learning_rate: 1e-05
train_batch_size: 16
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
gradient_accumulation_steps: 8
total_train_batch_size: 128
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss	Pearson Cosine	Spearman Cosine	Pearson Manhattan	Spearman Manhattan	Pearson Euclidean	Spearman Euclidean	Pearson Dot	Spearman Dot
1.0084	0.1172	250	0.1579	0.6838	0.6994	0.6615	0.6693	0.6621	0.6694	0.3480	0.3442
0.7072	0.2343	500	0.1364	0.7226	0.7375	0.7207	0.7263	0.7214	0.7271	0.4002	0.3910
0.6207	0.3515	750	0.1194	0.7371	0.7509	0.7295	0.7398	0.7300	0.7401	0.4517	0.4462
0.5767	0.4686	1000	0.1147	0.7508	0.7636	0.7395	0.7502	0.7400	0.7511	0.5170	0.5181
0.5026	0.5858	1250	0.1047	0.7507	0.7635	0.7455	0.7558	0.7459	0.7564	0.5487	0.5531
0.5192	0.7029	1500	0.1166	0.7522	0.7673	0.7487	0.7591	0.7489	0.7594	0.5055	0.5053
0.5046	0.8201	1750	0.1110	0.7555	0.7675	0.7582	0.7675	0.7581	0.7672	0.5303	0.5391
0.5055	0.9372	2000	0.1062	0.7546	0.7726	0.7501	0.7650	0.7502	0.7651	0.5638	0.5710
0.4177	1.0544	2250	0.0942	0.7577	0.7709	0.7511	0.7635	0.7510	0.7633	0.5577	0.5635
0.4136	1.1715	2500	0.0915	0.7612	0.7727	0.7584	0.7696	0.7586	0.7696	0.5554	0.5595
0.4425	1.2887	2750	0.0928	0.7605	0.7726	0.7461	0.7591	0.7463	0.7592	0.5498	0.5512
0.3708	1.4058	3000	0.0819	0.7670	0.7783	0.7478	0.7634	0.7481	0.7637	0.5834	0.5847
0.3934	1.5230	3250	0.0848	0.7709	0.7814	0.7539	0.7692	0.7542	0.7689	0.5655	0.5668
0.3203	1.6401	3500	0.0781	0.7706	0.7810	0.7529	0.7689	0.7531	0.7691	0.5871	0.5891
0.4052	1.7573	3750	0.0824	0.7705	0.7816	0.7628	0.7771	0.7628	0.7771	0.5909	0.5989
0.3723	1.8744	4000	0.0819	0.7720	0.7840	0.7515	0.7679	0.7520	0.7685	0.5711	0.5713
0.3645	1.9916	4250	0.0802	0.7676	0.7804	0.7560	0.7704	0.7560	0.7703	0.5685	0.5701
0.3007	2.1087	4500	0.0662	0.7682	0.7799	0.7572	0.7721	0.7574	0.7721	0.5973	0.5981
0.2397	2.2259	4750	0.0617	0.7693	0.7782	0.7501	0.7655	0.7502	0.7652	0.5855	0.5898
0.28	2.3430	5000	0.0645	0.7654	0.7760	0.7567	0.7705	0.7569	0.7705	0.5925	0.5970
0.2631	2.4602	5250	0.0639	0.7712	0.7798	0.7561	0.7705	0.7562	0.7705	0.5715	0.5731
0.2488	2.5773	5500	0.0636	0.7736	0.7838	0.7537	0.7687	0.7538	0.7685	0.5835	0.5861
0.2557	2.6945	5750	0.0614	0.7739	0.7830	0.7570	0.7716	0.7571	0.7717	0.6008	0.6041
0.2699	2.8116	6000	0.0636	0.7722	0.7795	0.7570	0.7699	0.7572	0.7701	0.5844	0.5864
0.2794	2.9288	6250	0.0639	0.7704	0.7800	0.7582	0.7745	0.7581	0.7746	0.5817	0.5793
0.1778	3.0459	6500	0.0526	0.7738	0.7811	0.7574	0.7739	0.7573	0.7739	0.6193	0.6255
0.1791	3.1631	6750	0.0519	0.7728	0.7783	0.7540	0.7704	0.7538	0.7700	0.6116	0.6182
