ASAP_FineTuningBERT_AugV6_k5_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: 0.6274
• Qwk: 0.4967
• Mse: 0.6276
• Rmse: 0.7922

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	11.7796	0.0036	11.7792	3.4321
No log	1.0	4	10.7895	0.0	10.7891	3.2847
No log	1.5	6	9.9070	0.0	9.9067	3.1475
No log	2.0	8	8.6438	0.0	8.6434	2.9400
7.8752	2.5	10	7.1567	0.0	7.1565	2.6752
7.8752	3.0	12	5.9365	0.0541	5.9363	2.4365
7.8752	3.5	14	4.8800	0.0039	4.8797	2.2090
7.8752	4.0	16	3.8396	0.0	3.8395	1.9595
7.8752	4.5	18	2.9717	0.0	2.9717	1.7239
3.5382	5.0	20	2.3057	0.1565	2.3057	1.5185
3.5382	5.5	22	1.7358	0.0411	1.7359	1.3175
3.5382	6.0	24	1.3227	0.0107	1.3229	1.1502
3.5382	6.5	26	1.1226	0.0107	1.1228	1.0596
3.5382	7.0	28	1.3254	0.0174	1.3255	1.1513
1.9038	7.5	30	1.2345	0.0174	1.2346	1.1111
1.9038	8.0	32	1.1509	0.0174	1.1511	1.0729
1.9038	8.5	34	0.9894	0.0174	0.9896	0.9948
1.9038	9.0	36	1.2860	0.0475	1.2862	1.1341
1.9038	9.5	38	1.3512	0.0863	1.3514	1.1625
1.7185	10.0	40	0.9662	0.1515	0.9664	0.9831
1.7185	10.5	42	1.0916	0.1833	1.0917	1.0449
1.7185	11.0	44	1.1903	0.2075	1.1904	1.0911
1.7185	11.5	46	0.8310	0.3444	0.8313	0.9117
1.7185	12.0	48	0.9477	0.3292	0.9477	0.9735
1.0569	12.5	50	0.7520	0.4112	0.7520	0.8672
1.0569	13.0	52	0.7206	0.4222	0.7207	0.8489
1.0569	13.5	54	0.8669	0.3525	0.8669	0.9310
1.0569	14.0	56	0.6472	0.4564	0.6474	0.8046
1.0569	14.5	58	0.7666	0.4004	0.7667	0.8756
0.4513	15.0	60	0.6800	0.4969	0.6802	0.8247
0.4513	15.5	62	0.8852	0.4013	0.8853	0.9409
0.4513	16.0	64	0.6905	0.4670	0.6907	0.8311
0.4513	16.5	66	0.8808	0.3921	0.8810	0.9386
0.4513	17.0	68	0.7145	0.4574	0.7148	0.8454
0.2643	17.5	70	1.1367	0.3339	1.1369	1.0663
0.2643	18.0	72	0.7711	0.4486	0.7714	0.8783
0.2643	18.5	74	0.8054	0.4241	0.8058	0.8976
0.2643	19.0	76	0.9591	0.3579	0.9594	0.9795
0.2643	19.5	78	0.7146	0.4560	0.7149	0.8455
0.186	20.0	80	0.9512	0.3647	0.9515	0.9754
0.186	20.5	82	0.7173	0.5003	0.7176	0.8471
0.186	21.0	84	0.7600	0.4691	0.7603	0.8720
0.186	21.5	86	0.8846	0.4086	0.8849	0.9407
0.186	22.0	88	0.7296	0.4873	0.7299	0.8543
0.1518	22.5	90	0.7952	0.4228	0.7954	0.8919
0.1518	23.0	92	0.6782	0.4887	0.6785	0.8237
0.1518	23.5	94	0.7797	0.4205	0.7799	0.8831
0.1518	24.0	96	0.7851	0.4315	0.7853	0.8862
0.1518	24.5	98	0.6927	0.4816	0.6929	0.8324
