ASAP_FineTuningBERT_AugV7_k4_task1_organization_fold1

This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.8814
• Qwk: 0.3974
• Mse: 0.8815
• Rmse: 0.9389

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: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Qwk	Mse	Rmse
No log	0.5	2	11.4438	0.0042	11.4412	3.3825
No log	1.0	4	10.3503	0.0	10.3478	3.2168
No log	1.5	6	9.3031	0.0	9.3009	3.0497
No log	2.0	8	7.9101	0.0	7.9082	2.8122
7.2479	2.5	10	6.5550	0.0	6.5535	2.5600
7.2479	3.0	12	5.2647	0.0285	5.2634	2.2942
7.2479	3.5	14	4.2545	0.0	4.2534	2.0624
7.2479	4.0	16	3.3659	0.0	3.3651	1.8344
7.2479	4.5	18	2.6599	0.0	2.6594	1.6308
3.1093	5.0	20	2.0080	0.0707	2.0078	1.4170
3.1093	5.5	22	1.5538	0.0	1.5538	1.2465
3.1093	6.0	24	1.2949	0.0	1.2950	1.1380
3.1093	6.5	26	1.2539	0.0106	1.2540	1.1198
3.1093	7.0	28	1.2717	0.0315	1.2718	1.1278
1.8606	7.5	30	1.5457	0.0707	1.5456	1.2432
1.8606	8.0	32	1.5601	0.0856	1.5599	1.2490
1.8606	8.5	34	1.3175	0.0834	1.3175	1.1478
1.8606	9.0	36	1.0897	0.0755	1.0897	1.0439
1.8606	9.5	38	1.3559	0.1160	1.3557	1.1644
1.6	10.0	40	1.7347	0.1355	1.7343	1.3169
1.6	10.5	42	1.3620	0.1553	1.3617	1.1669
1.6	11.0	44	1.4967	0.1604	1.4963	1.2232
1.6	11.5	46	1.2538	0.1706	1.2535	1.1196
1.6	12.0	48	1.6601	0.1390	1.6596	1.2882
0.8183	12.5	50	1.1489	0.2105	1.1488	1.0718
0.8183	13.0	52	1.1569	0.2114	1.1568	1.0756
0.8183	13.5	54	1.7648	0.1735	1.7642	1.3282
0.8183	14.0	56	2.0464	0.1432	2.0457	1.4303
0.8183	14.5	58	0.9365	0.3326	0.9367	0.9678
0.4176	15.0	60	0.9299	0.3300	0.9300	0.9644
0.4176	15.5	62	1.7464	0.1409	1.7460	1.3214
0.4176	16.0	64	1.6213	0.1537	1.6210	1.2732
0.4176	16.5	66	1.1405	0.3148	1.1407	1.0680
0.4176	17.0	68	1.8041	0.1414	1.8035	1.3429
0.3106	17.5	70	1.3723	0.2134	1.3719	1.1713
0.3106	18.0	72	0.8351	0.4337	0.8352	0.9139
0.3106	18.5	74	1.1318	0.2855	1.1316	1.0638
0.3106	19.0	76	1.5923	0.2225	1.5918	1.2617
0.3106	19.5	78	0.9293	0.4043	0.9293	0.9640
0.2442	20.0	80	0.9727	0.3835	0.9728	0.9863
0.2442	20.5	82	1.5183	0.2280	1.5179	1.2320
0.2442	21.0	84	1.0668	0.3316	1.0669	1.0329
0.2442	21.5	86	0.8292	0.3750	0.8296	0.9108
0.2442	22.0	88	1.2191	0.2567	1.2189	1.1041
0.1934	22.5	90	1.2054	0.2638	1.2052	1.0978
0.1934	23.0	92	0.7450	0.4063	0.7454	0.8634
0.1934	23.5	94	0.7587	0.4352	0.7592	0.8713
