Training Loss	Epoch	Step	Validation Loss	Bleu	Gen Len
No log	1.0	11	2.9595	0.2068	9.7301
No log	2.0	22	2.5919	0.4412	10.0736
No log	3.0	33	2.2077	0.9166	9.6626
No log	4.0	44	1.9446	0.7991	9.8037
No log	5.0	55	1.6666	0.8674	9.8221
No log	6.0	66	1.4209	1.0262	10.0613
No log	7.0	77	1.1828	1.573	9.9693
No log	8.0	88	0.9715	1.6163	9.9509
No log	9.0	99	0.8203	2.1844	9.7362
No log	10.0	110	0.6698	2.193	9.6687
No log	11.0	121	0.5533	3.1733	9.816
No log	12.0	132	0.4650	3.0054	9.6687
No log	13.0	143	0.3783	3.5488	9.6012
No log	14.0	154	0.3130	4.1709	9.7362
No log	15.0	165	0.2620	4.9365	9.6442
No log	16.0	176	0.2351	5.5276	9.546
No log	17.0	187	0.1953	5.6558	9.6074
No log	18.0	198	0.1524	6.4656	9.6503
No log	19.0	209	0.1226	6.9583	9.5828
No log	20.0	220	0.0953	7.7977	9.5951
No log	21.0	231	0.0766	7.7172	9.638
No log	22.0	242	0.0633	8.2632	9.6135
No log	23.0	253	0.0581	8.3314	9.6135
No log	24.0	264	0.0478	8.6339	9.6564
No log	25.0	275	0.0379	8.4599	9.681
No log	26.0	286	0.0349	8.8518	9.681
No log	27.0	297	0.0284	8.6561	9.6994
No log	28.0	308	0.0215	8.8647	9.6748
No log	29.0	319	0.0189	8.8318	9.681
No log	30.0	330	0.0211	8.7839	9.681
No log	31.0	341	0.0223	9.0581	9.6687
No log	32.0	352	0.0172	9.0431	9.6687
No log	33.0	363	0.0131	9.0838	9.681
No log	34.0	374	0.0152	8.9549	9.681
No log	35.0	385	0.0121	9.0402	9.681
No log	36.0	396	0.0178	9.1416	9.6442
No log	37.0	407	0.0161	9.0402	9.6564
No log	38.0	418	0.0139	9.1518	9.681
No log	39.0	429	0.0162	9.0741	9.681
No log	40.0	440	0.0126	9.1518	9.681
No log	41.0	451	0.0108	9.0897	9.681
No log	42.0	462	0.0144	9.0836	9.6933
No log	43.0	473	0.0238	9.1129	9.6871
No log	44.0	484	0.0075	9.1518	9.681
No log	45.0	495	0.0108	8.9628	9.681
0.7724	46.0	506	0.0071	8.9863	9.681
0.7724	47.0	517	0.0087	9.1518	9.681
0.7724	48.0	528	0.0082	9.1518	9.681
0.7724	49.0	539	0.0064	9.1518	9.681
0.7724	50.0	550	0.0095	9.1518	9.681
0.7724	51.0	561	0.0090	9.1518	9.681
0.7724	52.0	572	0.0091	9.1801	9.681
0.7724	53.0	583	0.0105	9.1801	9.681
0.7724	54.0	594	0.0180	8.9309	9.681
0.7724	55.0	605	0.0123	9.1518	9.681
0.7724	56.0	616	0.0119	9.1518	9.681
0.7724	57.0	627	0.0061	9.1518	9.681
0.7724	58.0	638	0.0082	9.1518	9.681
0.7724	59.0	649	0.0059	9.1518	9.681
0.7724	60.0	660	0.0146	9.0639	9.681
0.7724	61.0	671	0.0123	9.0639	9.681
0.7724	62.0	682	0.0084	9.0639	9.681
0.7724	63.0	693	0.0122	9.0639	9.681
0.7724	64.0	704	0.0319	9.1518	9.681
0.7724	65.0	715	0.0142	9.1518	9.681
0.7724	66.0	726	0.0086	9.1518	9.681
0.7724	67.0	737	0.0078	9.0847	9.681
0.7724	68.0	748	0.0122	9.1518	9.681
0.7724	69.0	759	0.0092	9.1518	9.681
