|
--- |
|
license: apache-2.0 |
|
base_model: distilbert-base-uncased |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
model-index: |
|
- name: DIALOGUE_overfit_check |
|
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_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 |
|
|
