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

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: 0.3612
- Precision: 0.5135
- Recall: 0.3322
- F1 Macro: 0.3711
- Accuracy: 0.6277
- F1 Binary Minimum3: 0.5704

## 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     | 5.6298          | 0.0010    | 0.1667 | 0.0020   | 0.0059   | 0                  |
| 0.3853        | 1.4493  | 1000  | 0.3886          | 0.4945    | 0.3110 | 0.3354   | 0.6037   | 0.5761             |
| 0.3791        | 2.8986  | 2000  | 0.3729          | 0.5041    | 0.3090 | 0.3395   | 0.6208   | 0.5716             |
| 0.3722        | 4.3478  | 3000  | 0.3720          | 0.5261    | 0.3116 | 0.3440   | 0.6189   | 0.5673             |
| 0.3751        | 5.7971  | 4000  | 0.3704          | 0.5247    | 0.3204 | 0.3565   | 0.6199   | 0.5766             |
| 0.3651        | 7.2464  | 5000  | 0.3718          | 0.5113    | 0.3352 | 0.3678   | 0.6310   | 0.5161             |
| 0.3695        | 8.6957  | 6000  | 0.3649          | 0.5055    | 0.3253 | 0.3607   | 0.6249   | 0.5632             |
| 0.361         | 10.1449 | 7000  | 0.3647          | 0.5042    | 0.3236 | 0.3571   | 0.6354   | 0.5410             |
| 0.3666        | 11.5942 | 8000  | 0.3764          | 0.5290    | 0.3371 | 0.3752   | 0.6146   | 0.5941             |
| 0.3563        | 13.0435 | 9000  | 0.3617          | 0.5179    | 0.3356 | 0.3743   | 0.6303   | 0.5674             |
| 0.3735        | 14.4928 | 10000 | 0.3663          | 0.4998    | 0.3423 | 0.3760   | 0.6340   | 0.5320             |
| 0.349         | 15.9420 | 11000 | 0.3616          | 0.5063    | 0.3306 | 0.3681   | 0.6273   | 0.5696             |
| 0.3679        | 17.3913 | 12000 | 0.3632          | 0.5078    | 0.3396 | 0.3786   | 0.6252   | 0.5762             |
| 0.3622        | 18.8406 | 13000 | 0.3612          | 0.5135    | 0.3322 | 0.3711   | 0.6277   | 0.5704             |


### Framework versions

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