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

# distilbert-finetuning

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7899
- Accuracy: 0.8078
- F1: 0.8069

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:|
| 0.6237        | 1.0   | 65741  | 0.8881          | 0.7796   | 0.7739 |
| 1.3107        | 2.0   | 131482 | 0.8164          | 0.7995   | 0.7953 |
| 0.8264        | 3.0   | 197223 | 0.7899          | 0.8078   | 0.8069 |
| 0.4175        | 4.0   | 262964 | 0.8556          | 0.8130   | 0.8116 |
| 0.3076        | 5.0   | 328705 | 0.9789          | 0.8127   | 0.8116 |


### Framework versions

- Transformers 4.43.3
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1