
<!-- 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. -->

# DIALOGUE_overfit_check

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: 0.1840
- Precision: 0.9762
- Recall: 0.9737
- F1: 0.9736
- Accuracy: 0.9737

## 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: 3e-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
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.0266        | 0.62  | 30   | 0.5087          | 1.0       | 1.0    | 1.0    | 1.0      |
| 0.4009        | 1.25  | 60   | 0.1389          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.1301        | 1.88  | 90   | 0.1436          | 0.9637    | 0.9605 | 0.9604 | 0.9605   |
| 0.0342        | 2.5   | 120  | 0.1055          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0288        | 3.12  | 150  | 0.1395          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0099        | 3.75  | 180  | 0.1259          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0057        | 4.38  | 210  | 0.1315          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0042        | 5.0   | 240  | 0.1338          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0033        | 5.62  | 270  | 0.1373          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0027        | 6.25  | 300  | 0.1403          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0024        | 6.88  | 330  | 0.1457          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.002         | 7.5   | 360  | 0.1483          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0017        | 8.12  | 390  | 0.1483          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0016        | 8.75  | 420  | 0.1503          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0014        | 9.38  | 450  | 0.1535          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0013        | 10.0  | 480  | 0.1546          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0012        | 10.62 | 510  | 0.1576          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0011        | 11.25 | 540  | 0.1593          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.001         | 11.88 | 570  | 0.1672          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0009        | 12.5  | 600  | 0.1686          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0008        | 13.12 | 630  | 0.1696          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0008        | 13.75 | 660  | 0.1696          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0007        | 14.38 | 690  | 0.1702          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0007        | 15.0  | 720  | 0.1711          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0006        | 15.62 | 750  | 0.1716          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0006        | 16.25 | 780  | 0.1726          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0006        | 16.88 | 810  | 0.1731          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0006        | 17.5  | 840  | 0.1744          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0006        | 18.12 | 870  | 0.1762          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0005        | 18.75 | 900  | 0.1773          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0005        | 19.38 | 930  | 0.1777          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0005        | 20.0  | 960  | 0.1781          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0005        | 20.62 | 990  | 0.1785          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 21.25 | 1020 | 0.1795          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 21.88 | 1050 | 0.1801          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 22.5  | 1080 | 0.1805          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 23.12 | 1110 | 0.1812          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 23.75 | 1140 | 0.1818          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 24.38 | 1170 | 0.1821          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 25.0  | 1200 | 0.1824          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 25.62 | 1230 | 0.1827          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 26.25 | 1260 | 0.1831          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 26.88 | 1290 | 0.1833          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 27.5  | 1320 | 0.1836          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 28.12 | 1350 | 0.1838          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 28.75 | 1380 | 0.1839          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 29.38 | 1410 | 0.1840          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 30.0  | 1440 | 0.1840          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "DIALOGUE_overfit_check", "results": []}]}
transformers tensorboard safetensors distilbert text-classification generated_from_trainer base_model:distilbert-base-uncased license:apache-2.0 autotrain_compatible endpoints_compatible region:us