ASAP_FineTuningBERT_AugV6_k4_task1_organization_fold2

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: 0.7637
• Qwk: 0.3819
• Mse: 0.7637
• Rmse: 0.8739

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	11.8302	0.0021	11.8298	3.4394
No log	1.0	4	10.7486	0.0	10.7482	3.2784
No log	1.5	6	9.8402	0.0	9.8400	3.1369
No log	2.0	8	8.4365	0.0	8.4362	2.9045
7.6527	2.5	10	6.9563	0.0	6.9561	2.6374
7.6527	3.0	12	5.8270	0.0345	5.8268	2.4139
7.6527	3.5	14	4.6772	0.0039	4.6770	2.1626
7.6527	4.0	16	3.6284	0.0	3.6283	1.9048
7.6527	4.5	18	2.7715	0.0	2.7715	1.6648
3.4117	5.0	20	2.0881	0.0551	2.0882	1.4451
3.4117	5.5	22	1.5830	0.0107	1.5831	1.2582
3.4117	6.0	24	1.2673	0.0107	1.2675	1.1258
3.4117	6.5	26	1.1839	0.0	1.1841	1.0882
3.4117	7.0	28	1.9866	0.0	1.9868	1.4095
1.9935	7.5	30	1.3297	0.0	1.3299	1.1532
1.9935	8.0	32	1.2329	0.0107	1.2331	1.1104
1.9935	8.5	34	1.4651	0.0174	1.4653	1.2105
1.9935	9.0	36	1.2988	0.0174	1.2990	1.1397
1.9935	9.5	38	1.4868	0.0345	1.4870	1.2194
1.8715	10.0	40	1.5889	0.0345	1.5890	1.2606
1.8715	10.5	42	1.5801	0.0602	1.5803	1.2571
1.8715	11.0	44	1.1010	0.0408	1.1013	1.0494
1.8715	11.5	46	1.3569	0.0159	1.3571	1.1649
1.8715	12.0	48	0.7566	0.3412	0.7570	0.8700
1.5929	12.5	50	1.0997	0.0280	1.1001	1.0488
1.5929	13.0	52	2.2930	0.0518	2.2932	1.5143
1.5929	13.5	54	2.3062	0.0394	2.3064	1.5187
1.5929	14.0	56	1.2169	0.1032	1.2173	1.1033
1.5929	14.5	58	0.7032	0.4176	0.7035	0.8387
1.6354	15.0	60	1.9428	-0.0527	1.9431	1.3940
1.6354	15.5	62	2.3480	-0.0920	2.3482	1.5324
1.6354	16.0	64	1.1002	0.2756	1.1007	1.0491
1.6354	16.5	66	1.1760	0.3616	1.1765	1.0847
1.6354	17.0	68	0.9830	0.4178	0.9836	0.9918
2.0859	17.5	70	1.1832	0.1758	1.1836	1.0879
2.0859	18.0	72	1.6468	-0.0123	1.6468	1.2833
2.0859	18.5	74	0.8232	0.2843	0.8235	0.9075
2.0859	19.0	76	0.5977	0.4353	0.5980	0.7733
2.0859	19.5	78	0.6304	0.4421	0.6307	0.7942
1.249	20.0	80	1.0110	0.2391	1.0111	1.0056
1.249	20.5	82	1.1903	0.2223	1.1905	1.0911
1.249	21.0	84	0.9427	0.3816	0.9430	0.9711
1.249	21.5	86	0.7643	0.4479	0.7646	0.8744
1.249	22.0	88	0.6730	0.4536	0.6733	0.8205
0.9046	22.5	90	0.8263	0.3062	0.8266	0.9092
0.9046	23.0	92	0.7709	0.3092	0.7713	0.8782
0.9046	23.5	94	0.7953	0.2980	0.7956	0.8919
0.9046	24.0	96	0.8686	0.2728	0.8689	0.9321
0.9046	24.5	98	0.8495	0.3190	0.8497	0.9218
