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---
base_model: bigcode/starencoder
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: stack-edu-classifier-go
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. -->
# stack-edu-classifier-go
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: 0.3999
- Precision: 0.5624
- Recall: 0.2985
- F1 Macro: 0.3294
- Accuracy: 0.5640
- F1 Binary Minimum3: 0.6539
## 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: 0.0003
- train_batch_size: 64
- eval_batch_size: 256
- seed: 0
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 128
- total_eval_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 Macro | Accuracy | F1 Binary Minimum3 |
|:-------------:|:-------:|:-----:|:---------------:|:---------:|:------:|:--------:|:--------:|:------------------:|
| No log | 0 | 0 | 4.5815 | 0.0004 | 0.1667 | 0.0007 | 0.0022 | 0 |
| 0.4211 | 1.4451 | 1000 | 0.4201 | 0.4406 | 0.2753 | 0.2931 | 0.5507 | 0.6478 |
| 0.4039 | 2.8902 | 2000 | 0.4134 | 0.3772 | 0.2738 | 0.2876 | 0.5521 | 0.6465 |
| 0.405 | 4.3353 | 3000 | 0.4097 | 0.4386 | 0.2797 | 0.2979 | 0.5642 | 0.6287 |
| 0.3997 | 5.7803 | 4000 | 0.4122 | 0.4282 | 0.2856 | 0.3020 | 0.5675 | 0.6163 |
| 0.4135 | 7.2254 | 5000 | 0.4058 | 0.5497 | 0.2862 | 0.3084 | 0.5673 | 0.6401 |
| 0.3915 | 8.6705 | 6000 | 0.4051 | 0.5559 | 0.2911 | 0.3162 | 0.5594 | 0.6525 |
| 0.3873 | 10.1156 | 7000 | 0.4035 | 0.5652 | 0.2868 | 0.3113 | 0.5618 | 0.6511 |
| 0.3981 | 11.5607 | 8000 | 0.4020 | 0.5510 | 0.2890 | 0.3108 | 0.5693 | 0.6414 |
| 0.3855 | 13.0058 | 9000 | 0.4074 | 0.5470 | 0.2879 | 0.3104 | 0.5708 | 0.6192 |
| 0.3863 | 14.4509 | 10000 | 0.4003 | 0.5196 | 0.3047 | 0.3370 | 0.5664 | 0.6534 |
| 0.3784 | 15.8960 | 11000 | 0.4007 | 0.5616 | 0.2919 | 0.3179 | 0.5622 | 0.6516 |
| 0.3982 | 17.3410 | 12000 | 0.4034 | 0.5658 | 0.2910 | 0.3159 | 0.5595 | 0.6635 |
| 0.4013 | 18.7861 | 13000 | 0.3999 | 0.5624 | 0.2985 | 0.3294 | 0.5640 | 0.6539 |
### Framework versions
- Transformers 4.43.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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