---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-dob
  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-base-uncased-finetuned-dob

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: 0.0001

## 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: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5564        | 1.0   | 10   | 0.3900          |
| 0.3348        | 2.0   | 20   | 0.1773          |
| 0.1584        | 3.0   | 30   | 0.0763          |
| 0.0758        | 4.0   | 40   | 0.0294          |
| 0.0322        | 5.0   | 50   | 0.0055          |
| 0.0075        | 6.0   | 60   | 0.0023          |
| 0.0035        | 7.0   | 70   | 0.0015          |
| 0.0021        | 8.0   | 80   | 0.0011          |
| 0.0013        | 9.0   | 90   | 0.0009          |
| 0.0011        | 10.0  | 100  | 0.0008          |
| 0.0009        | 11.0  | 110  | 0.0008          |
| 0.0009        | 12.0  | 120  | 0.0007          |
| 0.0008        | 13.0  | 130  | 0.0006          |
| 0.0008        | 14.0  | 140  | 0.0006          |
| 0.0007        | 15.0  | 150  | 0.0006          |
| 0.0007        | 16.0  | 160  | 0.0005          |
| 0.0006        | 17.0  | 170  | 0.0005          |
| 0.0006        | 18.0  | 180  | 0.0005          |
| 0.0005        | 19.0  | 190  | 0.0004          |
| 0.0005        | 20.0  | 200  | 0.0004          |
| 0.0005        | 21.0  | 210  | 0.0004          |
| 0.0005        | 22.0  | 220  | 0.0004          |
| 0.0005        | 23.0  | 230  | 0.0004          |
| 0.0004        | 24.0  | 240  | 0.0004          |
| 0.0004        | 25.0  | 250  | 0.0003          |
| 0.0004        | 26.0  | 260  | 0.0003          |
| 0.0004        | 27.0  | 270  | 0.0003          |
| 0.0004        | 28.0  | 280  | 0.0003          |
| 0.0003        | 29.0  | 290  | 0.0003          |
| 0.0003        | 30.0  | 300  | 0.0003          |
| 0.0003        | 31.0  | 310  | 0.0003          |
| 0.0004        | 32.0  | 320  | 0.0003          |
| 0.0003        | 33.0  | 330  | 0.0003          |
| 0.0003        | 34.0  | 340  | 0.0003          |
| 0.0003        | 35.0  | 350  | 0.0003          |
| 0.0003        | 36.0  | 360  | 0.0003          |
| 0.0003        | 37.0  | 370  | 0.0002          |
| 0.0003        | 38.0  | 380  | 0.0002          |
| 0.0003        | 39.0  | 390  | 0.0002          |
| 0.0003        | 40.0  | 400  | 0.0002          |
| 0.0003        | 41.0  | 410  | 0.0002          |
| 0.0003        | 42.0  | 420  | 0.0002          |
| 0.0002        | 43.0  | 430  | 0.0002          |
| 0.0002        | 44.0  | 440  | 0.0002          |
| 0.0002        | 45.0  | 450  | 0.0002          |
| 0.0002        | 46.0  | 460  | 0.0002          |
| 0.0002        | 47.0  | 470  | 0.0002          |
| 0.0002        | 48.0  | 480  | 0.0002          |
| 0.0002        | 49.0  | 490  | 0.0002          |
| 0.0002        | 50.0  | 500  | 0.0002          |
| 0.0002        | 51.0  | 510  | 0.0002          |
| 0.0002        | 52.0  | 520  | 0.0002          |
| 0.0002        | 53.0  | 530  | 0.0002          |
| 0.0002        | 54.0  | 540  | 0.0002          |
| 0.0002        | 55.0  | 550  | 0.0002          |
| 0.0002        | 56.0  | 560  | 0.0002          |
| 0.0002        | 57.0  | 570  | 0.0002          |
| 0.0002        | 58.0  | 580  | 0.0002          |
| 0.0002        | 59.0  | 590  | 0.0002          |
| 0.0002        | 60.0  | 600  | 0.0002          |
| 0.0002        | 61.0  | 610  | 0.0002          |
| 0.0002        | 62.0  | 620  | 0.0002          |
| 0.0002        | 63.0  | 630  | 0.0002          |
| 0.0002        | 64.0  | 640  | 0.0002          |
| 0.0002        | 65.0  | 650  | 0.0002          |
| 0.0002        | 66.0  | 660  | 0.0002          |
| 0.0002        | 67.0  | 670  | 0.0002          |
| 0.0002        | 68.0  | 680  | 0.0002          |
| 0.0002        | 69.0  | 690  | 0.0002          |
| 0.0002        | 70.0  | 700  | 0.0002          |
| 0.0002        | 71.0  | 710  | 0.0002          |
| 0.0002        | 72.0  | 720  | 0.0002          |
| 0.0002        | 73.0  | 730  | 0.0002          |
| 0.0002        | 74.0  | 740  | 0.0002          |
| 0.0001        | 75.0  | 750  | 0.0002          |
| 0.0002        | 76.0  | 760  | 0.0002          |
| 0.0002        | 77.0  | 770  | 0.0002          |
| 0.0001        | 78.0  | 780  | 0.0002          |
| 0.0002        | 79.0  | 790  | 0.0002          |
| 0.0002        | 80.0  | 800  | 0.0002          |
| 0.0001        | 81.0  | 810  | 0.0002          |
| 0.0002        | 82.0  | 820  | 0.0002          |
| 0.0001        | 83.0  | 830  | 0.0001          |
| 0.0001        | 84.0  | 840  | 0.0001          |
| 0.0001        | 85.0  | 850  | 0.0001          |
| 0.0001        | 86.0  | 860  | 0.0001          |
| 0.0001        | 87.0  | 870  | 0.0001          |
| 0.0001        | 88.0  | 880  | 0.0001          |
| 0.0001        | 89.0  | 890  | 0.0001          |
| 0.0001        | 90.0  | 900  | 0.0001          |
| 0.0001        | 91.0  | 910  | 0.0001          |
| 0.0001        | 92.0  | 920  | 0.0001          |
| 0.0001        | 93.0  | 930  | 0.0001          |
| 0.0001        | 94.0  | 940  | 0.0001          |
| 0.0001        | 95.0  | 950  | 0.0001          |
| 0.0001        | 96.0  | 960  | 0.0001          |
| 0.0001        | 97.0  | 970  | 0.0001          |
| 0.0001        | 98.0  | 980  | 0.0001          |
| 0.0001        | 99.0  | 990  | 0.0001          |
| 0.0001        | 100.0 | 1000 | 0.0001          |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0