0.5309	25.0	100	0.7382	0.4294	0.7384	0.8593
0.5309	25.5	102	0.6997	0.4921	0.6999	0.8366
0.5309	26.0	104	0.7993	0.3578	0.7995	0.8942
0.5309	26.5	106	0.7316	0.4213	0.7319	0.8555
0.5309	27.0	108	0.8280	0.3447	0.8283	0.9101
0.2881	27.5	110	1.2642	0.0924	1.2648	1.1246
0.2881	28.0	112	1.3910	0.0684	1.3915	1.1796
0.2881	28.5	114	1.0009	0.2916	1.0014	1.0007
0.2881	29.0	116	0.9492	0.3298	0.9495	0.9744
0.2881	29.5	118	1.0745	0.1913	1.0748	1.0367
0.2969	30.0	120	1.1378	0.1243	1.1381	1.0668
0.2969	30.5	122	0.7867	0.4007	0.7868	0.8870
0.2969	31.0	124	0.8099	0.4017	0.8101	0.9000
0.2969	31.5	126	1.0417	0.1921	1.0418	1.0207
0.2969	32.0	128	1.2135	0.0955	1.2137	1.1017
0.2407	32.5	130	0.9350	0.3050	0.9353	0.9671
0.2407	33.0	132	0.8947	0.2702	0.8950	0.9460
0.2407	33.5	134	0.8952	0.2541	0.8956	0.9463
0.2407	34.0	136	0.9473	0.2366	0.9476	0.9735
0.2407	34.5	138	0.9133	0.3338	0.9137	0.9559
0.1711	35.0	140	1.0796	0.1732	1.0799	1.0392
0.1711	35.5	142	1.0168	0.2085	1.0170	1.0085
0.1711	36.0	144	0.7683	0.4428	0.7684	0.8766
0.1711	36.5	146	0.6951	0.5256	0.6951	0.8337
0.1711	37.0	148	0.7177	0.4502	0.7178	0.8472
0.2265	37.5	150	1.1132	0.2181	1.1133	1.0551
0.2265	38.0	152	1.0000	0.2281	1.0002	1.0001
0.2265	38.5	154	0.8489	0.3848	0.8490	0.9214
0.2265	39.0	156	0.9038	0.3858	0.9039	0.9507
0.2265	39.5	158	0.7698	0.4008	0.7699	0.8774
0.2451	40.0	160	0.9791	0.2347	0.9793	0.9896
0.2451	40.5	162	1.0043	0.2348	1.0045	1.0022
0.2451	41.0	164	0.7280	0.3583	0.7281	0.8533
0.2451	41.5	166	0.7025	0.4180	0.7027	0.8382
0.2451	42.0	168	0.8227	0.3565	0.8228	0.9071
0.1697	42.5	170	0.8452	0.3844	0.8453	0.9194
0.1697	43.0	172	0.8372	0.3261	0.8374	0.9151
0.1697	43.5	174	0.8432	0.2791	0.8434	0.9184
0.1697	44.0	176	0.7817	0.3188	0.7819	0.8842
0.1697	44.5	178	0.7810	0.3486	0.7811	0.8838
0.1164	45.0	180	0.9276	0.2978	0.9278	0.9632
0.1164	45.5	182	0.8470	0.3670	0.8471	0.9204
0.1164	46.0	184	0.7819	0.4767	0.7819	0.8842
0.1164	46.5	186	0.7792	0.4484	0.7792	0.8827
0.1164	47.0	188	0.9128	0.3275	0.9129	0.9555
0.1317	47.5	190	0.7927	0.3921	0.7929	0.8904
0.1317	48.0	192	0.6546	0.5228	0.6545	0.8090
0.1317	48.5	194	0.6632	0.4884	0.6631	0.8143
0.1317	49.0	196	0.7379	0.3741	0.7379	0.8590
0.1317	49.5	198	0.8140	0.3052	0.8140	0.9022