0.0978	25.0	100	0.8131	0.4224	0.8133	0.9018
0.0978	25.5	102	0.7242	0.4479	0.7244	0.8511
0.0978	26.0	104	0.6941	0.4600	0.6942	0.8332
0.0978	26.5	106	0.7039	0.4952	0.7041	0.8391
0.0978	27.0	108	0.7043	0.5064	0.7046	0.8394
0.0988	27.5	110	0.7174	0.4744	0.7177	0.8471
0.0988	28.0	112	0.6682	0.5070	0.6685	0.8176
0.0988	28.5	114	0.7541	0.4303	0.7543	0.8685
0.0988	29.0	116	0.6612	0.5053	0.6614	0.8133
0.0988	29.5	118	0.6959	0.4916	0.6961	0.8343
0.0938	30.0	120	0.6830	0.4813	0.6832	0.8266
0.0938	30.5	122	0.7379	0.4640	0.7380	0.8591
0.0938	31.0	124	0.6656	0.5018	0.6658	0.8160
0.0938	31.5	126	0.6744	0.4915	0.6745	0.8213
0.0938	32.0	128	0.6521	0.5140	0.6523	0.8077
0.0772	32.5	130	0.7119	0.4966	0.7120	0.8438
0.0772	33.0	132	0.6568	0.5071	0.6570	0.8106
0.0772	33.5	134	0.7118	0.4674	0.7119	0.8438
0.0772	34.0	136	0.6532	0.4947	0.6534	0.8083
0.0772	34.5	138	0.6425	0.4995	0.6427	0.8017
0.0722	35.0	140	0.7085	0.4656	0.7088	0.8419
0.0722	35.5	142	0.6605	0.5052	0.6608	0.8129
0.0722	36.0	144	0.6839	0.4956	0.6842	0.8271
0.0722	36.5	146	0.6333	0.5087	0.6336	0.7960
0.0722	37.0	148	0.6567	0.4797	0.6569	0.8105
0.0664	37.5	150	0.6350	0.5008	0.6352	0.7970
0.0664	38.0	152	0.6754	0.4908	0.6756	0.8219
0.0664	38.5	154	0.6798	0.5033	0.6801	0.8247
0.0664	39.0	156	0.7527	0.4662	0.7529	0.8677
0.0664	39.5	158	0.6856	0.5025	0.6858	0.8281
0.0589	40.0	160	0.6192	0.5407	0.6194	0.7870
0.0589	40.5	162	0.6467	0.5013	0.6469	0.8043
0.0589	41.0	164	0.8335	0.4597	0.8337	0.9131
0.0589	41.5	166	0.7050	0.5033	0.7052	0.8398
0.0589	42.0	168	0.6644	0.5238	0.6647	0.8153
0.0751	42.5	170	0.6647	0.5216	0.6649	0.8154
0.0751	43.0	172	0.7590	0.4529	0.7592	0.8713
0.0751	43.5	174	0.6592	0.5047	0.6594	0.8120
0.0751	44.0	176	0.6258	0.5316	0.6261	0.7912
0.0751	44.5	178	0.6741	0.4816	0.6743	0.8212
0.0617	45.0	180	0.6952	0.4969	0.6953	0.8339
0.0617	45.5	182	0.6637	0.5015	0.6639	0.8148
0.0617	46.0	184	0.7126	0.4911	0.7128	0.8443
0.0617	46.5	186	0.6529	0.5022	0.6531	0.8081
0.0617	47.0	188	0.6229	0.5097	0.6231	0.7894
0.0497	47.5	190	0.6895	0.4913	0.6896	0.8304
0.0497	48.0	192	0.6878	0.4936	0.6879	0.8294
0.0497	48.5	194	0.6306	0.5053	0.6308	0.7942
0.0497	49.0	196	0.6444	0.4818	0.6445	0.8028
0.0497	49.5	198	0.6434	0.4973	0.6436	0.8022
0.0515	50.0	200	0.6774	0.4872	0.6776	0.8231