0.7724	70.0	770	0.0059	9.1518	9.681
0.7724	71.0	781	0.0090	9.0944	9.6871
0.7724	72.0	792	0.0127	9.0944	9.6871
0.7724	73.0	803	0.0108	9.0944	9.6871
0.7724	74.0	814	0.0091	9.1518	9.681
0.7724	75.0	825	0.0073	9.1518	9.681
0.7724	76.0	836	0.0112	9.1518	9.681
0.7724	77.0	847	0.0113	9.1518	9.681
0.7724	78.0	858	0.0093	9.1518	9.681
0.7724	79.0	869	0.0048	9.1518	9.681
0.7724	80.0	880	0.0064	9.1518	9.681
0.7724	81.0	891	0.0102	9.1518	9.681
0.7724	82.0	902	0.0110	9.1467	9.6748
0.7724	83.0	913	0.0104	9.1467	9.6748
0.7724	84.0	924	0.0089	9.1467	9.6748
0.7724	85.0	935	0.0078	9.1518	9.681
0.7724	86.0	946	0.0067	9.1518	9.681
0.7724	87.0	957	0.0047	9.1518	9.681
0.7724	88.0	968	0.0047	9.1518	9.681
0.7724	89.0	979	0.0058	9.1518	9.681
0.7724	90.0	990	0.0045	9.1518	9.681
0.0426	91.0	1001	0.0087	9.1518	9.681
0.0426	92.0	1012	0.0096	9.1518	9.681
0.0426	93.0	1023	0.0063	9.1518	9.681
0.0426	94.0	1034	0.0076	9.1518	9.681
0.0426	95.0	1045	0.0055	9.1518	9.681
0.0426	96.0	1056	0.0054	9.1518	9.681
0.0426	97.0	1067	0.0052	9.1518	9.681
0.0426	98.0	1078	0.0046	9.1518	9.681
0.0426	99.0	1089	0.0100	9.1518	9.681
0.0426	100.0	1100	0.0104	9.1518	9.681
0.0426	101.0	1111	0.0180	9.1518	9.681
0.0426	102.0	1122	0.0208	9.1518	9.681
0.0426	103.0	1133	0.0159	9.1518	9.681
0.0426	104.0	1144	0.0139	9.1518	9.681
0.0426	105.0	1155	0.0122	9.1518	9.681
0.0426	106.0	1166	0.0080	9.1518	9.681
0.0426	107.0	1177	0.0063	9.1518	9.681
0.0426	108.0	1188	0.0149	9.1467	9.6687
0.0426	109.0	1199	0.0147	9.1518	9.681
0.0426	110.0	1210	0.0113	9.1518	9.681
0.0426	111.0	1221	0.0170	9.1518	9.681
0.0426	112.0	1232	0.0138	9.1518	9.681
0.0426	113.0	1243	0.0129	9.1518	9.681
0.0426	114.0	1254	0.0095	9.1518	9.681
0.0426	115.0	1265	0.0133	9.1518	9.681
0.0426	116.0	1276	0.0128	9.1518	9.681
0.0426	117.0	1287	0.0112	9.1518	9.681
0.0426	118.0	1298	0.0093	9.1518	9.681
0.0426	119.0	1309	0.0066	9.1518	9.681
0.0426	120.0	1320	0.0048	9.1518	9.681
0.0426	121.0	1331	0.0079	9.1518	9.681
0.0426	122.0	1342	0.0095	9.1518	9.681
0.0426	123.0	1353	0.0069	9.1518	9.681
0.0426	124.0	1364	0.0056	9.1518	9.681
0.0426	125.0	1375	0.0049	9.1518	9.681
0.0426	126.0	1386	0.0066	9.1518	9.681
0.0426	127.0	1397	0.0080	9.1518	9.681
0.0426	128.0	1408	0.0073	9.1467	9.6687
0.0426	129.0	1419	0.0063	9.1518	9.681
0.0426	130.0	1430	0.0063	9.1518	9.681
0.0426	131.0	1441	0.0051	9.1518	9.681
0.0426	132.0	1452	0.0045	9.1518	9.681
0.0426	133.0	1463	0.0061	9.1518	9.681
0.0426	134.0	1474	0.0061	9.1518	9.681
0.0426	135.0	1485	0.0042	9.1518	9.681
0.0426	136.0	1496	0.0043	9.1518	9.681
0.0153	137.0	1507	0.0045	9.1518	9.681