0.1049	50.0	200	0.8063	0.3761	0.8063	0.8979
0.1049	50.5	202	0.8126	0.2873	0.8128	0.9015
0.1049	51.0	204	0.7634	0.3520	0.7636	0.8738
0.1049	51.5	206	0.6858	0.4189	0.6859	0.8282
0.1049	52.0	208	0.7176	0.3807	0.7177	0.8472
0.0825	52.5	210	0.6776	0.4371	0.6777	0.8232
0.0825	53.0	212	0.6976	0.4544	0.6977	0.8353
0.0825	53.5	214	0.7213	0.4287	0.7215	0.8494
0.0825	54.0	216	0.7235	0.3904	0.7237	0.8507
0.0825	54.5	218	0.7252	0.3912	0.7254	0.8517
0.0891	55.0	220	0.8191	0.3129	0.8193	0.9051
0.0891	55.5	222	0.8950	0.3029	0.8952	0.9462
0.0891	56.0	224	0.7975	0.3948	0.7977	0.8932
0.0891	56.5	226	0.7351	0.4252	0.7354	0.8575
0.0891	57.0	228	0.8598	0.3849	0.8601	0.9274
0.0805	57.5	230	0.8789	0.3456	0.8792	0.9377
0.0805	58.0	232	0.7617	0.3729	0.7619	0.8729
0.0805	58.5	234	0.7690	0.3854	0.7692	0.8770
0.0805	59.0	236	0.7919	0.3724	0.7921	0.8900
0.0805	59.5	238	0.7904	0.3473	0.7906	0.8892
0.074	60.0	240	0.7524	0.4082	0.7526	0.8675
0.074	60.5	242	0.7594	0.3680	0.7596	0.8715
0.074	61.0	244	0.7646	0.3866	0.7648	0.8745
0.074	61.5	246	0.7686	0.3936	0.7687	0.8768
0.074	62.0	248	0.7418	0.4222	0.7420	0.8614
0.052	62.5	250	0.7584	0.3663	0.7586	0.8710
0.052	63.0	252	0.7950	0.3290	0.7951	0.8917
0.052	63.5	254	0.7858	0.3543	0.7859	0.8865
0.052	64.0	256	0.8036	0.3301	0.8038	0.8965
0.052	64.5	258	0.8467	0.2857	0.8469	0.9203
0.0634	65.0	260	0.8159	0.3099	0.8161	0.9034
0.0634	65.5	262	0.8003	0.3413	0.8005	0.8947
0.0634	66.0	264	0.8080	0.3172	0.8082	0.8990
0.0634	66.5	266	0.8872	0.2737	0.8875	0.9421
0.0634	67.0	268	0.8473	0.3207	0.8475	0.9206
0.0767	67.5	270	0.7511	0.3872	0.7512	0.8667
0.0767	68.0	272	0.7229	0.4301	0.7230	0.8503
0.0767	68.5	274	0.7389	0.3977	0.7390	0.8597
0.0767	69.0	276	0.8349	0.3451	0.8352	0.9139
0.0767	69.5	278	0.8055	0.3643	0.8058	0.8977
0.0748	70.0	280	0.7068	0.4262	0.7070	0.8408
0.0748	70.5	282	0.6923	0.4628	0.6925	0.8322
0.0748	71.0	284	0.7273	0.3912	0.7275	0.8529
0.0748	71.5	286	0.8454	0.3422	0.8457	0.9196
0.0748	72.0	288	0.8221	0.3503	0.8224	0.9068
0.0776	72.5	290	0.7182	0.3926	0.7184	0.8476
0.0776	73.0	292	0.7256	0.4571	0.7256	0.8518
0.0776	73.5	294	0.7665	0.4084	0.7665	0.8755
0.0776	74.0	296	0.8398	0.3020	0.8400	0.9165
0.0776	74.5	298	0.9030	0.2605	0.9032	0.9504
0.0763	75.0	300	0.8648	0.2753	0.8650	0.9300
0.0763	75.5	302	0.8098	0.3262	0.8099	0.8999