0.0515	50.5	202	0.6536	0.5170	0.6538	0.8086
0.0515	51.0	204	0.6575	0.5170	0.6577	0.8110
0.0515	51.5	206	0.6424	0.5117	0.6426	0.8016
0.0515	52.0	208	0.6860	0.4718	0.6862	0.8284
0.0458	52.5	210	0.6461	0.4960	0.6463	0.8040
0.0458	53.0	212	0.6354	0.5047	0.6356	0.7973
0.0458	53.5	214	0.7264	0.4618	0.7266	0.8524
0.0458	54.0	216	0.6955	0.4697	0.6957	0.8341
0.0458	54.5	218	0.6320	0.4866	0.6322	0.7951
0.0495	55.0	220	0.6161	0.4927	0.6162	0.7850
0.0495	55.5	222	0.6227	0.4709	0.6228	0.7892
0.0495	56.0	224	0.5973	0.5120	0.5975	0.7730
0.0495	56.5	226	0.6262	0.4702	0.6263	0.7914
0.0495	57.0	228	0.6762	0.4796	0.6764	0.8224
0.0489	57.5	230	0.6357	0.4959	0.6359	0.7974
0.0489	58.0	232	0.6224	0.4895	0.6225	0.7890
0.0489	58.5	234	0.6086	0.4999	0.6088	0.7803
0.0489	59.0	236	0.6105	0.4838	0.6106	0.7814
0.0489	59.5	238	0.6231	0.4886	0.6233	0.7895
0.0466	60.0	240	0.6266	0.5194	0.6268	0.7917
0.0466	60.5	242	0.6446	0.4843	0.6448	0.8030
0.0466	61.0	244	0.6321	0.5010	0.6323	0.7952
0.0466	61.5	246	0.6176	0.4989	0.6178	0.7860
0.0466	62.0	248	0.6011	0.5112	0.6014	0.7755
0.0428	62.5	250	0.6130	0.5066	0.6132	0.7831
0.0428	63.0	252	0.6183	0.5135	0.6185	0.7865
0.0428	63.5	254	0.6101	0.5299	0.6103	0.7812
0.0428	64.0	256	0.6261	0.5088	0.6262	0.7913
0.0428	64.5	258	0.6253	0.5091	0.6254	0.7908
0.0439	65.0	260	0.5917	0.5182	0.5919	0.7693
0.0439	65.5	262	0.5988	0.5227	0.5989	0.7739
0.0439	66.0	264	0.6455	0.4932	0.6456	0.8035
0.0439	66.5	266	0.6786	0.4835	0.6786	0.8238
0.0439	67.0	268	0.6251	0.4872	0.6252	0.7907
0.0503	67.5	270	0.6234	0.5367	0.6236	0.7897
0.0503	68.0	272	0.6247	0.5075	0.6249	0.7905
0.0503	68.5	274	0.6861	0.4807	0.6863	0.8284
0.0503	69.0	276	0.7266	0.4619	0.7267	0.8525
0.0503	69.5	278	0.6527	0.4804	0.6528	0.8080
0.0516	70.0	280	0.6006	0.5161	0.6008	0.7751
0.0516	70.5	282	0.6024	0.5049	0.6026	0.7763
0.0516	71.0	284	0.6366	0.4903	0.6368	0.7980
0.0516	71.5	286	0.6388	0.4923	0.6390	0.7994
0.0516	72.0	288	0.6106	0.5203	0.6108	0.7816
0.0376	72.5	290	0.6085	0.4988	0.6087	0.7802
0.0376	73.0	292	0.6378	0.5013	0.6379	0.7987
0.0376	73.5	294	0.6414	0.5056	0.6415	0.8010
0.0376	74.0	296	0.6092	0.5038	0.6094	0.7806
0.0376	74.5	298	0.6144	0.4876	0.6146	0.7840
0.0353	75.0	300	0.6445	0.4790	0.6446	0.8029
0.0353	75.5	302	0.6474	0.4653	0.6476	0.8047