0.0153	138.0	1518	0.0056	9.1518	9.681
0.0153	139.0	1529	0.0113	9.1518	9.681
0.0153	140.0	1540	0.0054	9.1518	9.681
0.0153	141.0	1551	0.0054	9.1518	9.681
0.0153	142.0	1562	0.0058	9.1518	9.681
0.0153	143.0	1573	0.0048	9.1518	9.681
0.0153	144.0	1584	0.0049	9.1518	9.681
0.0153	145.0	1595	0.0047	9.1518	9.681
0.0153	146.0	1606	0.0046	9.1518	9.681
0.0153	147.0	1617	0.0046	9.1518	9.681
0.0153	148.0	1628	0.0046	9.1518	9.681
0.0153	149.0	1639	0.0045	9.1518	9.681
0.0153	150.0	1650	0.0048	9.1518	9.681
0.0153	151.0	1661	0.0054	9.1518	9.681
0.0153	152.0	1672	0.0058	9.1518	9.681
0.0153	153.0	1683	0.0057	9.1518	9.681
0.0153	154.0	1694	0.0056	9.1518	9.681
0.0153	155.0	1705	0.0056	9.1518	9.681
0.0153	156.0	1716	0.0061	9.1518	9.681
0.0153	157.0	1727	0.0062	9.1518	9.681
0.0153	158.0	1738	0.0060	9.1518	9.681
0.0153	159.0	1749	0.0060	9.1518	9.681
0.0153	160.0	1760	0.0061	9.1518	9.681
0.0153	161.0	1771	0.0052	9.1518	9.681
0.0153	162.0	1782	0.0049	9.1518	9.681
0.0153	163.0	1793	0.0047	9.1518	9.681
0.0153	164.0	1804	0.0046	9.1518	9.681
0.0153	165.0	1815	0.0045	9.1518	9.681
0.0153	166.0	1826	0.0046	9.1518	9.681
0.0153	167.0	1837	0.0048	9.1518	9.681
0.0153	168.0	1848	0.0052	9.1518	9.681
0.0153	169.0	1859	0.0051	9.1518	9.681
0.0153	170.0	1870	0.0055	9.1518	9.681
0.0153	171.0	1881	0.0056	9.1518	9.681
0.0153	172.0	1892	0.0051	9.1518	9.681
0.0153	173.0	1903	0.0050	9.1518	9.681
0.0153	174.0	1914	0.0048	9.1518	9.681
0.0153	175.0	1925	0.0048	9.1518	9.681
0.0153	176.0	1936	0.0045	9.1518	9.681
0.0153	177.0	1947	0.0045	9.1518	9.681
0.0153	178.0	1958	0.0045	9.1518	9.681
0.0153	179.0	1969	0.0044	9.1518	9.681
0.0153	180.0	1980	0.0046	9.1518	9.681
0.0153	181.0	1991	0.0046	9.1518	9.681
0.007	182.0	2002	0.0046	9.1518	9.681
0.007	183.0	2013	0.0046	9.1518	9.681
0.007	184.0	2024	0.0046	9.1518	9.681
0.007	185.0	2035	0.0046	9.1518	9.681
0.007	186.0	2046	0.0046	9.1518	9.681
0.007	187.0	2057	0.0046	9.1518	9.681
0.007	188.0	2068	0.0047	9.1518	9.681
0.007	189.0	2079	0.0047	9.1518	9.681
0.007	190.0	2090	0.0048	9.1518	9.681
0.007	191.0	2101	0.0048	9.1518	9.681
0.007	192.0	2112	0.0049	9.1518	9.681
0.007	193.0	2123	0.0049	9.1518	9.681
0.007	194.0	2134	0.0048	9.1518	9.681
0.007	195.0	2145	0.0048	9.1518	9.681
0.007	196.0	2156	0.0048	9.1518	9.681
0.007	197.0	2167	0.0048	9.1518	9.681
0.007	198.0	2178	0.0048	9.1518	9.681
0.007	199.0	2189	0.0049	9.1518	9.681
0.007	200.0	2200	0.0048	9.1518	9.681

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0

youdiniplays/tl-bic-model

tl-bic-model

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

youdiniplays/
tl-bic-model