Method2_E13B_SC_BS4_LR3e5

This model is a fine-tuned version of rafsankabir/Pretrained_E13B_Method2 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 2.5641
• Accuracy: 0.6803
• F1 Macro: 0.6446

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: 4
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 40
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro
No log	0.16	500	1.0767	0.3976	0.1896
1.075	0.32	1000	1.0769	0.3976	0.1896
1.075	0.48	1500	1.0183	0.5539	0.4151
1.0246	0.64	2000	0.8956	0.5916	0.4745
1.0246	0.8	2500	0.8743	0.6082	0.5120
0.8948	0.95	3000	0.8365	0.6216	0.5546
0.8948	1.11	3500	0.8635	0.6311	0.5752
0.8069	1.27	4000	0.9060	0.6158	0.5398
0.8069	1.43	4500	0.8231	0.6388	0.5924
0.7969	1.59	5000	0.8368	0.6331	0.5935
0.7969	1.75	5500	0.8262	0.6477	0.5981
0.7804	1.91	6000	0.8299	0.6579	0.6208
0.7804	2.07	6500	0.8197	0.6579	0.6364
0.715	2.23	7000	0.8498	0.6624	0.5955
0.715	2.39	7500	0.8357	0.6669	0.6218
0.6953	2.54	8000	0.8438	0.6560	0.6269
0.6953	2.7	8500	0.8528	0.6669	0.6022
0.7074	2.86	9000	0.8009	0.6745	0.6457
0.7074	3.02	9500	0.8222	0.6720	0.6402
0.6598	3.18	10000	0.9347	0.6650	0.6062
0.6598	3.34	10500	0.9053	0.6803	0.6510
0.6634	3.5	11000	0.8902	0.6720	0.6434
0.6634	3.66	11500	0.9370	0.6733	0.6415
0.6182	3.82	12000	0.8914	0.6745	0.6519
0.6182	3.98	12500	0.8938	0.6752	0.6389
0.6043	4.13	13000	1.0143	0.6745	0.6413
0.6043	4.29	13500	1.0768	0.6765	0.6543
0.587	4.45	14000	1.1154	0.6790	0.6421
0.587	4.61	14500	1.1295	0.6828	0.6525
0.6345	4.77	15000	1.1210	0.6822	0.6390
0.6345	4.93	15500	1.0062	0.6726	0.6380
0.6	5.09	16000	1.1504	0.6739	0.6369
0.6	5.25	16500	1.3298	0.6733	0.6280
0.5667	5.41	17000	1.2751	0.6662	0.6308
0.5667	5.57	17500	1.4070	0.6567	0.6069
0.614	5.73	18000	1.2956	0.6694	0.6284
0.614	5.88	18500	1.2795	0.6822	0.6382
0.5651	6.04	19000	1.3021	0.6739	0.6478
0.5651	6.2	19500	1.4076	0.6682	0.6333
0.5347	6.36	20000	1.3917	0.6733	0.6344
0.5347	6.52	20500	1.4203	0.6790	0.6285
0.5278	6.68	21000	1.3340	0.6860	0.6628
0.5278	6.84	21500	1.3521	0.6873	0.6489
0.5796	7.0	22000	1.2835	0.6847	0.6567
0.5796	7.16	22500	1.4437	0.6879	0.6563
0.4627	7.32	23000	1.5052	0.6835	0.6435
0.4627	7.47	23500	1.4991	0.6707	0.6434
0.518	7.63	24000	1.5436	0.6656	0.6403
0.518	7.79	24500	1.5247	0.6784	0.6433
0.5373	7.95	25000	1.4743	0.6835	0.6537
0.5373	8.11	25500	1.5379	0.6777	0.6385
0.4539	8.27	26000	1.5548	0.6739	0.6393
0.4539	8.43	26500	1.6174	0.6669	0.6378
0.4519	8.59	27000	1.5949	0.6816	0.6504
0.4519	8.75	27500	1.5558	0.6816	0.6357
0.4813	8.91	28000	1.5826	0.6739	0.6553
0.4813	9.06	28500	1.5929	0.6867	0.6540
0.4121	9.22	29000	1.6260	0.6886	0.6545
