metadata

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-intent-classification-cs-th
    results: []
datasets:
  - Porameht/customer-support-th-26.9k
language:
  - th
library_name: transformers

bert-base-intent-classification-cs-th

This model is a fine-tuned version of google-bert/bert-base-multilingual-cased on an Porameht/customer-support-th-26.9k dataset.

🧠 Can understand if any customer wants to cancel an order from a sentence.

It achieves the following results on the evaluation set:

Loss: 0.0408
Accuracy: 0.9936
F1: 0.9936
Precision: 0.9937
Recall: 0.9936

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: 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
3.2835	0.0595	50	3.1041	0.1203	0.0504	0.0632	0.1210
2.6752	0.1190	100	1.9646	0.5387	0.4737	0.6298	0.5426
1.4751	0.1786	150	0.9447	0.8190	0.7929	0.8271	0.8188
0.7571	0.2381	200	0.5163	0.8952	0.8826	0.8812	0.8955
0.4849	0.2976	250	0.3539	0.9003	0.8905	0.8926	0.9021
0.3401	0.3571	300	0.2883	0.9160	0.9037	0.9012	0.9165
0.2533	0.4167	350	0.1735	0.9431	0.9322	0.9266	0.9443
0.177	0.4762	400	0.1326	0.9665	0.9670	0.9676	0.9671
0.119	0.5357	450	0.1527	0.9592	0.9582	0.9699	0.9600
0.1183	0.5952	500	0.0886	0.9839	0.9841	0.9841	0.9842
0.1065	0.6548	550	0.0829	0.9844	0.9844	0.9847	0.9844
0.1006	0.7143	600	0.0686	0.9869	0.9869	0.9872	0.9869
0.1096	0.7738	650	0.1071	0.9789	0.9791	0.9800	0.9788
0.1392	0.8333	700	0.0939	0.9804	0.9804	0.9808	0.9803
0.1067	0.8929	750	0.1077	0.9786	0.9790	0.9802	0.9786
0.0779	0.9524	800	0.0657	0.9878	0.9878	0.9879	0.9879
0.0626	1.0119	850	0.0750	0.9851	0.9853	0.9856	0.9852
0.0419	1.0714	900	0.0641	0.9893	0.9893	0.9895	0.9893
0.0373	1.1310	950	0.0664	0.9891	0.9891	0.9893	0.9890
0.035	1.1905	1000	0.0575	0.9906	0.9906	0.9907	0.9906
0.036	1.25	1050	0.0601	0.9891	0.9893	0.9895	0.9892
0.0765	1.3095	1100	0.0682	0.9875	0.9875	0.9877	0.9874
0.0637	1.3690	1150	0.0587	0.9906	0.9906	0.9908	0.9906
0.0241	1.4286	1200	0.0528	0.9906	0.9907	0.9909	0.9905
0.0608	1.4881	1250	0.0458	0.9920	0.9920	0.9922	0.9919
0.0199	1.5476	1300	0.0508	0.9914	0.9914	0.9915	0.9914
0.0663	1.6071	1350	0.0461	0.9911	0.9910	0.9911	0.9910
0.0495	1.6667	1400	0.0525	0.9906	0.9907	0.9908	0.9906
0.0336	1.7262	1450	0.0478	0.9915	0.9916	0.9917	0.9915
0.0249	1.7857	1500	0.0578	0.9891	0.9891	0.9892	0.9891
0.0287	1.8452	1550	0.0547	0.9908	0.9908	0.9909	0.9908
0.0607	1.9048	1600	0.0395	0.9929	0.9929	0.9930	0.9928
0.0268	1.9643	1650	0.0529	0.9897	0.9898	0.9902	0.9897
0.013	2.0238	1700	0.0455	0.9924	0.9925	0.9926	0.9925
0.0106	2.0833	1750	0.0419	0.9927	0.9928	0.9928	0.9927
0.007	2.1429	1800	0.0461	0.9920	0.9920	0.9921	0.9919
0.0502	2.2024	1850	0.0433	0.9929	0.9929	0.9930	0.9929
0.017	2.2619	1900	0.0440	0.9926	0.9926	0.9927	0.9926
0.0119	2.3214	1950	0.0403	0.9927	0.9928	0.9928	0.9927
0.0063	2.3810	2000	0.0391	0.9930	0.9930	0.9931	0.9930
0.0103	2.4405	2050	0.0412	0.9929	0.9929	0.9930	0.9929
0.012	2.5	2100	0.0420	0.9929	0.9929	0.9930	0.9929
0.0233	2.5595	2150	0.0407	0.9927	0.9928	0.9928	0.9928
0.0169	2.6190	2200	0.0397	0.9930	0.9930	0.9931	0.9930
0.0281	2.6786	2250	0.0367	0.9933	0.9933	0.9934	0.9933
0.0117	2.7381	2300	0.0360	0.9933	0.9933	0.9934	0.9933
0.0225	2.7976	2350	0.0354	0.9936	0.9936	0.9937	0.9936
0.0078	2.8571	2400	0.0357	0.9936	0.9936	0.9937	0.9936
0.0164	2.9167	2450	0.0346	0.9939	0.9939	0.9940	0.9939
0.0016	2.9762	2500	0.0345	0.9939	0.9939	0.9940	0.9939

Framework versions

Transformers 4.40.1
Pytorch 2.2.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1