | 0.5005 | 0.5094 | 0.6187 | 0.8649 | 0.9304 |
| 0.3724 | 21.9619 | 75000 | 0.3646 | 0.6370 | 0.5010 | 0.5102 | 0.6194 | 0.8654 | 0.9304 |
| 0.3762 | 22.2548 | 76000 | 0.3642 | 0.6594 | 0.4977 | 0.5068 | 0.6182 | 0.8645 | 0.9300 |
| 0.3795 | 22.5476 | 77000 | 0.3674 | 0.6267 | 0.4968 | 0.5067 | 0.6151 | 0.8659 | 0.9291 |
| 0.3774 | 22.8404 | 78000 | 0.3660 | 0.6146 | 0.4995 | 0.5090 | 0.6171 | 0.8658 | 0.9297 |
| 0.351 | 23.1332 | 79000 | 0.3648 | 0.6453 | 0.4993 | 0.5087 | 0.6180 | 0.8653 | 0.9302 |
| 0.3645 | 23.4261 | 80000 | 0.3643 | 0.6314 | 0.4953 | 0.5023 | 0.6193 | 0.8629 | 0.9304 |
| 0.3664 | 23.7189 | 81000 | 0.3644 | 0.6284 | 0.5017 | 0.5101 | 0.6202 | 0.8654 | 0.9303 |
| 0.3641 | 24.0117 | 82000 | 0.3655 | 0.6297 | 0.4991 | 0.5090 | 0.6174 | 0.8655 | 0.9299 |
| 0.3588 | 24.3045 | 83000 | 0.3646 | 0.6656 | 0.4986 | 0.5084 | 0.6179 | 0.8656 | 0.9300 |
| 0.3721 | 24.5974 | 84000 | 0.3633 | 0.6598 | 0.4988 | 0.5078 | 0.6197 | 0.8643 | 0.9307 |
| 0.3644 | 24.8902 | 85000 | 0.3636 | 0.6465 | 0.4980 | 0.5053 | 0.6202 | 0.8636 | 0.9304 |
| 0.3555 | 25.1830 | 86000 | 0.3631 | 0.6518 | 0.4985 | 0.5064 | 0.6203 | 0.8638 | 0.9306 |
| 0.3607 | 25.4758 | 87000 | 0.3641 | 0.6353 | 0.4997 | 0.5091 | 0.6186 | 0.8654 | 0.9303 |
| 0.3643 | 25.7687 | 88000 | 0.3629 | 0.6528 | 0.4991 | 0.5072 | 0.6210 | 0.8645 | 0.9308 |
| 0.3715 | 26.0615 | 89000 | 0.3644 | 0.6259 | 0.5000 | 0.5092 | 0.6184 | 0.8660 | 0.9303 |
| 0.3662 | 26.3543 | 90000 | 0.3642 | 0.6294 | 0.5007 | 0.5102 | 0.6190 | 0.8656 | 0.9303 |
| 0.36 | 26.6471 | 91000 | 0.3640 | 0.6215 | 0.5011 | 0.5104 | 0.6194 | 0.8657 | 0.9305 |
| 0.3676 | 26.9400 | 92000 | 0.3632 | 0.6607 | 0.4987 | 0.5079 | 0.6192 | 0.8656 | 0.9306 |
| 0.3614 | 27.2328 | 93000 | 0.3644 | 0.6186 | 0.5002 | 0.5094 | 0.6187 | 0.8659 | 0.9304 |
| 0.3622 | 27.5256 | 94000 | 0.3627 | 0.6478 | 0.4987 | 0.5062 | 0.6210 | 0.8642 | 0.9307 |
| 0.3431 | 27.8184 | 95000 | 0.3633 | 0.6520 | 0.5003 | 0.5097 | 0.6195 | 0.8654 | 0.9305 |
| 0.3625 | 28.1113 | 96000 | 0.3628 | 0.6578 | 0.5007 | 0.5099 | 0.6199 | 0.8656 | 0.9305 |
| 0.3795 | 28.4041 | 97000 | 0.3625 | 0.6436 | 0.4978 | 0.5054 | 0.6200 | 0.8646 | 0.9308 |
| 0.3626 | 28.6969 | 98000 | 0.3638 | 0.6261 | 0.5008 | 0.5099 | 0.6193 | 0.8657 | 0.9304 |
| 0.3701 | 28.9898 | 99000 | 0.3625 | 0.6597 | 0.4995 | 0.5083 | 0.6203 | 0.8647 | 0.9308 |
| 0.3643 | 29.2826 | 100000 | 0.3630 | 0.6583 | 0.5005 | 0.5098 | 0.6199 | 0.8654 | 0.9306 |
| 0.3594 | 29.5754 | 101000 | 0.3626 | 0.6603 | 0.4997 | 0.5086 | 0.6205 | 0.8651 | 0.9308 |
| 0.3569 | 29.8682 | 102000 | 0.3628 | 0.6557 | 0.5002 | 0.5093 | 0.6202 | 0.8655 | 0.9307 |
### Framework versions
- Transformers 4.43.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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