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

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: 0.4309
- Precision: 0.4200
- Recall: 0.3245
- F1 Macro: 0.3364
- Accuracy: 0.5715
- F1 Binary Minimum3: 0.6938

## 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     | 6.3938          | 0.0009    | 0.1667 | 0.0018   | 0.0054   | 0                  |
| 0.4784        | 1.4535  | 1000  | 0.4524          | 0.4359    | 0.3052 | 0.3115   | 0.5596   | 0.6790             |
| 0.4553        | 2.9070  | 2000  | 0.4622          | 0.4179    | 0.3081 | 0.3193   | 0.5299   | 0.7012             |
| 0.4397        | 4.3605  | 3000  | 0.4428          | 0.4256    | 0.3126 | 0.3225   | 0.5646   | 0.6890             |
| 0.4463        | 5.8140  | 4000  | 0.4417          | 0.4252    | 0.3155 | 0.3242   | 0.5667   | 0.6850             |
| 0.4305        | 7.2674  | 5000  | 0.4419          | 0.4416    | 0.3232 | 0.3397   | 0.5488   | 0.7001             |
| 0.4499        | 8.7209  | 6000  | 0.4361          | 0.4250    | 0.3185 | 0.3282   | 0.5682   | 0.6878             |
| 0.4339        | 10.1744 | 7000  | 0.4351          | 0.4452    | 0.3258 | 0.3384   | 0.5711   | 0.6884             |
| 0.449         | 11.6279 | 8000  | 0.4386          | 0.4217    | 0.3180 | 0.3291   | 0.5718   | 0.6782             |
| 0.425         | 13.0814 | 9000  | 0.4360          | 0.4224    | 0.3213 | 0.3323   | 0.5737   | 0.6828             |
| 0.4434        | 14.5349 | 10000 | 0.4328          | 0.4376    | 0.3280 | 0.3436   | 0.5626   | 0.6957             |
| 0.4396        | 15.9884 | 11000 | 0.4347          | 0.4170    | 0.3243 | 0.3384   | 0.5576   | 0.6994             |
| 0.4207        | 17.4419 | 12000 | 0.4326          | 0.4181    | 0.3233 | 0.3365   | 0.5606   | 0.6996             |
| 0.4334        | 18.8953 | 13000 | 0.4309          | 0.4200    | 0.3245 | 0.3364   | 0.5715   | 0.6938             |


### Framework versions

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