ASAP_FineTuningBERT_AugV7_k4_task1_organization_fold0

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: 0.7431
• Qwk: 0.5252
• Mse: 0.7431
• Rmse: 0.8620

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	9.0634	0.0	9.0634	3.0106
No log	1.0	4	8.0132	0.0	8.0132	2.8308
No log	1.5	6	6.8230	0.0	6.8230	2.6121
No log	2.0	8	6.2324	-0.0089	6.2324	2.4965
5.5349	2.5	10	5.3248	0.0112	5.3248	2.3076
5.5349	3.0	12	4.5377	0.0039	4.5377	2.1302
5.5349	3.5	14	3.7792	0.0039	3.7792	1.9440
5.5349	4.0	16	3.0361	0.0	3.0361	1.7424
5.5349	4.5	18	2.3895	0.0977	2.3895	1.5458
2.9454	5.0	20	1.9100	0.0689	1.9100	1.3820
2.9454	5.5	22	1.4211	0.0316	1.4211	1.1921
2.9454	6.0	24	1.1894	0.0316	1.1894	1.0906
2.9454	6.5	26	1.1176	0.0316	1.1176	1.0572
2.9454	7.0	28	1.1965	0.0316	1.1965	1.0938
1.9633	7.5	30	1.2519	0.0484	1.2519	1.1189
1.9633	8.0	32	1.2078	0.0484	1.2078	1.0990
1.9633	8.5	34	1.0599	0.0484	1.0599	1.0295
1.9633	9.0	36	0.9361	0.0484	0.9361	0.9675
1.9633	9.5	38	0.9773	0.1025	0.9773	0.9886
1.6806	10.0	40	0.8358	0.2954	0.8358	0.9142
1.6806	10.5	42	0.9783	0.2377	0.9783	0.9891
1.6806	11.0	44	1.1321	0.2148	1.1321	1.0640
1.6806	11.5	46	0.8477	0.3961	0.8477	0.9207
1.6806	12.0	48	0.6776	0.4589	0.6776	0.8232
1.4329	12.5	50	0.7722	0.4349	0.7722	0.8787
1.4329	13.0	52	0.8025	0.4433	0.8025	0.8958
1.4329	13.5	54	0.6982	0.4821	0.6982	0.8356
1.4329	14.0	56	0.6896	0.4858	0.6896	0.8304
1.4329	14.5	58	0.5996	0.4552	0.5996	0.7743
1.0133	15.0	60	0.6111	0.4549	0.6111	0.7818
1.0133	15.5	62	0.6131	0.5101	0.6131	0.7830
1.0133	16.0	64	0.5732	0.5066	0.5732	0.7571
1.0133	16.5	66	0.6529	0.4095	0.6529	0.8080
1.0133	17.0	68	0.6543	0.4183	0.6543	0.8089
0.491	17.5	70	0.5879	0.4895	0.5879	0.7668
0.491	18.0	72	0.6073	0.5225	0.6073	0.7793
0.491	18.5	74	0.6084	0.5235	0.6084	0.7800
0.491	19.0	76	0.6174	0.5369	0.6174	0.7857
0.491	19.5	78	0.5804	0.5813	0.5804	0.7619
0.2875	20.0	80	0.7718	0.4713	0.7718	0.8785
0.2875	20.5	82	0.7091	0.4648	0.7091	0.8421
0.2875	21.0	84	0.6413	0.5176	0.6413	0.8008
0.2875	21.5	86	0.6762	0.5247	0.6762	0.8223
0.2875	22.0	88	0.6504	0.5167	0.6504	0.8065
0.255	22.5	90	0.7184	0.5044	0.7184	0.8476
0.255	23.0	92	0.7347	0.5012	0.7347	0.8572
0.255	23.5	94	0.6777	0.5108	0.6777	0.8232
0.255	24.0	96	0.6983	0.5317	0.6983	0.8356