0.201	3.2802	7000	0.0511	0.7755	0.7825	0.7506	0.7671	0.7503	0.7670	0.6039	0.6071
0.225	3.3974	7250	0.0513	0.7684	0.7749	0.7515	0.7689	0.7514	0.7692	0.5867	0.5894
0.1748	3.5145	7500	0.0502	0.7752	0.7801	0.7459	0.7630	0.7461	0.7636	0.5877	0.5949
0.2045	3.6317	7750	0.0512	0.7787	0.7856	0.7457	0.7636	0.7460	0.7642	0.6113	0.6156
0.1821	3.7488	8000	0.0502	0.7782	0.7842	0.7543	0.7707	0.7545	0.7710	0.6045	0.6069
0.1783	3.8660	8250	0.0491	0.7772	0.7829	0.7455	0.7630	0.7459	0.7637	0.5915	0.5984
0.2055	3.9831	8500	0.0504	0.7776	0.7832	0.7476	0.7658	0.7480	0.7662	0.5959	0.6017
0.1345	4.1003	8750	0.0467	0.7762	0.7802	0.7429	0.7606	0.7435	0.7611	0.6206	0.6303
0.1506	4.2174	9000	0.0477	0.7711	0.7759	0.7466	0.7625	0.7473	0.7631	0.5978	0.6025
0.1565	4.3346	9250	0.0477	0.7717	0.7768	0.7481	0.7641	0.7486	0.7645	0.6026	0.6102
0.1577	4.4517	9500	0.0442	0.7794	0.7824	0.7439	0.7627	0.7444	0.7630	0.6182	0.6291
0.1463	4.5689	9750	0.0456	0.7764	0.7821	0.7401	0.7602	0.7405	0.7604	0.5941	0.5991
0.16	4.6860	10000	0.0460	0.7749	0.7793	0.7495	0.7658	0.7498	0.7660	0.6140	0.6192
0.148	4.8032	10250	0.0436	0.7817	0.7855	0.7421	0.7596	0.7425	0.7601	0.6171	0.6239
0.1382	4.9203	10500	0.0446	0.7824	0.7872	0.7437	0.7620	0.7443	0.7625	0.6330	0.6424
0.1109	5.0375	10750	0.0426	0.7796	0.7846	0.7431	0.7600	0.7434	0.7602	0.6195	0.6249
0.1009	5.1546	11000	0.0431	0.7807	0.7835	0.7423	0.7591	0.7428	0.7591	0.6237	0.6377
0.1082	5.2718	11250	0.0438	0.7774	0.7818	0.7430	0.7591	0.7433	0.7593	0.6039	0.6129
0.1138	5.3889	11500	0.0415	0.7829	0.7870	0.7405	0.7560	0.7410	0.7561	0.6347	0.6464
0.1015	5.5061	11750	0.0420	0.7778	0.7811	0.7437	0.7592	0.7435	0.7589	0.6249	0.6370
0.1153	5.6232	12000	0.0448	0.7730	0.7784	0.7451	0.7598	0.7453	0.7596	0.6141	0.6214
0.1269	5.7404	12250	0.0420	0.7802	0.7840	0.7413	0.7562	0.7417	0.7564	0.6217	0.6311
0.0888	5.8575	12500	0.0414	0.7805	0.7841	0.7408	0.7567	0.7412	0.7568	0.6245	0.6365
0.1202	5.9747	12750	0.0431	0.7793	0.7835	0.7412	0.7572	0.7414	0.7575	0.6261	0.6405
0.0941	6.0918	13000	0.0399	0.7838	0.7873	0.7388	0.7527	0.7391	0.7530	0.6493	0.6642
0.081	6.2090	13250	0.0405	0.7814	0.7854	0.7353	0.7513	0.7355	0.7514	0.6356	0.6478
0.0807	6.3261	13500	0.0401	0.7838	0.7879	0.7339	0.7510	0.7344	0.7513	0.6450	0.6615
0.0863	6.4433	13750	0.0405	0.7814	0.7841	0.7404	0.7587	0.7408	0.7589	0.6324	0.6479
0.0948	6.5604	14000	0.0397	0.7830	0.7866	0.7410	0.7578	0.7415	0.7579	0.6308	0.6460
0.0919	6.6776	14250	0.0409	0.7820	0.7858	0.7402	0.7545	0.7403	0.7544	0.6341	0.6459