0.1934	24.0	96	1.0230	0.3465	1.0230	1.0114
0.1934	24.5	98	1.8734	0.1560	1.8727	1.3685
0.2221	25.0	100	1.4741	0.2191	1.4737	1.2140
0.2221	25.5	102	0.8684	0.4156	0.8686	0.9320
0.2221	26.0	104	0.9371	0.3966	0.9371	0.9680
0.2221	26.5	106	1.2128	0.2950	1.2126	1.1012
0.2221	27.0	108	0.8754	0.4169	0.8754	0.9357
0.1269	27.5	110	0.8889	0.4265	0.8890	0.9428
0.1269	28.0	112	1.2094	0.3014	1.2092	1.0996
0.1269	28.5	114	0.9748	0.3905	0.9747	0.9873
0.1269	29.0	116	0.7708	0.4482	0.7711	0.8781
0.1269	29.5	118	0.8650	0.4283	0.8652	0.9301
0.1201	30.0	120	1.2839	0.2721	1.2838	1.1330
0.1201	30.5	122	1.0733	0.3326	1.0733	1.0360
0.1201	31.0	124	0.7966	0.3995	0.7970	0.8928
0.1201	31.5	126	0.8889	0.3985	0.8892	0.9430
0.1201	32.0	128	1.1738	0.2598	1.1738	1.0834
0.1058	32.5	130	0.9951	0.3536	0.9952	0.9976
0.1058	33.0	132	0.8009	0.4133	0.8014	0.8952
0.1058	33.5	134	0.9207	0.4190	0.9210	0.9597
0.1058	34.0	136	1.7062	0.1843	1.7059	1.3061
0.1058	34.5	138	2.0024	0.1645	2.0019	1.4149
0.174	35.0	140	1.3458	0.2437	1.3456	1.1600
0.174	35.5	142	0.7648	0.4476	0.7652	0.8747
0.174	36.0	144	0.7345	0.4231	0.7349	0.8573
0.174	36.5	146	0.8508	0.4050	0.8510	0.9225
0.174	37.0	148	1.4894	0.2240	1.4891	1.2203
0.1542	37.5	150	1.7034	0.1706	1.7029	1.3050
0.1542	38.0	152	1.2120	0.2892	1.2119	1.1009
0.1542	38.5	154	0.8236	0.3994	0.8240	0.9077
0.1542	39.0	156	0.7938	0.4111	0.7942	0.8912
0.1542	39.5	158	0.9900	0.3290	0.9900	0.9950
0.1251	40.0	160	1.0983	0.3074	1.0983	1.0480
0.1251	40.5	162	0.9594	0.3885	0.9595	0.9796
0.1251	41.0	164	0.8959	0.4125	0.8961	0.9466
0.1251	41.5	166	0.9146	0.4074	0.9146	0.9564
0.1251	42.0	168	1.0456	0.3457	1.0455	1.0225
0.0956	42.5	170	0.9079	0.3701	0.9079	0.9528
0.0956	43.0	172	0.7763	0.4483	0.7765	0.8812
0.0956	43.5	174	0.8514	0.4128	0.8516	0.9228
0.0956	44.0	176	1.0969	0.3315	1.0968	1.0473
0.0956	44.5	178	1.0107	0.3354	1.0107	1.0053
0.0815	45.0	180	0.8340	0.3824	0.8341	0.9133
0.0815	45.5	182	0.8730	0.3496	0.8731	0.9344
0.0815	46.0	184	0.9856	0.3326	0.9857	0.9928
0.0815	46.5	186	0.9184	0.3894	0.9186	0.9584
0.0815	47.0	188	0.8287	0.4140	0.8291	0.9106
0.0786	47.5	190	0.8698	0.4071	0.8701	0.9328
0.0786	48.0	192	1.2889	0.2561	1.2887	1.1352
0.0786	48.5	194	1.5184	0.1898	1.5181	1.2321
0.0786	49.0	196	1.2225	0.2854	1.2224	1.1056
0.0786	49.5	198	0.8145	0.4072	0.8147	0.9026