0.0353	76.0	304	0.6188	0.4924	0.6190	0.7868
0.0353	76.5	306	0.6144	0.4864	0.6146	0.7840
0.0353	77.0	308	0.6160	0.4898	0.6162	0.7850
0.039	77.5	310	0.6201	0.4831	0.6203	0.7876
0.039	78.0	312	0.6108	0.4807	0.6110	0.7816
0.039	78.5	314	0.6113	0.4883	0.6115	0.7820
0.039	79.0	316	0.6165	0.5003	0.6167	0.7853
0.039	79.5	318	0.6328	0.4957	0.6330	0.7956
0.0312	80.0	320	0.6177	0.4998	0.6179	0.7861
0.0312	80.5	322	0.6178	0.5127	0.6180	0.7861
0.0312	81.0	324	0.6331	0.5011	0.6332	0.7958
0.0312	81.5	326	0.6579	0.4918	0.6581	0.8112
0.0312	82.0	328	0.6403	0.4971	0.6405	0.8003
0.0333	82.5	330	0.6143	0.4959	0.6145	0.7839
0.0333	83.0	332	0.6153	0.5027	0.6154	0.7845
0.0333	83.5	334	0.6359	0.5092	0.6360	0.7975
0.0333	84.0	336	0.6306	0.4942	0.6308	0.7942
0.0333	84.5	338	0.6131	0.4961	0.6132	0.7831
0.0324	85.0	340	0.6139	0.5102	0.6141	0.7836
0.0324	85.5	342	0.6230	0.4965	0.6232	0.7894
0.0324	86.0	344	0.6462	0.4938	0.6464	0.8040
0.0324	86.5	346	0.6454	0.4938	0.6455	0.8034
0.0324	87.0	348	0.6248	0.4944	0.6250	0.7906
0.0326	87.5	350	0.6132	0.4995	0.6134	0.7832
0.0326	88.0	352	0.6134	0.5072	0.6136	0.7833
0.0326	88.5	354	0.6208	0.4973	0.6210	0.7880
0.0326	89.0	356	0.6344	0.5005	0.6346	0.7966
0.0326	89.5	358	0.6314	0.4991	0.6316	0.7947
0.03	90.0	360	0.6308	0.4982	0.6309	0.7943
0.03	90.5	362	0.6266	0.4968	0.6268	0.7917
0.03	91.0	364	0.6173	0.4988	0.6175	0.7858
0.03	91.5	366	0.6110	0.5003	0.6111	0.7817
0.03	92.0	368	0.6132	0.5013	0.6134	0.7832
0.0308	92.5	370	0.6212	0.4953	0.6214	0.7883
0.0308	93.0	372	0.6322	0.4995	0.6323	0.7952
0.0308	93.5	374	0.6306	0.5010	0.6307	0.7942
0.0308	94.0	376	0.6202	0.4929	0.6204	0.7877
0.0308	94.5	378	0.6129	0.4984	0.6130	0.7830
0.0302	95.0	380	0.6129	0.4959	0.6131	0.7830
0.0302	95.5	382	0.6159	0.5023	0.6160	0.7849
0.0302	96.0	384	0.6174	0.5032	0.6176	0.7859
0.0302	96.5	386	0.6169	0.4998	0.6171	0.7856
0.0302	97.0	388	0.6194	0.4983	0.6195	0.7871
0.0312	97.5	390	0.6225	0.5012	0.6226	0.7891
0.0312	98.0	392	0.6255	0.4954	0.6256	0.7910
0.0312	98.5	394	0.6266	0.4967	0.6268	0.7917
0.0312	99.0	396	0.6275	0.4967	0.6277	0.7923
0.0312	99.5	398	0.6274	0.4967	0.6275	0.7922
0.0276	100.0	400	0.6274	0.4967	0.6276	0.7922

Framework versions

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