0.0784	6.7948	14500	0.0408	0.7794	0.7839	0.7308	0.7495	0.7312	0.7494	0.6306	0.6427
0.0821	6.9119	14750	0.0406	0.7789	0.7822	0.7265	0.7446	0.7270	0.7446	0.6377	0.6567
0.0792	7.0291	15000	0.0401	0.7800	0.7833	0.7398	0.7569	0.7405	0.7572	0.6338	0.6467
0.0698	7.1462	15250	0.0396	0.7822	0.7855	0.7341	0.7507	0.7346	0.7509	0.6381	0.6552
0.0699	7.2634	15500	0.0392	0.7820	0.7851	0.7322	0.7502	0.7325	0.7502	0.6466	0.6629
0.0739	7.3805	15750	0.0389	0.7865	0.7886	0.7323	0.7491	0.7328	0.7495	0.6412	0.6589
0.0745	7.4977	16000	0.0397	0.7794	0.7827	0.7366	0.7524	0.7373	0.7524	0.6380	0.6504
0.0779	7.6148	16250	0.0391	0.7826	0.7846	0.7326	0.7462	0.7333	0.7467	0.6372	0.6532
0.078	7.7320	16500	0.0397	0.7810	0.7826	0.7299	0.7461	0.7300	0.7457	0.6364	0.6555
0.0699	7.8491	16750	0.0405	0.7811	0.7837	0.7308	0.7468	0.7312	0.7470	0.6315	0.6426
0.0735	7.9663	17000	0.0394	0.7804	0.7823	0.7320	0.7455	0.7326	0.7462	0.6468	0.6607
0.0682	8.0834	17250	0.0386	0.7845	0.7869	0.7306	0.7447	0.7311	0.7449	0.6431	0.6613
0.0526	8.2006	17500	0.0389	0.7824	0.7832	0.7272	0.7431	0.7275	0.7431	0.6370	0.6539
0.0558	8.3177	17750	0.0385	0.7856	0.7865	0.7370	0.7513	0.7376	0.7518	0.6517	0.6679
0.0633	8.4349	18000	0.0392	0.7822	0.7845	0.7388	0.7537	0.7395	0.7542	0.6512	0.6664
0.0568	8.5520	18250	0.0389	0.7826	0.7831	0.7358	0.7510	0.7362	0.7509	0.6378	0.6536
0.0645	8.6692	18500	0.0377	0.7888	0.7892	0.7315	0.7495	0.7319	0.7499	0.6514	0.6704
0.0563	8.7863	18750	0.0376	0.7870	0.7878	0.7285	0.7451	0.7289	0.7454	0.6393	0.6606
0.0669	8.9035	19000	0.0383	0.7850	0.7866	0.7238	0.7433	0.7244	0.7437	0.6359	0.6571
0.0436	9.0206	19250	0.0377	0.7855	0.7856	0.7289	0.7462	0.7293	0.7465	0.6489	0.6696
0.047	9.1378	19500	0.0377	0.7870	0.7882	0.7249	0.7414	0.7254	0.7413	0.6459	0.6694
0.0482	9.2549	19750	0.0377	0.7863	0.7871	0.7296	0.7442	0.7306	0.7449	0.6498	0.6690
0.0529	9.3721	20000	0.0377	0.7873	0.7888	0.7285	0.7423	0.7290	0.7426	0.6490	0.6690
0.0429	9.4892	20250	0.0378	0.7868	0.7883	0.7286	0.7426	0.7292	0.7431	0.6503	0.6684
0.0534	9.6064	20500	0.0380	0.7861	0.7881	0.7300	0.7443	0.7305	0.7451	0.6446	0.6635
0.0531	9.7235	20750	0.0375	0.7886	0.7894	0.7350	0.7492	0.7356	0.7498	0.6442	0.6634
0.0464	9.8407	21000	0.0380	0.7861	0.7871	0.7314	0.7464	0.7320	0.7468	0.6415	0.6600
0.0406	9.9578	21250	0.0387	0.7824	0.7845	0.7335	0.7460	0.7337	0.7463	0.6362	0.6532

Framework versions

Transformers 4.48.0.dev0
Pytorch 2.5.1+cu121
Datasets 3.1.0
Tokenizers 0.21.0

CocoRoF/KMB_SimCSE

KMB_SimCSE

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

CocoRoF/
KMB_SimCSE