0.1315	50.0	200	0.7504	0.4603	0.7507	0.8664
0.1315	50.5	202	0.8473	0.4236	0.8475	0.9206
0.1315	51.0	204	1.1674	0.3293	1.1672	1.0804
0.1315	51.5	206	1.1196	0.3370	1.1195	1.0581
0.1315	52.0	208	0.8521	0.4147	0.8522	0.9231
0.0809	52.5	210	0.8219	0.4104	0.8220	0.9066
0.0809	53.0	212	0.9839	0.3590	0.9839	0.9919
0.0809	53.5	214	1.0200	0.3583	1.0200	1.0100
0.0809	54.0	216	0.9340	0.4197	0.9341	0.9665
0.0809	54.5	218	0.9194	0.3964	0.9196	0.9589
0.0697	55.0	220	0.9730	0.3425	0.9731	0.9864
0.0697	55.5	222	1.0269	0.3295	1.0269	1.0134
0.0697	56.0	224	1.0363	0.3426	1.0363	1.0180
0.0697	56.5	226	0.8981	0.3966	0.8984	0.9478
0.0697	57.0	228	0.8608	0.3964	0.8610	0.9279
0.0754	57.5	230	0.9924	0.3387	0.9925	0.9962
0.0754	58.0	232	0.9927	0.3330	0.9928	0.9964
0.0754	58.5	234	0.8822	0.3766	0.8824	0.9394
0.0754	59.0	236	0.9470	0.3527	0.9471	0.9732
0.0754	59.5	238	0.9967	0.3584	0.9968	0.9984
0.0619	60.0	240	1.0748	0.3408	1.0747	1.0367
0.0619	60.5	242	1.1110	0.3309	1.1109	1.0540
0.0619	61.0	244	0.9189	0.3762	0.9190	0.9586
0.0619	61.5	246	0.7638	0.4380	0.7641	0.8741
0.0619	62.0	248	0.7647	0.4291	0.7649	0.8746
0.072	62.5	250	0.8295	0.4065	0.8297	0.9109
0.072	63.0	252	0.8926	0.4025	0.8927	0.9448
0.072	63.5	254	1.0420	0.3386	1.0421	1.0208
0.072	64.0	256	1.0383	0.3362	1.0384	1.0190
0.072	64.5	258	0.9242	0.3922	0.9245	0.9615
0.0557	65.0	260	0.8637	0.3942	0.8640	0.9295
0.0557	65.5	262	0.8460	0.3978	0.8463	0.9200
0.0557	66.0	264	0.8714	0.4056	0.8716	0.9336
0.0557	66.5	266	0.9281	0.3867	0.9283	0.9635
0.0557	67.0	268	0.9401	0.3831	0.9402	0.9696
0.0541	67.5	270	0.8558	0.4016	0.8560	0.9252
0.0541	68.0	272	0.7707	0.4410	0.7709	0.8780
0.0541	68.5	274	0.8027	0.4215	0.8029	0.8960
0.0541	69.0	276	0.8937	0.3973	0.8938	0.9454
0.0541	69.5	278	0.9158	0.3936	0.9159	0.9570
0.0522	70.0	280	0.8935	0.3937	0.8937	0.9453
0.0522	70.5	282	0.9169	0.3955	0.9170	0.9576
0.0522	71.0	284	0.8652	0.3910	0.8653	0.9302
0.0522	71.5	286	0.8408	0.3915	0.8410	0.9171
0.0522	72.0	288	0.8530	0.3833	0.8532	0.9237
0.0639	72.5	290	0.8591	0.3952	0.8593	0.9270
0.0639	73.0	292	0.8759	0.3871	0.8760	0.9360
0.0639	73.5	294	0.8743	0.4161	0.8745	0.9351
0.0639	74.0	296	0.8809	0.4051	0.8810	0.9386
0.0639	74.5	298	0.8962	0.4037	0.8963	0.9467
0.0553	75.0	300	0.8289	0.4106	0.8290	0.9105
0.0553	75.5	302	0.7921	0.4169	0.7923	0.8901