0.0763	76.0	304	0.7794	0.3730	0.7794	0.8828
0.0763	76.5	306	0.7440	0.3505	0.7441	0.8626
0.0763	77.0	308	0.7619	0.3469	0.7620	0.8729
0.0634	77.5	310	0.7525	0.3523	0.7526	0.8675
0.0634	78.0	312	0.7431	0.3697	0.7432	0.8621
0.0634	78.5	314	0.7310	0.3951	0.7310	0.8550
0.0634	79.0	316	0.7420	0.3562	0.7421	0.8615
0.0634	79.5	318	0.7676	0.3467	0.7677	0.8762
0.0482	80.0	320	0.7433	0.3671	0.7434	0.8622
0.0482	80.5	322	0.7353	0.3914	0.7354	0.8576
0.0482	81.0	324	0.7239	0.3953	0.7240	0.8509
0.0482	81.5	326	0.7313	0.3956	0.7314	0.8552
0.0482	82.0	328	0.7690	0.3592	0.7692	0.8770
0.048	82.5	330	0.7665	0.3620	0.7667	0.8756
0.048	83.0	332	0.7370	0.3974	0.7371	0.8585
0.048	83.5	334	0.7382	0.3907	0.7383	0.8592
0.048	84.0	336	0.7461	0.3858	0.7462	0.8638
0.048	84.5	338	0.7673	0.3606	0.7674	0.8760
0.0506	85.0	340	0.7856	0.3400	0.7857	0.8864
0.0506	85.5	342	0.7770	0.3430	0.7771	0.8815
0.0506	86.0	344	0.7729	0.3582	0.7730	0.8792
0.0506	86.5	346	0.7838	0.3541	0.7838	0.8853
0.0506	87.0	348	0.8019	0.3404	0.8019	0.8955
0.0494	87.5	350	0.8067	0.3335	0.8068	0.8982
0.0494	88.0	352	0.7884	0.3471	0.7884	0.8879
0.0494	88.5	354	0.7757	0.3539	0.7757	0.8808
0.0494	89.0	356	0.7727	0.3597	0.7728	0.8791
0.0494	89.5	358	0.7866	0.3403	0.7867	0.8870
0.0499	90.0	360	0.8116	0.3126	0.8117	0.9009
0.0499	90.5	362	0.8121	0.3208	0.8122	0.9012
0.0499	91.0	364	0.7940	0.3442	0.7941	0.8911
0.0499	91.5	366	0.7836	0.3510	0.7837	0.8852
0.0499	92.0	368	0.7799	0.3484	0.7799	0.8831
0.0453	92.5	370	0.7772	0.3696	0.7773	0.8816
0.0453	93.0	372	0.7800	0.3684	0.7801	0.8832
0.0453	93.5	374	0.7902	0.3538	0.7903	0.8890
0.0453	94.0	376	0.7959	0.3457	0.7960	0.8922
0.0453	94.5	378	0.7937	0.3484	0.7937	0.8909
0.0505	95.0	380	0.7841	0.3563	0.7842	0.8856
0.0505	95.5	382	0.7758	0.3684	0.7758	0.8808
0.0505	96.0	384	0.7723	0.3738	0.7723	0.8788
0.0505	96.5	386	0.7708	0.3686	0.7708	0.8780
0.0505	97.0	388	0.7715	0.3729	0.7716	0.8784
0.0432	97.5	390	0.7715	0.3648	0.7716	0.8784
0.0432	98.0	392	0.7682	0.3647	0.7683	0.8765
0.0432	98.5	394	0.7650	0.3741	0.7651	0.8747
0.0432	99.0	396	0.7639	0.3819	0.7640	0.8741
0.0432	99.5	398	0.7636	0.3819	0.7636	0.8739
0.0424	100.0	400	0.7637	0.3819	0.7637	0.8739

Framework versions

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