bank-transactions-statements-classification

This model is a fine-tuned version of flaubert/flaubert_small_cased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.0458
• Accuracy: 0.7683
• F1 Macro: 0.7945
• F1 Weighted: 0.7635

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: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	F1 Weighted
No log	0.29	50	3.6955	0.1012	0.0238	0.0880
No log	0.58	100	3.2965	0.2150	0.0569	0.1598
No log	0.87	150	3.1122	0.2530	0.0833	0.1889
No log	1.16	200	2.6838	0.3622	0.1800	0.3051
No log	1.45	250	2.5128	0.3808	0.1938	0.3139
No log	1.74	300	2.1573	0.4913	0.3241	0.4522
No log	2.03	350	2.0208	0.5220	0.3910	0.4832
No log	2.33	400	2.0454	0.5053	0.4090	0.4613
No log	2.62	450	1.7601	0.5599	0.4682	0.5303
3.1338	2.91	500	1.6837	0.5965	0.5489	0.5736
3.1338	3.2	550	1.6337	0.5885	0.5744	0.5609
3.1338	3.49	600	1.4553	0.6491	0.6219	0.6322
3.1338	3.78	650	1.4483	0.6531	0.6441	0.6345
3.1338	4.07	700	1.4108	0.6625	0.6810	0.6522
3.1338	4.36	750	1.3241	0.6924	0.6999	0.6769
3.1338	4.65	800	1.3254	0.6824	0.6960	0.6703
3.1338	4.94	850	1.3349	0.6937	0.6952	0.6759
3.1338	5.23	900	1.2264	0.7057	0.7157	0.6931
3.1338	5.52	950	1.3012	0.6891	0.7061	0.6748
1.6259	5.81	1000	1.2756	0.7071	0.7224	0.6925
1.6259	6.1	1050	1.1432	0.7317	0.7440	0.7267
1.6259	6.4	1100	1.2014	0.7290	0.7434	0.7161
1.6259	6.69	1150	1.1029	0.7483	0.7656	0.7367
1.6259	6.98	1200	1.1643	0.7310	0.7470	0.7227
1.6259	7.27	1250	1.1112	0.7477	0.7561	0.7371
1.6259	7.56	1300	1.1662	0.7350	0.7668	0.7254
1.6259	7.85	1350	1.0756	0.7577	0.7823	0.7530
1.6259	8.14	1400	1.1390	0.7403	0.7657	0.7318
1.6259	8.43	1450	1.1555	0.7437	0.7637	0.7377
1.092	8.72	1500	1.1086	0.7437	0.7686	0.7384
1.092	9.01	1550	1.0789	0.7510	0.7780	0.7427
1.092	9.3	1600	1.0613	0.7543	0.7823	0.7492
1.092	9.59	1650	1.0750	0.7477	0.7701	0.7382
1.092	9.88	1700	1.1412	0.7423	0.7772	0.7349
1.092	10.17	1750	1.0580	0.7617	0.7918	0.7549
1.092	10.47	1800	1.0667	0.7670	0.7856	0.7580
1.092	10.76	1850	1.1344	0.7403	0.7757	0.7332
1.092	11.05	1900	1.0808	0.7603	0.7944	0.7571
1.092	11.34	1950	1.0367	0.7690	0.7932	0.7655
0.9029	11.63	2000	1.0921	0.7577	0.7861	0.7504
0.9029	11.92	2050	1.0833	0.7603	0.7912	0.7541
0.9029	12.21	2100	1.0523	0.7716	0.7968	0.7662
0.9029	12.5	2150	1.0467	0.7683	0.7939	0.7614
0.9029	12.79	2200	1.0515	0.7703	0.7987	0.7667
0.9029	13.08	2250	1.0604	0.7696	0.8020	0.7654
0.9029	13.37	2300	1.0900	0.7716	0.8002	0.7663
0.9029	13.66	2350	1.0348	0.7743	0.8009	0.7686
0.9029	13.95	2400	1.0495	0.7656	0.7929	0.7610
0.9029	14.24	2450	1.0411	0.7670	0.7956	0.7624
0.7924	14.53	2500	1.0458	0.7683	0.7945	0.7635
0.7924	14.83	2550	1.0401	0.7696	0.7982	0.7649

Framework versions

• Transformers 4.34.0
• Pytorch 2.0.1+cu118
• Datasets 2.14.5
• Tokenizers 0.14.1