0.4121	9.38	29500	1.5950	0.6841	0.6500
0.4451	9.54	30000	1.6146	0.6854	0.6481
0.4451	9.7	30500	1.6587	0.6796	0.6493
0.4039	9.86	31000	1.6173	0.6758	0.6400
0.4039	10.02	31500	1.5952	0.6803	0.6517
0.3921	10.18	32000	1.7298	0.6694	0.6413
0.3921	10.34	32500	1.7106	0.6796	0.6467
0.3799	10.5	33000	1.6695	0.6867	0.6505
0.3799	10.66	33500	1.6907	0.6803	0.6550
0.4003	10.81	34000	1.6811	0.6809	0.6413
0.4003	10.97	34500	1.6644	0.6771	0.6352
0.3812	11.13	35000	1.7371	0.6822	0.6386
0.3812	11.29	35500	1.7405	0.6841	0.6516
0.3399	11.45	36000	1.6981	0.6822	0.6503
0.3399	11.61	36500	1.6536	0.6847	0.6483
0.3653	11.77	37000	1.7461	0.6790	0.6475
0.3653	11.93	37500	1.7247	0.6790	0.6485
0.338	12.09	38000	1.7433	0.6905	0.6532
0.338	12.25	38500	1.7331	0.6765	0.6558
0.3302	12.4	39000	1.7603	0.6796	0.6456
0.3302	12.56	39500	1.7784	0.6726	0.6505
0.3195	12.72	40000	1.8032	0.6784	0.6469
0.3195	12.88	40500	1.7869	0.6822	0.6553
0.3508	13.04	41000	1.7761	0.6752	0.6506
0.3508	13.2	41500	1.7806	0.6847	0.6454
0.2915	13.36	42000	1.8542	0.6707	0.6528
0.2915	13.52	42500	1.8365	0.6796	0.6520
0.3023	13.68	43000	1.8563	0.6828	0.6524
0.3023	13.84	43500	1.7947	0.6752	0.6495
0.3213	13.99	44000	1.8130	0.6796	0.6546
0.3213	14.15	44500	1.8288	0.6841	0.6502
0.2644	14.31	45000	1.8140	0.6726	0.6453
0.2644	14.47	45500	1.8711	0.6809	0.6552
0.2739	14.63	46000	1.8439	0.6873	0.6534
0.2739	14.79	46500	1.8302	0.6828	0.6460
0.3012	14.95	47000	1.8708	0.6752	0.6454
0.3012	15.11	47500	1.8498	0.6822	0.6487
0.2805	15.27	48000	1.8908	0.6803	0.6453
0.2805	15.43	48500	1.9480	0.6790	0.6406
0.2895	15.59	49000	1.8994	0.6675	0.6392
0.2895	15.74	49500	1.9135	0.6790	0.6461
0.2444	15.9	50000	1.9387	0.6841	0.6480
0.2444	16.06	50500	1.9175	0.6745	0.6463
0.2569	16.22	51000	1.9332	0.6745	0.6472
0.2569	16.38	51500	1.9400	0.6771	0.6445
0.2251	16.54	52000	1.9596	0.6745	0.6441
0.2251	16.7	52500	1.9959	0.6835	0.6464
0.2686	16.86	53000	1.9879	0.6777	0.6456
0.2686	17.02	53500	1.9882	0.6828	0.6471
0.2168	17.18	54000	2.0254	0.6886	0.6520
0.2168	17.33	54500	2.0432	0.6777	0.6442
0.2735	17.49	55000	1.9843	0.6745	0.6443
0.2735	17.65	55500	2.0330	0.6828	0.6451
0.2159	17.81	56000	2.0698	0.6682	0.6423
0.2159	17.97	56500	1.9797	0.6771	0.6426
0.245	18.13	57000	2.0008	0.6726	0.6383
0.245	18.29	57500	2.0425	0.6816	0.6473
0.2036	18.45	58000	2.0482	0.6720	0.6356
0.2036	18.61	58500	2.0950	0.6675	0.6384
0.2336	18.77	59000	2.0167	0.6854	0.6458
0.2336	18.92	59500	1.9984	0.6809	0.6406
0.2332	19.08	60000	2.0552	0.6739	0.6441
0.2332	19.24	60500	2.0450	0.6784	0.6459
0.1984	19.4	61000	2.0599	0.6752	0.6434
0.1984	19.56	61500	2.0704	0.6784	0.6417
0.1945	19.72	62000	2.0755	0.6758	0.6445
