---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: results_classification
  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. -->

# results_classification

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2517
- Accuracy: 0.9214
- F1: 0.9214

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 500

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| 0.152         | 0.0133 | 50   | 0.3216          | 0.9037   | 0.9033 |
| 0.1533        | 0.0267 | 100  | 0.3024          | 0.9096   | 0.9095 |
| 0.1443        | 0.04   | 150  | 0.3356          | 0.9017   | 0.9010 |
| 0.1101        | 0.0533 | 200  | 0.3121          | 0.9134   | 0.9133 |
| 0.1147        | 0.0667 | 250  | 0.3813          | 0.9005   | 0.9002 |
| 0.1611        | 0.08   | 300  | 0.2992          | 0.9134   | 0.9129 |
| 0.1553        | 0.0933 | 350  | 0.2858          | 0.9166   | 0.9166 |
| 0.1268        | 0.1067 | 400  | 0.2769          | 0.9186   | 0.9185 |
| 0.2011        | 0.12   | 450  | 0.2525          | 0.9214   | 0.9215 |
| 0.1845        | 0.1333 | 500  | 0.2517          | 0.9214   | 0.9214 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1