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---
base_model: bigcode/starencoder
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: stack-edu-classifier-ruby
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-ruby
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: 0.3282
- Precision: 0.4623
- Recall: 0.3260
- F1 Macro: 0.3536
- Accuracy: 0.6657
- F1 Binary Minimum3: 0.6101
## 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 | 4.7647 | 0.0010 | 0.1667 | 0.0020 | 0.0062 | 0 |
| 0.358 | 1.4368 | 1000 | 0.3549 | 0.4093 | 0.2953 | 0.3072 | 0.6513 | 0.5882 |
| 0.3559 | 2.8736 | 2000 | 0.3425 | 0.4649 | 0.3087 | 0.3294 | 0.6571 | 0.6143 |
| 0.3534 | 4.3103 | 3000 | 0.3391 | 0.4318 | 0.3144 | 0.3349 | 0.6586 | 0.6149 |
| 0.3539 | 5.7471 | 4000 | 0.3394 | 0.4219 | 0.3244 | 0.3446 | 0.6579 | 0.6298 |
| 0.3585 | 7.1839 | 5000 | 0.3359 | 0.4756 | 0.3106 | 0.3350 | 0.6622 | 0.6069 |
| 0.3476 | 8.6207 | 6000 | 0.3339 | 0.4551 | 0.3178 | 0.3415 | 0.6638 | 0.6082 |
| 0.3496 | 10.0575 | 7000 | 0.3307 | 0.4512 | 0.3263 | 0.3505 | 0.6656 | 0.6204 |
| 0.3362 | 11.4943 | 8000 | 0.3307 | 0.4657 | 0.3228 | 0.3485 | 0.6640 | 0.6178 |
| 0.3442 | 12.9310 | 9000 | 0.3307 | 0.4771 | 0.3248 | 0.3517 | 0.6677 | 0.6095 |
| 0.344 | 14.3678 | 10000 | 0.3287 | 0.4774 | 0.3222 | 0.3496 | 0.6660 | 0.6147 |
| 0.3332 | 15.8046 | 11000 | 0.3281 | 0.4678 | 0.3240 | 0.3504 | 0.6658 | 0.6168 |
| 0.3359 | 17.2414 | 12000 | 0.3300 | 0.4658 | 0.3203 | 0.3471 | 0.6643 | 0.6100 |
| 0.3306 | 18.6782 | 13000 | 0.3282 | 0.4623 | 0.3260 | 0.3536 | 0.6657 | 0.6101 |
### Framework versions
- Transformers 4.43.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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