---
base_model: bigcode/starencoder
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: stack-edu-classifier-c
  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-c

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: 0.4660
- Precision: 0.4766
- Recall: 0.3489
- F1 Macro: 0.3689
- Accuracy: 0.5471
- F1 Binary Minimum3: 0.7028

## 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     | 5.6344          | 0.0030    | 0.1667 | 0.0058   | 0.0178   | 0                  |
| 0.5079        | 1.4493  | 1000  | 0.5017          | 0.4119    | 0.3155 | 0.3188   | 0.5307   | 0.6809             |
| 0.5001        | 2.8986  | 2000  | 0.4960          | 0.4423    | 0.3275 | 0.3419   | 0.5208   | 0.7030             |
| 0.4739        | 4.3478  | 3000  | 0.4846          | 0.4462    | 0.3255 | 0.3353   | 0.5374   | 0.6863             |
| 0.4739        | 5.7971  | 4000  | 0.4784          | 0.4522    | 0.3223 | 0.3328   | 0.5354   | 0.6934             |
| 0.4508        | 7.2464  | 5000  | 0.4890          | 0.4492    | 0.3423 | 0.3540   | 0.5409   | 0.6770             |
| 0.4798        | 8.6957  | 6000  | 0.4746          | 0.4663    | 0.3353 | 0.3520   | 0.5308   | 0.7028             |
| 0.4613        | 10.1449 | 7000  | 0.4775          | 0.4707    | 0.3254 | 0.3405   | 0.5385   | 0.6900             |
| 0.4668        | 11.5942 | 8000  | 0.4934          | 0.4711    | 0.3347 | 0.3526   | 0.5149   | 0.7036             |
| 0.4657        | 13.0435 | 9000  | 0.4690          | 0.4797    | 0.3330 | 0.3496   | 0.5390   | 0.7002             |
| 0.4561        | 14.4928 | 10000 | 0.4676          | 0.4807    | 0.3375 | 0.3561   | 0.5398   | 0.7040             |
| 0.4597        | 15.9420 | 11000 | 0.4668          | 0.4788    | 0.3336 | 0.3497   | 0.5413   | 0.7037             |
| 0.4524        | 17.3913 | 12000 | 0.4680          | 0.4759    | 0.3353 | 0.3541   | 0.5370   | 0.7038             |
| 0.4674        | 18.8406 | 13000 | 0.4660          | 0.4766    | 0.3489 | 0.3689   | 0.5471   | 0.7028             |


### Framework versions

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