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

# model_ckpt

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: 0.0091
- Accuracy: 0.9989
- F1: 0.9989
- Precision: 0.9989
- Recall: 0.9989

## 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: 116
- eval_batch_size: 116
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3936        | 1.0   | 45   | 0.0168          | 1.0      | 1.0    | 1.0       | 1.0    |
| 0.0187        | 2.0   | 90   | 0.0047          | 0.9989   | 0.9989 | 0.9989    | 0.9989 |
| 0.0049        | 3.0   | 135  | 0.0345          | 0.9945   | 0.9945 | 0.9945    | 0.9945 |
| 0.0035        | 4.0   | 180  | 0.0105          | 0.9978   | 0.9978 | 0.9978    | 0.9978 |
| 0.0015        | 5.0   | 225  | 0.0075          | 0.9989   | 0.9989 | 0.9989    | 0.9989 |
| 0.0009        | 6.0   | 270  | 0.0077          | 0.9989   | 0.9989 | 0.9989    | 0.9989 |
| 0.0007        | 7.0   | 315  | 0.0078          | 0.9989   | 0.9989 | 0.9989    | 0.9989 |
| 0.0006        | 8.0   | 360  | 0.0079          | 0.9989   | 0.9989 | 0.9989    | 0.9989 |
| 0.0005        | 9.0   | 405  | 0.0081          | 0.9989   | 0.9989 | 0.9989    | 0.9989 |
| 0.0004        | 10.0  | 450  | 0.0083          | 0.9989   | 0.9989 | 0.9989    | 0.9989 |
| 0.0004        | 11.0  | 495  | 0.0085          | 0.9989   | 0.9989 | 0.9989    | 0.9989 |
| 0.0003        | 12.0  | 540  | 0.0086          | 0.9989   | 0.9989 | 0.9989    | 0.9989 |
| 0.0003        | 13.0  | 585  | 0.0087          | 0.9989   | 0.9989 | 0.9989    | 0.9989 |
| 0.0003        | 14.0  | 630  | 0.0088          | 0.9989   | 0.9989 | 0.9989    | 0.9989 |
| 0.0003        | 15.0  | 675  | 0.0089          | 0.9989   | 0.9989 | 0.9989    | 0.9989 |
| 0.0003        | 16.0  | 720  | 0.0090          | 0.9989   | 0.9989 | 0.9989    | 0.9989 |
| 0.0002        | 17.0  | 765  | 0.0090          | 0.9989   | 0.9989 | 0.9989    | 0.9989 |
| 0.0002        | 18.0  | 810  | 0.0090          | 0.9989   | 0.9989 | 0.9989    | 0.9989 |
| 0.0002        | 19.0  | 855  | 0.0091          | 0.9989   | 0.9989 | 0.9989    | 0.9989 |
| 0.0002        | 20.0  | 900  | 0.0091          | 0.9989   | 0.9989 | 0.9989    | 0.9989 |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1