0.255	24.5	98	0.8410	0.4874	0.8410	0.9170
0.222	25.0	100	0.7253	0.5061	0.7253	0.8516
0.222	25.5	102	0.7849	0.4769	0.7849	0.8859
0.222	26.0	104	0.7016	0.5274	0.7016	0.8376
0.222	26.5	106	0.8062	0.4803	0.8062	0.8979
0.222	27.0	108	0.7850	0.4594	0.7850	0.8860
0.166	27.5	110	0.8080	0.4904	0.8080	0.8989
0.166	28.0	112	0.8104	0.4906	0.8104	0.9002
0.166	28.5	114	0.7438	0.5065	0.7438	0.8624
0.166	29.0	116	0.8609	0.4686	0.8609	0.9279
0.166	29.5	118	0.7379	0.5097	0.7379	0.8590
0.1506	30.0	120	0.7388	0.5189	0.7388	0.8595
0.1506	30.5	122	0.6749	0.5611	0.6749	0.8215
0.1506	31.0	124	0.6626	0.5311	0.6626	0.8140
0.1506	31.5	126	1.0541	0.3678	1.0541	1.0267
0.1506	32.0	128	1.3381	0.3163	1.3381	1.1568
0.2224	32.5	130	0.9900	0.4241	0.9900	0.9950
0.2224	33.0	132	0.7632	0.4802	0.7632	0.8736
0.2224	33.5	134	0.6957	0.4993	0.6957	0.8341
0.2224	34.0	136	0.6538	0.5185	0.6538	0.8086
0.2224	34.5	138	0.6726	0.5320	0.6726	0.8201
0.1158	35.0	140	0.6157	0.5513	0.6157	0.7846
0.1158	35.5	142	0.6387	0.5270	0.6387	0.7992
0.1158	36.0	144	0.7229	0.5225	0.7229	0.8503
0.1158	36.5	146	0.6941	0.5310	0.6941	0.8331
0.1158	37.0	148	0.9245	0.4763	0.9245	0.9615
0.1171	37.5	150	0.9023	0.4584	0.9023	0.9499
0.1171	38.0	152	0.8765	0.4823	0.8765	0.9362
0.1171	38.5	154	0.7933	0.5046	0.7933	0.8907
0.1171	39.0	156	0.8029	0.4775	0.8029	0.8960
0.1171	39.5	158	0.9432	0.4152	0.9432	0.9712
0.1247	40.0	160	1.0878	0.4098	1.0878	1.0430
0.1247	40.5	162	0.7047	0.5122	0.7047	0.8395
0.1247	41.0	164	0.6168	0.5402	0.6168	0.7854
0.1247	41.5	166	0.7633	0.4948	0.7633	0.8737
0.1247	42.0	168	1.0284	0.4304	1.0284	1.0141
0.1284	42.5	170	0.8185	0.4811	0.8185	0.9047
0.1284	43.0	172	0.6620	0.5322	0.6620	0.8136
0.1284	43.5	174	0.7636	0.5065	0.7636	0.8739
0.1284	44.0	176	0.8644	0.4718	0.8644	0.9297
0.1284	44.5	178	0.7710	0.4761	0.7710	0.8781
0.1064	45.0	180	0.8348	0.4714	0.8348	0.9137
0.1064	45.5	182	0.7194	0.4826	0.7194	0.8481
0.1064	46.0	184	0.7381	0.4888	0.7381	0.8591
0.1064	46.5	186	0.7344	0.4930	0.7344	0.8569
0.1064	47.0	188	0.8636	0.4608	0.8636	0.9293
0.0708	47.5	190	0.9770	0.4520	0.9770	0.9884
0.0708	48.0	192	0.8934	0.4762	0.8934	0.9452
0.0708	48.5	194	0.8044	0.4806	0.8044	0.8969
0.0708	49.0	196	0.8562	0.4713	0.8562	0.9253
0.0708	49.5	198	0.7516	0.5161	0.7516	0.8670
