Training Loss	Epoch	Step	Validation Loss	Bleu	Gen Len
1.3998	0.0131	100	1.1909	9.0438	16.6128
1.3745	0.0262	200	1.1892	8.9685	16.7342
1.3584	0.0393	300	1.1884	8.9498	16.6153
1.3615	0.0525	400	1.1913	8.94	16.5859
1.3417	0.0656	500	1.1818	8.8506	16.6169
1.3459	0.0787	600	1.1812	9.1565	16.6316
1.35	0.0918	700	1.1819	8.9922	16.5945
1.3244	0.1049	800	1.1749	8.9409	16.6778
1.3282	0.1180	900	1.1690	8.9618	16.5828
1.3198	0.1311	1000	1.1703	9.1664	16.6026
1.3359	0.1443	1100	1.1685	8.978	16.6677
1.3177	0.1574	1200	1.1654	8.9768	16.6347
1.3334	0.1705	1300	1.1615	8.9667	16.6148
1.3192	0.1836	1400	1.1635	9.1455	16.5879
1.315	0.1967	1500	1.1618	8.97	16.5452
1.309	0.2098	1600	1.1606	9.1667	16.6367
1.3052	0.2229	1700	1.1613	8.962	16.6047
1.3006	0.2361	1800	1.1535	9.042	16.6484
1.2999	0.2492	1900	1.1560	8.977	16.5513
1.2939	0.2623	2000	1.1553	9.0578	16.5996
1.3079	0.2754	2100	1.1505	9.1548	16.6438
1.3086	0.2885	2200	1.1521	8.9797	16.5493
1.2993	0.3016	2300	1.1498	9.1659	16.5727
1.2963	0.3147	2400	1.1454	9.1355	16.532
1.2894	0.3279	2500	1.1423	9.2378	16.5803
1.2914	0.3410	2600	1.1425	9.3786	16.6011
1.2898	0.3541	2700	1.1447	9.2694	16.5112
1.2883	0.3672	2800	1.1446	9.2671	16.561
1.2796	0.3803	2900	1.1407	9.3267	16.5528
1.2854	0.3934	3000	1.1403	9.1921	16.5838
1.2657	0.4065	3100	1.1375	9.1904	16.5727
1.2729	0.4197	3200	1.1396	9.1816	16.596
1.2782	0.4328	3300	1.1382	9.3068	16.5503
1.2784	0.4459	3400	1.1345	9.2616	16.5168
1.2687	0.4590	3500	1.1333	9.2731	16.5569
1.2802	0.4721	3600	1.1285	9.2272	16.5772
1.2693	0.4852	3700	1.1304	9.3535	16.5645
1.279	0.4983	3800	1.1343	9.3037	16.565
1.2678	0.5115	3900	1.1306	9.3029	16.6118
1.2579	0.5246	4000	1.1318	9.3173	16.6448
1.262	0.5377	4100	1.1282	9.3084	16.6199
1.2778	0.5508	4200	1.1258	9.4782	16.6032
1.2567	0.5639	4300	1.1246	9.3401	16.5965
1.2425	0.5770	4400	1.1293	9.4245	16.5671
1.2593	0.5901	4500	1.1228	9.2466	16.6037
1.2591	0.6033	4600	1.1220	9.3294	16.5925
1.2661	0.6164	4700	1.1255	9.333	16.5361
1.2446	0.6295	4800	1.1235	9.3146	16.5676
1.2563	0.6426	4900	1.1205	9.3765	16.5661
1.2416	0.6557	5000	1.1188	9.3549	16.5849
1.2605	0.6688	5100	1.1187	9.313	16.5767
1.253	0.6819	5200	1.1191	9.24	16.5407
1.2429	0.6951	5300	1.1178	9.1666	16.5549
1.2587	0.7082	5400	1.1167	9.26	16.5513
1.2432	0.7213	5500	1.1135	9.2584	16.5381
1.2422	0.7344	5600	1.1137	9.3422	16.5752
1.2581	0.7475	5700	1.1146	9.3159	16.5767
1.2451	0.7606	5800	1.1142	9.278	16.534
1.25	0.7737	5900	1.1140	9.3551	16.596
1.2435	0.7869	6000	1.1117	9.3174	16.561
1.2452	0.8000	6100	1.1112	9.3823	16.5706
1.2344	0.8131	6200	1.1120	9.3922	16.5508
1.2231	0.8262	6300	1.1092	9.3544	16.532
1.2449	0.8393	6400	1.1071	9.3757	16.5534
1.2154	0.8524	6500	1.1087	9.3746	16.5366
1.236	0.8655	6600	1.1083	9.3719	16.5554
1.2355	0.8787	6700	1.1088	9.4179	16.5701
1.2403	0.8918	6800	1.1079	9.3163	16.5407
1.2213	0.9049	6900	1.1062	9.3422	16.5605
1.2315	0.9180	7000	1.1067	9.4145	16.5615
1.2217	0.9311	7100	1.1062	9.4026	16.5452
1.2418	0.9442	7200	1.1053	9.3595	16.5564
1.2181	0.9573	7300	1.1058	9.3921	16.5737
1.214	0.9705	7400	1.1051	9.4053	16.5671
1.2135	0.9836	7500	1.1054	9.377	16.5615
1.2327	0.9967	7600	1.1051	9.3944	16.5625

Framework versions

Transformers 4.41.2
Pytorch 2.3.1+cu121
Datasets 2.19.2
Tokenizers 0.19.1

kokodio/my_awesome_opus_books_model

my_awesome_opus_books_model

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

kokodio/
my_awesome_opus_books_model