---
license: apache-2.0
base_model: distilbert-base-cased
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-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1637
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.1634        | 0.62  | 30   | 0.7096          | 0.8900    | 0.8421 | 0.8322 | 0.8421   |
| 0.5104        | 1.25  | 60   | 0.2078          | 0.9524    | 0.9474 | 0.9472 | 0.9474   |
| 0.1827        | 1.88  | 90   | 0.1302          | 0.9637    | 0.9605 | 0.9604 | 0.9605   |
| 0.0426        | 2.5   | 120  | 0.0556          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0178        | 3.12  | 150  | 0.1489          | 0.9421    | 0.9342 | 0.9339 | 0.9342   |
| 0.0091        | 3.75  | 180  | 0.1377          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0054        | 4.38  | 210  | 0.1268          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0041        | 5.0   | 240  | 0.1340          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0033        | 5.62  | 270  | 0.1405          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0029        | 6.25  | 300  | 0.1328          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0024        | 6.88  | 330  | 0.1372          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0021        | 7.5   | 360  | 0.1467          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0019        | 8.12  | 390  | 0.1397          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0016        | 8.75  | 420  | 0.1445          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0015        | 9.38  | 450  | 0.1510          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0013        | 10.0  | 480  | 0.1413          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0012        | 10.62 | 510  | 0.1453          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0011        | 11.25 | 540  | 0.1494          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.001         | 11.88 | 570  | 0.1480          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.001         | 12.5  | 600  | 0.1514          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0008        | 13.12 | 630  | 0.1513          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0008        | 13.75 | 660  | 0.1514          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0008        | 14.38 | 690  | 0.1521          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0008        | 15.0  | 720  | 0.1527          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0007        | 15.62 | 750  | 0.1528          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0007        | 16.25 | 780  | 0.1563          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0006        | 16.88 | 810  | 0.1570          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0006        | 17.5  | 840  | 0.1554          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0006        | 18.12 | 870  | 0.1560          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0006        | 18.75 | 900  | 0.1565          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0005        | 19.38 | 930  | 0.1571          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0005        | 20.0  | 960  | 0.1576          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0005        | 20.62 | 990  | 0.1591          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0005        | 21.25 | 1020 | 0.1605          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0005        | 21.88 | 1050 | 0.1608          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0005        | 22.5  | 1080 | 0.1613          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0005        | 23.12 | 1110 | 0.1609          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 23.75 | 1140 | 0.1611          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 24.38 | 1170 | 0.1617          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 25.0  | 1200 | 0.1617          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 25.62 | 1230 | 0.1625          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 26.25 | 1260 | 0.1628          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 26.88 | 1290 | 0.1630          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 27.5  | 1320 | 0.1635          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 28.12 | 1350 | 0.1636          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 28.75 | 1380 | 0.1637          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 29.38 | 1410 | 0.1637          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 30.0  | 1440 | 0.1637          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |


### Framework versions

- Transformers 4.37.1
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1