---
base_model: distilbert-base-uncased
license: apache-2.0
metrics:
- accuracy
- precision
- recall
- f1
tags:
- generated_from_trainer
model-index:
- name: distilbert
  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

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: 0.1400
- Accuracy: 0.973
- Precision: 0.974
- Recall: 0.973
- F1: 0.973

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1    |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:-----:|
| 2.7987        | 1.0   | 114  | 2.0513          | 0.527    | 0.53      | 0.527  | 0.449 |
| 1.3683        | 2.0   | 228  | 0.4828          | 0.955    | 0.959     | 0.955  | 0.955 |
| 0.4676        | 3.0   | 342  | 0.2051          | 0.949    | 0.936     | 0.949  | 0.94  |
| 0.2473        | 4.0   | 456  | 0.1503          | 0.971    | 0.973     | 0.971  | 0.971 |
| 0.1912        | 5.0   | 570  | 0.1231          | 0.973    | 0.974     | 0.973  | 0.973 |
| 0.1413        | 6.0   | 684  | 0.1538          | 0.971    | 0.972     | 0.971  | 0.971 |
| 0.1289        | 7.0   | 798  | 0.1197          | 0.976    | 0.977     | 0.976  | 0.976 |
| 0.0951        | 8.0   | 912  | 0.1246          | 0.978    | 0.979     | 0.978  | 0.978 |
| 0.0686        | 9.0   | 1026 | 0.1397          | 0.973    | 0.974     | 0.973  | 0.973 |
| 0.0518        | 10.0  | 1140 | 0.1400          | 0.973    | 0.974     | 0.973  | 0.973 |


### Framework versions

- Transformers 4.43.3
- Pytorch 2.1.0+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1