0.1945	19.88	62500	2.0660	0.6809	0.6428
0.2143	20.04	63000	2.0670	0.6739	0.6448
0.2143	20.2	63500	2.0581	0.6873	0.6509
0.1878	20.36	64000	2.1272	0.6752	0.6452
0.1878	20.52	64500	2.1002	0.6803	0.6511
0.2144	20.67	65000	2.1383	0.6713	0.6438
0.2144	20.83	65500	2.1070	0.6809	0.6419
0.2121	20.99	66000	2.1273	0.6726	0.6412
0.2121	21.15	66500	2.1605	0.6707	0.6395
0.1835	21.31	67000	2.2891	0.6567	0.6331
0.1835	21.47	67500	2.2472	0.6765	0.6402
0.1991	21.63	68000	2.2238	0.6752	0.6412
0.1991	21.79	68500	2.1965	0.6669	0.6372
0.2018	21.95	69000	2.2050	0.6669	0.6395
0.2018	22.11	69500	2.1795	0.6803	0.6467
0.151	22.26	70000	2.2214	0.6777	0.6430
0.151	22.42	70500	2.1754	0.6867	0.6513
0.2078	22.58	71000	2.1959	0.6822	0.6488
0.2078	22.74	71500	2.1933	0.6860	0.6481
0.2004	22.9	72000	2.2001	0.6816	0.6500
0.2004	23.06	72500	2.2159	0.6784	0.6490
0.1773	23.22	73000	2.2603	0.6790	0.6462
0.1773	23.38	73500	2.2331	0.6777	0.6470
0.174	23.54	74000	2.2554	0.6765	0.6471
0.174	23.7	74500	2.2000	0.6854	0.6517
0.2071	23.85	75000	2.1896	0.6790	0.6500
0.2071	24.01	75500	2.2270	0.6828	0.6479
0.1419	24.17	76000	2.2776	0.6765	0.6426
0.1419	24.33	76500	2.2895	0.6809	0.6437
0.1564	24.49	77000	2.2746	0.6828	0.6515
0.1564	24.65	77500	2.3156	0.6765	0.6356
0.1802	24.81	78000	2.2891	0.6726	0.6426
0.1802	24.97	78500	2.2610	0.6835	0.6502
0.1795	25.13	79000	2.2856	0.6777	0.6478
0.1795	25.29	79500	2.2410	0.6828	0.6478
0.1753	25.45	80000	2.2738	0.6701	0.6451
0.1753	25.6	80500	2.2679	0.6847	0.6440
0.1517	25.76	81000	2.2667	0.6796	0.6525
0.1517	25.92	81500	2.3471	0.6682	0.6455
0.1593	26.08	82000	2.2945	0.6816	0.6504
0.1593	26.24	82500	2.3202	0.6841	0.6456
0.1332	26.4	83000	2.3667	0.6733	0.6405
0.1332	26.56	83500	2.3295	0.6771	0.6377
0.1765	26.72	84000	2.3680	0.6720	0.6394
0.1765	26.88	84500	2.3246	0.6828	0.6456
0.1578	27.04	85000	2.3192	0.6745	0.6453
0.1578	27.19	85500	2.3216	0.6822	0.6471
0.1355	27.35	86000	2.3730	0.6796	0.6490
0.1355	27.51	86500	2.3650	0.6758	0.6415
0.1308	27.67	87000	2.4015	0.6784	0.6471
0.1308	27.83	87500	2.3700	0.6809	0.6403
0.1446	27.99	88000	2.3748	0.6796	0.6483
0.1446	28.15	88500	2.3575	0.6809	0.6497
0.1135	28.31	89000	2.3663	0.6835	0.6438
0.1135	28.47	89500	2.3817	0.6809	0.6490
0.1354	28.63	90000	2.4026	0.6739	0.6436
0.1354	28.78	90500	2.3825	0.6745	0.6392
0.1661	28.94	91000	2.3461	0.6771	0.6482
0.1661	29.1	91500	2.3496	0.6771	0.6422
0.1188	29.26	92000	2.3568	0.6790	0.6488
0.1188	29.42	92500	2.3496	0.6828	0.6430
0.1433	29.58	93000	2.4252	0.6707	0.6378
0.1433	29.74	93500	2.3805	0.6847	0.6459
0.1328	29.9	94000	2.3918	0.6860	0.6495
0.1328	30.06	94500	2.4026	0.6828	0.6495
