File size: 6,276 Bytes
070883d 07e521a 070883d 6e10c91 070883d 900b62f 070883d 6e10c91 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 |
---
license: apache-2.0
tags:
- generated_from_trainer
base_model: google-bert/bert-base-multilingual-cased
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: bert-base-multi-class-classification-cs
results: []
datasets:
- Porameht/customer-support-th-26.9k
language:
- th
pipeline_tag: text-classification
---
<!-- 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. -->
# bert-base-multi-class-classification-cs
This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on an [Porameht/customer-support-th-26.9k](https://huggingface.co/datasets/Porameht/customer-support-th-26.9k) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0385
- Accuracy: 0.9942
- F1: 0.9942
- Precision: 0.9942
- Recall: 0.9942
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 3.3053 | 0.0595 | 50 | 3.1767 | 0.0871 | 0.0306 | 0.0333 | 0.0869 |
| 2.8293 | 0.1190 | 100 | 2.1807 | 0.4647 | 0.3675 | 0.5055 | 0.4651 |
| 1.6887 | 0.1786 | 150 | 1.1300 | 0.7705 | 0.7362 | 0.7621 | 0.7722 |
| 0.9012 | 0.2381 | 200 | 0.6245 | 0.8321 | 0.8086 | 0.8549 | 0.8356 |
| 0.5237 | 0.2976 | 250 | 0.3510 | 0.9129 | 0.9029 | 0.9007 | 0.9147 |
| 0.3115 | 0.3571 | 300 | 0.2218 | 0.9512 | 0.9517 | 0.9545 | 0.9518 |
| 0.2217 | 0.4167 | 350 | 0.1746 | 0.9382 | 0.9284 | 0.9596 | 0.9388 |
| 0.1464 | 0.4762 | 400 | 0.1210 | 0.9729 | 0.9731 | 0.9749 | 0.9730 |
| 0.1201 | 0.5357 | 450 | 0.0977 | 0.9804 | 0.9805 | 0.9810 | 0.9805 |
| 0.0921 | 0.5952 | 500 | 0.1212 | 0.9722 | 0.9721 | 0.9741 | 0.9718 |
| 0.1061 | 0.6548 | 550 | 0.1224 | 0.9726 | 0.9728 | 0.9750 | 0.9728 |
| 0.0996 | 0.7143 | 600 | 0.0812 | 0.9817 | 0.9815 | 0.9819 | 0.9816 |
| 0.1196 | 0.7738 | 650 | 0.0726 | 0.9859 | 0.9858 | 0.9862 | 0.9857 |
| 0.101 | 0.8333 | 700 | 0.0711 | 0.9853 | 0.9854 | 0.9856 | 0.9853 |
| 0.1159 | 0.8929 | 750 | 0.1012 | 0.9792 | 0.9795 | 0.9803 | 0.9796 |
| 0.086 | 0.9524 | 800 | 0.0693 | 0.9870 | 0.9871 | 0.9874 | 0.9870 |
| 0.0742 | 1.0119 | 850 | 0.0619 | 0.9885 | 0.9886 | 0.9888 | 0.9885 |
| 0.0713 | 1.0714 | 900 | 0.0517 | 0.9896 | 0.9896 | 0.9897 | 0.9896 |
| 0.02 | 1.1310 | 950 | 0.0707 | 0.9869 | 0.9870 | 0.9874 | 0.9870 |
| 0.038 | 1.1905 | 1000 | 0.0455 | 0.9920 | 0.9920 | 0.9920 | 0.9919 |
| 0.0378 | 1.25 | 1050 | 0.0485 | 0.9906 | 0.9906 | 0.9906 | 0.9906 |
| 0.0257 | 1.3095 | 1100 | 0.0452 | 0.9921 | 0.9921 | 0.9922 | 0.9921 |
| 0.0454 | 1.3690 | 1150 | 0.0494 | 0.9905 | 0.9905 | 0.9906 | 0.9905 |
| 0.0174 | 1.4286 | 1200 | 0.0404 | 0.9923 | 0.9922 | 0.9922 | 0.9922 |
| 0.0425 | 1.4881 | 1250 | 0.0627 | 0.9879 | 0.9877 | 0.9879 | 0.9877 |
| 0.0489 | 1.5476 | 1300 | 0.0525 | 0.9908 | 0.9907 | 0.9907 | 0.9907 |
| 0.0816 | 1.6071 | 1350 | 0.0439 | 0.9918 | 0.9918 | 0.9919 | 0.9917 |
| 0.0375 | 1.6667 | 1400 | 0.0434 | 0.9921 | 0.9920 | 0.9920 | 0.9921 |
| 0.0435 | 1.7262 | 1450 | 0.0368 | 0.9929 | 0.9928 | 0.9929 | 0.9929 |
| 0.0285 | 1.7857 | 1500 | 0.0364 | 0.9935 | 0.9934 | 0.9935 | 0.9934 |
| 0.0222 | 1.8452 | 1550 | 0.0332 | 0.9942 | 0.9942 | 0.9943 | 0.9941 |
| 0.0311 | 1.9048 | 1600 | 0.0394 | 0.9929 | 0.9929 | 0.9930 | 0.9929 |
| 0.0269 | 1.9643 | 1650 | 0.0359 | 0.9935 | 0.9934 | 0.9935 | 0.9934 |
| 0.0258 | 2.0238 | 1700 | 0.0326 | 0.9937 | 0.9937 | 0.9938 | 0.9937 |
| 0.0046 | 2.0833 | 1750 | 0.0324 | 0.9945 | 0.9944 | 0.9945 | 0.9944 |
| 0.0152 | 2.1429 | 1800 | 0.0329 | 0.9946 | 0.9946 | 0.9948 | 0.9946 |
| 0.024 | 2.2024 | 1850 | 0.0305 | 0.9948 | 0.9947 | 0.9948 | 0.9947 |
| 0.0212 | 2.2619 | 1900 | 0.0333 | 0.9943 | 0.9943 | 0.9943 | 0.9943 |
| 0.0029 | 2.3214 | 1950 | 0.0322 | 0.9937 | 0.9937 | 0.9938 | 0.9937 |
| 0.0114 | 2.3810 | 2000 | 0.0342 | 0.9940 | 0.9940 | 0.9941 | 0.9940 |
| 0.0115 | 2.4405 | 2050 | 0.0328 | 0.9942 | 0.9941 | 0.9942 | 0.9941 |
| 0.0182 | 2.5 | 2100 | 0.0333 | 0.9937 | 0.9937 | 0.9938 | 0.9936 |
| 0.01 | 2.5595 | 2150 | 0.0314 | 0.9940 | 0.9940 | 0.9941 | 0.9940 |
| 0.0205 | 2.6190 | 2200 | 0.0325 | 0.9937 | 0.9937 | 0.9938 | 0.9937 |
| 0.0173 | 2.6786 | 2250 | 0.0335 | 0.9940 | 0.9940 | 0.9941 | 0.9940 |
| 0.0093 | 2.7381 | 2300 | 0.0340 | 0.9942 | 0.9942 | 0.9943 | 0.9941 |
| 0.0151 | 2.7976 | 2350 | 0.0327 | 0.9946 | 0.9947 | 0.9947 | 0.9946 |
| 0.0077 | 2.8571 | 2400 | 0.0321 | 0.9948 | 0.9948 | 0.9949 | 0.9948 |
| 0.0074 | 2.9167 | 2450 | 0.0311 | 0.9946 | 0.9946 | 0.9947 | 0.9946 |
| 0.0021 | 2.9762 | 2500 | 0.0310 | 0.9946 | 0.9946 | 0.9947 | 0.9946 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |