bic-tl-model

This model is a fine-tuned version of t5-small on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0000
• Bleu: 8.6577
• Gen Len: 9.5337

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: 0.001
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 200
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu	Gen Len
No log	1.0	11	2.8101	0.4192	9.6933
No log	2.0	22	2.4413	0.5091	9.8896
No log	3.0	33	2.1777	0.5912	9.9939
No log	4.0	44	1.9031	0.7161	9.7791
No log	5.0	55	1.6391	0.5207	9.6564
No log	6.0	66	1.4137	0.8978	9.6135
No log	7.0	77	1.1997	1.1307	9.6135
No log	8.0	88	1.0269	1.4498	9.638
No log	9.0	99	0.8257	1.9986	9.7423
No log	10.0	110	0.6801	2.1989	9.4417
No log	11.0	121	0.5581	2.5771	9.6687
No log	12.0	132	0.4526	2.7754	9.5951
No log	13.0	143	0.3840	3.3881	9.4479
No log	14.0	154	0.3263	3.767	9.589
No log	15.0	165	0.2600	4.1389	9.5399
No log	16.0	176	0.1992	4.6642	9.4785
No log	17.0	187	0.1552	5.3166	9.4724
No log	18.0	198	0.1273	5.5679	9.5153
No log	19.0	209	0.0899	6.497	9.4724
No log	20.0	220	0.0848	6.5853	9.6074
No log	21.0	231	0.0564	7.0719	9.4847
No log	22.0	242	0.0583	7.1069	9.5521
No log	23.0	253	0.0379	7.6262	9.5521
No log	24.0	264	0.0362	7.2957	9.5031
No log	25.0	275	0.0341	8.1019	9.5767
No log	26.0	286	0.0320	8.1036	9.6012
No log	27.0	297	0.0166	8.3655	9.5337
No log	28.0	308	0.0190	8.1402	9.5337
No log	29.0	319	0.0123	8.2834	9.5399
No log	30.0	330	0.0104	8.4782	9.5337
No log	31.0	341	0.0092	8.1852	9.5337
No log	32.0	352	0.0086	8.5036	9.5276
No log	33.0	363	0.0057	8.3105	9.5337
No log	34.0	374	0.0049	8.3607	9.5337
No log	35.0	385	0.0055	8.3729	9.5399
No log	36.0	396	0.0070	8.5598	9.546
No log	37.0	407	0.0088	8.1822	9.5276
No log	38.0	418	0.0061	8.2457	9.5276
No log	39.0	429	0.0054	8.4559	9.5276
No log	40.0	440	0.0052	8.6455	9.5399
No log	41.0	451	0.0065	8.6455	9.5399
No log	42.0	462	0.0017	8.6577	9.5337
No log	43.0	473	0.0035	8.638	9.5337
No log	44.0	484	0.0022	8.6577	9.5337
No log	45.0	495	0.0016	8.5791	9.5337
0.7779	46.0	506	0.0025	8.5791	9.5337
0.7779	47.0	517	0.0014	8.5791	9.5337
0.7779	48.0	528	0.0015	8.5791	9.5337
0.7779	49.0	539	0.0022	8.4109	9.5337
0.7779	50.0	550	0.0014	8.591	9.5337
0.7779	51.0	561	0.0021	8.6455	9.5399
0.7779	52.0	572	0.0011	8.6577	9.5337
0.7779	53.0	583	0.0010	8.6577	9.5337
0.7779	54.0	594	0.0016	8.6036	9.5337
0.7779	55.0	605	0.0009	8.6083	9.5337
0.7779	56.0	616	0.0007	8.6577	9.5337
0.7779	57.0	627	0.0009	8.6577	9.5337
0.7779	58.0	638	0.0035	8.653	9.5337
0.7779	59.0	649	0.0007	8.6577	9.5337
0.7779	60.0	660	0.0003	8.6577	9.5337
0.7779	61.0	671	0.0004	8.6577	9.5337
0.7779	62.0	682	0.0007	8.6577	9.5337
0.7779	63.0	693	0.0004	8.6577	9.5337
0.7779	64.0	704	0.0003	8.6577	9.5337
0.7779	65.0	715	0.0004	8.6577	9.5337
0.7779	66.0	726	0.0002	8.6577	9.5337
0.7779	67.0	737	0.0002	8.6577	9.5337
0.7779	68.0	748	0.0003	8.6577	9.5337
0.7779	69.0	759	0.0007	8.6211	9.5337
0.7779	70.0	770	0.0006	8.6577	9.5337
0.7779	71.0	781	0.0001	8.6577	9.5337
0.7779	72.0	792	0.0001	8.6577	9.5337
0.7779	73.0	803	0.0010	8.6577	9.5337
0.7779	74.0	814	0.0002	8.6577	9.5337
0.7779	75.0	825	0.0005	8.6577	9.5337
0.7779	76.0	836	0.0005	8.6577	9.5337
0.7779	77.0	847	0.0006	8.6577	9.5337
0.7779	78.0	858	0.0003	8.6577	9.5337
0.7779	79.0	869	0.0001	8.6577	9.5337
0.7779	80.0	880	0.0001	8.6577	9.5337
0.7779	81.0	891	0.0001	8.6577	9.5337
0.7779	82.0	902	0.0001	8.6577	9.5337
0.7779	83.0	913	0.0002	8.6577	9.5337
0.7779	84.0	924	0.0005	8.6577	9.5337
0.7779	85.0	935	0.0003	8.6577	9.5337
0.7779	86.0	946	0.0000	8.6577	9.5337
0.7779	87.0	957	0.0001	8.6577	9.5337
0.7779	88.0	968	0.0042	8.653	9.5337
0.7779	89.0	979	0.0001	8.6577	9.5337
0.7779	90.0	990	0.0002	8.6355	9.5337
0.0387	91.0	1001	0.0001	8.6577	9.5337
0.0387	92.0	1012	0.0000	8.6577	9.5337
0.0387	93.0	1023	0.0000	8.6577	9.5337
0.0387	94.0	1034	0.0001	8.6577	9.5337
0.0387	95.0	1045	0.0002	8.591	9.5337
0.0387	96.0	1056	0.0003	8.6577	9.5337
0.0387	97.0	1067	0.0001	8.6577	9.5337
0.0387	98.0	1078	0.0000	8.6577	9.5337
0.0387	99.0	1089	0.0000	8.6577	9.5337
0.0387	100.0	1100	0.0000	8.6577	9.5337
0.0387	101.0	1111	0.0000	8.6577	9.5337
0.0387	102.0	1122	0.0000	8.6577	9.5337
0.0387	103.0	1133	0.0000	8.6577	9.5337
0.0387	104.0	1144	0.0000	8.6577	9.5337
0.0387	105.0	1155	0.0000	8.6577	9.5337
0.0387	106.0	1166	0.0000	8.6577	9.5337
0.0387	107.0	1177	0.0000	8.6577	9.5337
0.0387	108.0	1188	0.0000	8.6577	9.5337
0.0387	109.0	1199	0.0000	8.6577	9.5337
0.0387	110.0	1210	0.0000	8.6577	9.5337
0.0387	111.0	1221	0.0000	8.6577	9.5337
0.0387	112.0	1232	0.0000	8.6577	9.5337
0.0387	113.0	1243	0.0000	8.6577	9.5337
0.0387	114.0	1254	0.0000	8.6577	9.5337
0.0387	115.0	1265	0.0002	8.6036	9.5337
0.0387	116.0	1276	0.0001	8.6577	9.5337
0.0387	117.0	1287	0.0000	8.6577	9.5337
0.0387	118.0	1298	0.0000	8.6577	9.5337
0.0387	119.0	1309	0.0000	8.6577	9.5337
0.0387	120.0	1320	0.0012	8.5758	9.5337
0.0387	121.0	1331	0.0010	8.5758	9.5337
0.0387	122.0	1342	0.0003	8.6577	9.5337
0.0387	123.0	1353	0.0000	8.6577	9.5337
0.0387	124.0	1364	0.0000	8.6577	9.5337
0.0387	125.0	1375	0.0000	8.6577	9.5337
0.0387	126.0	1386	0.0000	8.6577	9.5337
0.0387	127.0	1397	0.0000	8.6577	9.5337
0.0387	128.0	1408	0.0000	8.6577	9.5337
0.0387	129.0	1419	0.0000	8.6577	9.5337
0.0387	130.0	1430	0.0000	8.6577	9.5337
0.0387	131.0	1441	0.0000	8.6577	9.5337
0.0387	132.0	1452	0.0000	8.6577	9.5337
0.0387	133.0	1463	0.0000	8.6577	9.5337
0.0387	134.0	1474	0.0000	8.6577	9.5337
0.0387	135.0	1485	0.0000	8.6577	9.5337
0.0387	136.0	1496	0.0000	8.6577	9.5337
0.0123	137.0	1507	0.0000	8.6577	9.5337
0.0123	138.0	1518	0.0000	8.6577	9.5337
0.0123	139.0	1529	0.0000	8.6577	9.5337
0.0123	140.0	1540	0.0000	8.6577	9.5337
0.0123	141.0	1551	0.0000	8.6577	9.5337
0.0123	142.0	1562	0.0000	8.6577	9.5337
0.0123	143.0	1573	0.0000	8.6577	9.5337
0.0123	144.0	1584	0.0000	8.6577	9.5337
0.0123	145.0	1595	0.0000	8.6577	9.5337
0.0123	146.0	1606	0.0000	8.6577	9.5337
0.0123	147.0	1617	0.0000	8.6577	9.5337
0.0123	148.0	1628	0.0000	8.6577	9.5337
0.0123	149.0	1639	0.0000	8.6577	9.5337
0.0123	150.0	1650	0.0000	8.6577	9.5337
0.0123	151.0	1661	0.0000	8.6577	9.5337
0.0123	152.0	1672	0.0000	8.6577	9.5337
0.0123	153.0	1683	0.0000	8.6577	9.5337
0.0123	154.0	1694	0.0000	8.6577	9.5337
0.0123	155.0	1705	0.0000	8.6577	9.5337
0.0123	156.0	1716	0.0000	8.6577	9.5337
0.0123	157.0	1727	0.0000	8.6577	9.5337
0.0123	158.0	1738	0.0000	8.6577	9.5337
0.0123	159.0	1749	0.0000	8.6577	9.5337
0.0123	160.0	1760	0.0000	8.6577	9.5337
0.0123	161.0	1771	0.0000	8.6577	9.5337
0.0123	162.0	1782	0.0000	8.6577	9.5337
0.0123	163.0	1793	0.0000	8.6577	9.5337
0.0123	164.0	1804	0.0000	8.6577	9.5337
0.0123	165.0	1815	0.0000	8.6577	9.5337
0.0123	166.0	1826	0.0000	8.6577	9.5337
0.0123	167.0	1837	0.0000	8.6577	9.5337
0.0123	168.0	1848	0.0000	8.6577	9.5337
0.0123	169.0	1859	0.0000	8.6577	9.5337
0.0123	170.0	1870	0.0000	8.6577	9.5337
0.0123	171.0	1881	0.0000	8.6577	9.5337
0.0123	172.0	1892	0.0000	8.6577	9.5337
0.0123	173.0	1903	0.0000	8.6577	9.5337
0.0123	174.0	1914	0.0000	8.6577	9.5337
0.0123	175.0	1925	0.0000	8.6577	9.5337
0.0123	176.0	1936	0.0000	8.6577	9.5337
0.0123	177.0	1947	0.0000	8.6577	9.5337
0.0123	178.0	1958	0.0000	8.6577	9.5337
0.0123	179.0	1969	0.0000	8.6577	9.5337
0.0123	180.0	1980	0.0000	8.6577	9.5337
0.0123	181.0	1991	0.0000	8.6577	9.5337
0.0053	182.0	2002	0.0000	8.6577	9.5337
0.0053	183.0	2013	0.0000	8.6577	9.5337
0.0053	184.0	2024	0.0000	8.6577	9.5337
0.0053	185.0	2035	0.0000	8.6577	9.5337
0.0053	186.0	2046	0.0000	8.6577	9.5337
0.0053	187.0	2057	0.0000	8.6577	9.5337
0.0053	188.0	2068	0.0000	8.6577	9.5337
0.0053	189.0	2079	0.0000	8.6577	9.5337
0.0053	190.0	2090	0.0000	8.6577	9.5337
0.0053	191.0	2101	0.0000	8.6577	9.5337
0.0053	192.0	2112	0.0000	8.6577	9.5337
0.0053	193.0	2123	0.0000	8.6577	9.5337
0.0053	194.0	2134	0.0000	8.6577	9.5337
0.0053	195.0	2145	0.0000	8.6577	9.5337
0.0053	196.0	2156	0.0000	8.6577	9.5337
0.0053	197.0	2167	0.0000	8.6577	9.5337
0.0053	198.0	2178	0.0000	8.6577	9.5337
0.0053	199.0	2189	0.0000	8.6577	9.5337
0.0053	200.0	2200	0.0000	8.6577	9.5337

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu121
• Datasets 2.16.1
• Tokenizers 0.15.0