0.1317	30.22	95000	2.4319	0.6841	0.6483
0.1317	30.38	95500	2.4375	0.6828	0.6492
0.122	30.53	96000	2.4401	0.6822	0.6475
0.122	30.69	96500	2.4397	0.6860	0.6473
0.1266	30.85	97000	2.4572	0.6847	0.6504
0.1266	31.01	97500	2.4506	0.6847	0.6513
0.1437	31.17	98000	2.4251	0.6822	0.6496
0.1437	31.33	98500	2.4420	0.6822	0.6521
0.1205	31.49	99000	2.4446	0.6816	0.6464
0.1205	31.65	99500	2.4408	0.6790	0.6450
0.1188	31.81	100000	2.4522	0.6765	0.6487
0.1188	31.97	100500	2.4313	0.6828	0.6495
0.1326	32.12	101000	2.4577	0.6784	0.6466
0.1326	32.28	101500	2.4524	0.6822	0.6479
0.1103	32.44	102000	2.4665	0.6765	0.6426
0.1103	32.6	102500	2.4642	0.6777	0.6431
0.118	32.76	103000	2.4628	0.6771	0.6451
0.118	32.92	103500	2.4671	0.6835	0.6474
0.1214	33.08	104000	2.4613	0.6771	0.6503
0.1214	33.24	104500	2.4833	0.6771	0.6475
0.0965	33.4	105000	2.4888	0.6803	0.6450
0.0965	33.56	105500	2.4910	0.6816	0.6476
0.1207	33.72	106000	2.4806	0.6860	0.6482
0.1207	33.87	106500	2.4741	0.6771	0.6445
0.1277	34.03	107000	2.5050	0.6790	0.6409
0.1277	34.19	107500	2.4809	0.6777	0.6402
0.1164	34.35	108000	2.5006	0.6777	0.6428
0.1164	34.51	108500	2.4889	0.6822	0.6474
0.1103	34.67	109000	2.4852	0.6822	0.6457
0.1103	34.83	109500	2.4923	0.6771	0.6418
0.1013	34.99	110000	2.4662	0.6784	0.6437
0.1013	35.15	110500	2.4755	0.6822	0.6483
0.0922	35.31	111000	2.4908	0.6816	0.6465
0.0922	35.46	111500	2.4922	0.6809	0.6502
0.0856	35.62	112000	2.5096	0.6828	0.6422
0.0856	35.78	112500	2.5035	0.6828	0.6463
0.1005	35.94	113000	2.5231	0.6828	0.6452
0.1005	36.1	113500	2.5196	0.6796	0.6469
0.0884	36.26	114000	2.5187	0.6796	0.6444
0.0884	36.42	114500	2.5180	0.6790	0.6454
0.0891	36.58	115000	2.5407	0.6771	0.6442
0.0891	36.74	115500	2.5349	0.6765	0.6417
0.1082	36.9	116000	2.5451	0.6777	0.6427
0.1082	37.05	116500	2.5349	0.6803	0.6469
0.1072	37.21	117000	2.5507	0.6816	0.6457
0.1072	37.37	117500	2.5485	0.6790	0.6459
0.0882	37.53	118000	2.5477	0.6809	0.6448
0.0882	37.69	118500	2.5620	0.6790	0.6401
0.0852	37.85	119000	2.5597	0.6790	0.6447
0.0852	38.01	119500	2.5545	0.6796	0.6436
0.1029	38.17	120000	2.5519	0.6796	0.6436
0.1029	38.33	120500	2.5539	0.6822	0.6463
0.0903	38.49	121000	2.5590	0.6822	0.6490
0.0903	38.65	121500	2.5658	0.6803	0.6457
0.092	38.8	122000	2.5590	0.6803	0.6433
0.092	38.96	122500	2.5620	0.6803	0.6449
0.094	39.12	123000	2.5634	0.6796	0.6436
0.094	39.28	123500	2.5677	0.6790	0.6435
0.0801	39.44	124000	2.5662	0.6803	0.6445
0.0801	39.6	124500	2.5648	0.6796	0.6440
0.103	39.76	125000	2.5641	0.6809	0.6451
0.103	39.92	125500	2.5641	0.6803	0.6446

Framework versions

• Transformers 4.29.2
• Pytorch 2.0.1+cu118
• Datasets 2.12.0
• Tokenizers 0.13.3