0.0553	76.0	304	0.7871	0.4281	0.7872	0.8873
0.0553	76.5	306	0.8255	0.4218	0.8257	0.9087
0.0553	77.0	308	0.9022	0.3969	0.9023	0.9499
0.0476	77.5	310	0.8894	0.3973	0.8896	0.9432
0.0476	78.0	312	0.8496	0.4166	0.8498	0.9219
0.0476	78.5	314	0.8279	0.4093	0.8281	0.9100
0.0476	79.0	316	0.8439	0.3999	0.8441	0.9187
0.0476	79.5	318	0.8906	0.3954	0.8907	0.9438
0.0463	80.0	320	0.9191	0.3907	0.9192	0.9588
0.0463	80.5	322	0.9063	0.3905	0.9064	0.9520
0.0463	81.0	324	0.9404	0.3945	0.9405	0.9698
0.0463	81.5	326	0.9639	0.3748	0.9639	0.9818
0.0463	82.0	328	0.9336	0.3826	0.9336	0.9663
0.0464	82.5	330	0.8542	0.4083	0.8544	0.9243
0.0464	83.0	332	0.8126	0.4108	0.8128	0.9015
0.0464	83.5	334	0.8333	0.4103	0.8335	0.9130
0.0464	84.0	336	0.8985	0.3885	0.8986	0.9479
0.0464	84.5	338	0.9361	0.3828	0.9362	0.9676
0.0543	85.0	340	0.9329	0.3813	0.9329	0.9659
0.0543	85.5	342	0.9034	0.4034	0.9035	0.9505
0.0543	86.0	344	0.9030	0.4034	0.9031	0.9503
0.0543	86.5	346	0.8696	0.4043	0.8697	0.9326
0.0543	87.0	348	0.8176	0.4366	0.8178	0.9043
0.0467	87.5	350	0.8044	0.4249	0.8046	0.8970
0.0467	88.0	352	0.8301	0.4159	0.8303	0.9112
0.0467	88.5	354	0.8918	0.4013	0.8918	0.9444
0.0467	89.0	356	0.9411	0.3744	0.9411	0.9701
0.0467	89.5	358	0.9559	0.3649	0.9559	0.9777
0.049	90.0	360	0.9224	0.3895	0.9225	0.9604
0.049	90.5	362	0.8714	0.4009	0.8715	0.9335
0.049	91.0	364	0.8216	0.4090	0.8218	0.9065
0.049	91.5	366	0.8062	0.4237	0.8064	0.8980
0.049	92.0	368	0.8139	0.4216	0.8140	0.9022
0.0449	92.5	370	0.8402	0.4145	0.8404	0.9167
0.0449	93.0	372	0.8699	0.4003	0.8700	0.9328
0.0449	93.5	374	0.8684	0.4004	0.8685	0.9320
0.0449	94.0	376	0.8741	0.3946	0.8742	0.9350
0.0449	94.5	378	0.8699	0.4026	0.8700	0.9327
0.05	95.0	380	0.8615	0.4047	0.8617	0.9283
0.05	95.5	382	0.8537	0.4097	0.8538	0.9240
0.05	96.0	384	0.8534	0.4068	0.8535	0.9239
0.05	96.5	386	0.8565	0.4068	0.8566	0.9255
0.05	97.0	388	0.8650	0.4039	0.8651	0.9301
0.0421	97.5	390	0.8718	0.3958	0.8719	0.9338
0.0421	98.0	392	0.8732	0.3958	0.8733	0.9345
0.0421	98.5	394	0.8760	0.3958	0.8761	0.9360
0.0421	99.0	396	0.8794	0.3966	0.8795	0.9378
0.0421	99.5	398	0.8806	0.3974	0.8807	0.9385
0.043	100.0	400	0.8814	0.3974	0.8815	0.9389

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.1+cu121
• Datasets 3.2.0
• Tokenizers 0.19.1