---
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: 0.2117
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.9949        | 0.62  | 30   | 0.4697          | 0.9659    | 0.9605 | 0.9603 | 0.9605   |
| 0.3831        | 1.25  | 60   | 0.1338          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.1135        | 1.88  | 90   | 0.1407          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0256        | 2.5   | 120  | 0.1359          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0126        | 3.12  | 150  | 0.1449          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0227        | 3.75  | 180  | 0.1552          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0051        | 4.38  | 210  | 0.1573          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0037        | 5.0   | 240  | 0.1594          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.003         | 5.62  | 270  | 0.1626          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0024        | 6.25  | 300  | 0.1645          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0021        | 6.88  | 330  | 0.1737          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0018        | 7.5   | 360  | 0.1759          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0015        | 8.12  | 390  | 0.1774          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0014        | 8.75  | 420  | 0.1801          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0013        | 9.38  | 450  | 0.1837          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0011        | 10.0  | 480  | 0.1852          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.001         | 10.62 | 510  | 0.1878          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0009        | 11.25 | 540  | 0.1892          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0009        | 11.88 | 570  | 0.1939          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0008        | 12.5  | 600  | 0.1948          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0008        | 13.12 | 630  | 0.1961          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0007        | 13.75 | 660  | 0.1965          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0007        | 14.38 | 690  | 0.1972          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0006        | 15.0  | 720  | 0.1988          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0006        | 15.62 | 750  | 0.1993          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0005        | 16.25 | 780  | 0.2006          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0005        | 16.88 | 810  | 0.2020          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0005        | 17.5  | 840  | 0.2031          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0005        | 18.12 | 870  | 0.2045          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0005        | 18.75 | 900  | 0.2054          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 19.38 | 930  | 0.2051          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 20.0  | 960  | 0.2053          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 20.62 | 990  | 0.2058          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 21.25 | 1020 | 0.2069          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 21.88 | 1050 | 0.2076          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 22.5  | 1080 | 0.2079          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 23.12 | 1110 | 0.2084          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 23.75 | 1140 | 0.2092          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 24.38 | 1170 | 0.2095          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 25.0  | 1200 | 0.2100          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 25.62 | 1230 | 0.2104          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0003        | 26.25 | 1260 | 0.2109          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0003        | 26.88 | 1290 | 0.2111          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0003        | 27.5  | 1320 | 0.2113          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0003        | 28.12 | 1350 | 0.2115          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0003        | 28.75 | 1380 | 0.2116          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0003        | 29.38 | 1410 | 0.2117          | 0.9762    | 0.9737 | 0.9736 | 0.9737   |
| 0.0004        | 30.0  | 1440 | 0.2117          | 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