---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: github_issues-dataset-distilbert-base-uncased
  results: []
datasets:
- lewtun/github-issues
language:
- en
---

# github_issues-dataset-distilbert-base-uncased

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on a GitHub issues dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1495
- Accuracy: 0.9580
- F1: 0.6067
- Precision: 0.7297
- Recall: 0.5192

## Model description

[distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased)

## Intended uses & limitations

Multi Label Classification on GitHub repository issues.

## Training and evaluation data

GitHub issues dataset taken from [GitHub issues](https://huggingface.co/datasets/lewtun/github-issues).

Split the dataset into 80-20 train-test splits. Filtered out the pull requests and issues with no labels. 

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3962        | 1.0   | 114  | 0.2513          | 0.9208   | 0.34   | 0.3542    | 0.3269 |
| 0.2008        | 2.0   | 228  | 0.1847          | 0.9436   | 0.4198 | 0.5862    | 0.3269 |
| 0.1633        | 3.0   | 342  | 0.1608          | 0.9544   | 0.5581 | 0.7059    | 0.4615 |
| 0.1468        | 4.0   | 456  | 0.1519          | 0.9580   | 0.6067 | 0.7297    | 0.5192 |
| 0.1385        | 5.0   | 570  | 0.1495          | 0.9580   | 0.6067 | 0.7297    | 0.5192 |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1