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

# DIALOGUE_one

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: 0.1809
- 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: 2e-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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.1724        | 0.62  | 30   | 0.8000          | 0.9224    | 0.9079 | 0.9071 | 0.9079   |
| 0.6097        | 1.25  | 60   | 0.2953          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.2608        | 1.88  | 90   | 0.1342          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0958        | 2.5   | 120  | 0.0711          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0424        | 3.12  | 150  | 0.1116          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0205        | 3.75  | 180  | 0.1195          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0119        | 4.38  | 210  | 0.0987          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0087        | 5.0   | 240  | 0.1092          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0068        | 5.62  | 270  | 0.1103          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0057        | 6.25  | 300  | 0.1116          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0048        | 6.88  | 330  | 0.1350          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0041        | 7.5   | 360  | 0.1359          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0037        | 8.12  | 390  | 0.1339          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0031        | 8.75  | 420  | 0.1655          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0028        | 9.38  | 450  | 0.1608          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0026        | 10.0  | 480  | 0.1545          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0024        | 10.62 | 510  | 0.1554          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0022        | 11.25 | 540  | 0.1602          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.002         | 11.88 | 570  | 0.1619          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0019        | 12.5  | 600  | 0.1632          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0017        | 13.12 | 630  | 0.1643          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0016        | 13.75 | 660  | 0.1647          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0016        | 14.38 | 690  | 0.1667          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0015        | 15.0  | 720  | 0.1683          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0013        | 15.62 | 750  | 0.1688          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0013        | 16.25 | 780  | 0.1702          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0012        | 16.88 | 810  | 0.1708          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0011        | 17.5  | 840  | 0.1715          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0011        | 18.12 | 870  | 0.1742          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.001         | 18.75 | 900  | 0.1754          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.001         | 19.38 | 930  | 0.1755          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.001         | 20.0  | 960  | 0.1759          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0009        | 20.62 | 990  | 0.1765          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0009        | 21.25 | 1020 | 0.1776          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0009        | 21.88 | 1050 | 0.1779          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0008        | 22.5  | 1080 | 0.1776          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0009        | 23.12 | 1110 | 0.1782          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0008        | 23.75 | 1140 | 0.1789          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0008        | 24.38 | 1170 | 0.1792          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0008        | 25.0  | 1200 | 0.1796          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0008        | 25.62 | 1230 | 0.1799          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0008        | 26.25 | 1260 | 0.1803          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0007        | 26.88 | 1290 | 0.1804          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0008        | 27.5  | 1320 | 0.1808          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0007        | 28.12 | 1350 | 0.1809          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0007        | 28.75 | 1380 | 0.1810          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0008        | 29.38 | 1410 | 0.1809          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0008        | 30.0  | 1440 | 0.1809          | 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