0.0672	50.0	200	0.7107	0.5100	0.7107	0.8430
0.0672	50.5	202	0.9034	0.4627	0.9034	0.9505
0.0672	51.0	204	0.9446	0.4692	0.9446	0.9719
0.0672	51.5	206	0.7942	0.5063	0.7942	0.8912
0.0672	52.0	208	0.7732	0.5244	0.7732	0.8793
0.0716	52.5	210	0.7789	0.5085	0.7789	0.8826
0.0716	53.0	212	0.8124	0.4913	0.8124	0.9013
0.0716	53.5	214	0.9234	0.4506	0.9234	0.9609
0.0716	54.0	216	0.7996	0.5056	0.7996	0.8942
0.0716	54.5	218	0.6925	0.5295	0.6925	0.8322
0.0744	55.0	220	0.6586	0.5305	0.6586	0.8115
0.0744	55.5	222	0.7302	0.5244	0.7302	0.8545
0.0744	56.0	224	0.7359	0.5201	0.7359	0.8578
0.0744	56.5	226	0.7014	0.5279	0.7014	0.8375
0.0744	57.0	228	0.7654	0.5243	0.7654	0.8749
0.065	57.5	230	0.7353	0.5234	0.7353	0.8575
0.065	58.0	232	0.7876	0.5263	0.7876	0.8874
0.065	58.5	234	0.9430	0.4861	0.9430	0.9711
0.065	59.0	236	0.8015	0.5165	0.8015	0.8953
0.065	59.5	238	0.7685	0.5212	0.7685	0.8766
0.0703	60.0	240	0.8733	0.5057	0.8733	0.9345
0.0703	60.5	242	0.8019	0.5280	0.8019	0.8955
0.0703	61.0	244	0.7009	0.5269	0.7009	0.8372
0.0703	61.5	246	0.7519	0.5237	0.7519	0.8671
0.0703	62.0	248	0.9770	0.4673	0.9770	0.9884
0.0713	62.5	250	0.9693	0.4851	0.9693	0.9845
0.0713	63.0	252	0.7574	0.5148	0.7574	0.8703
0.0713	63.5	254	0.7450	0.5129	0.7450	0.8632
0.0713	64.0	256	0.8994	0.4938	0.8994	0.9484
0.0713	64.5	258	0.9334	0.4847	0.9334	0.9661
0.06	65.0	260	0.8432	0.4786	0.8432	0.9183
0.06	65.5	262	0.8636	0.4844	0.8636	0.9293
0.06	66.0	264	0.8856	0.5127	0.8856	0.9411
0.06	66.5	266	0.8437	0.5174	0.8437	0.9185
0.06	67.0	268	0.7529	0.5265	0.7529	0.8677
0.0796	67.5	270	0.7375	0.5192	0.7375	0.8588
0.0796	68.0	272	0.8585	0.4571	0.8585	0.9266
0.0796	68.5	274	0.9746	0.4589	0.9746	0.9872
0.0796	69.0	276	0.8847	0.4883	0.8847	0.9406
0.0796	69.5	278	0.7054	0.5209	0.7054	0.8399
0.0606	70.0	280	0.6831	0.5288	0.6831	0.8265
0.0606	70.5	282	0.7845	0.5136	0.7845	0.8857
0.0606	71.0	284	0.9920	0.4595	0.9920	0.9960
0.0606	71.5	286	0.9671	0.4637	0.9671	0.9834
0.0606	72.0	288	0.7809	0.5060	0.7809	0.8837
0.0667	72.5	290	0.7231	0.5210	0.7231	0.8504
0.0667	73.0	292	0.8012	0.5088	0.8012	0.8951
0.0667	73.5	294	0.8418	0.4991	0.8418	0.9175
0.0667	74.0	296	0.7459	0.5272	0.7459	0.8637
0.0667	74.5	298	0.7324	0.5222	0.7324	0.8558
0.0598	75.0	300	0.8032	0.5015	0.8032	0.8962
0.0598	75.5	302	0.8740	0.4861	0.8740	0.9349
0.0598	76.0	304	0.7943	0.5342	0.7943	0.8912
0.0598	76.5	306	0.7117	0.5202	0.7117	0.8436
0.0598	77.0	308	0.7410	0.5242	0.7410	0.8608
0.0553	77.5	310	0.8261	0.4946	0.8261	0.9089
0.0553	78.0	312	0.9515	0.4678	0.9515	0.9755
0.0553	78.5	314	0.9715	0.4537	0.9715	0.9857
0.0553	79.0	316	0.8247	0.4920	0.8247	0.9081
0.0553	79.5	318	0.7006	0.5311	0.7006	0.8370
0.047	80.0	320	0.6803	0.5333	0.6803	0.8248
0.047	80.5	322	0.7134	0.5251	0.7134	0.8446
0.047	81.0	324	0.8279	0.5062	0.8279	0.9099
0.047	81.5	326	0.8879	0.4831	0.8879	0.9423
0.047	82.0	328	0.8115	0.4885	0.8115	0.9008
0.0556	82.5	330	0.7193	0.5263	0.7193	0.8481
0.0556	83.0	332	0.7025	0.5229	0.7025	0.8381
0.0556	83.5	334	0.7563	0.5283	0.7563	0.8697
0.0556	84.0	336	0.7974	0.4992	0.7974	0.8930
0.0556	84.5	338	0.7845	0.5031	0.7845	0.8857
0.0443	85.0	340	0.7802	0.5068	0.7802	0.8833
0.0443	85.5	342	0.8005	0.5015	0.8005	0.8947
0.0443	86.0	344	0.7778	0.5253	0.7778	0.8819
0.0443	86.5	346	0.7521	0.5235	0.7521	0.8673
0.0443	87.0	348	0.7572	0.5264	0.7572	0.8701
0.0448	87.5	350	0.7803	0.5121	0.7803	0.8834
0.0448	88.0	352	0.7998	0.4825	0.7998	0.8943
0.0448	88.5	354	0.8135	0.4834	0.8135	0.9020
0.0448	89.0	356	0.8208	0.4938	0.8208	0.9060
0.0448	89.5	358	0.7736	0.5150	0.7736	0.8795
0.0433	90.0	360	0.7314	0.5270	0.7314	0.8552
0.0433	90.5	362	0.7098	0.5224	0.7098	0.8425
0.0433	91.0	364	0.7182	0.5231	0.7182	0.8475
0.0433	91.5	366	0.7416	0.5294	0.7416	0.8611
0.0433	92.0	368	0.7749	0.5242	0.7749	0.8803
0.0448	92.5	370	0.7672	0.5206	0.7672	0.8759
0.0448	93.0	372	0.7412	0.5294	0.7412	0.8609
0.0448	93.5	374	0.7147	0.5270	0.7147	0.8454
0.0448	94.0	376	0.7022	0.5260	0.7022	0.8380
0.0448	94.5	378	0.7102	0.5267	0.7102	0.8428
0.0394	95.0	380	0.7157	0.5228	0.7157	0.8460
0.0394	95.5	382	0.7223	0.5238	0.7223	0.8499
0.0394	96.0	384	0.7277	0.5308	0.7277	0.8531
0.0394	96.5	386	0.7391	0.5301	0.7391	0.8597
0.0394	97.0	388	0.7472	0.5311	0.7472	0.8644
0.0457	97.5	390	0.7557	0.5222	0.7557	0.8693
0.0457	98.0	392	0.7527	0.5238	0.7527	0.8676
0.0457	98.5	394	0.7450	0.5264	0.7450	0.8632
0.0457	99.0	396	0.7437	0.5264	0.7437	0.8624
0.0457	99.5	398	0.7425	0.5277	0.7425	0.8617
0.0466	100.0	400	0.7431	0.5252	0.7431	0.8620

Framework versions

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