t5-small-finetuned-text2log-finetuned-nl-to-fol

This model is a fine-tuned version of mrm8488/t5-small-finetuned-text2log on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0267
• Bleu: 36.0754
• Gen Len: 18.6964

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: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• 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	14	1.8310	1.6355	11.0
No log	2.0	28	1.0849	18.3378	18.8661
No log	3.0	42	0.8576	25.1889	18.7411
No log	4.0	56	0.7132	26.484	18.7589
No log	5.0	70	0.5993	26.645	18.7054
No log	6.0	84	0.5101	26.1493	18.6964
No log	7.0	98	0.4494	26.0154	18.7054
No log	8.0	112	0.4026	28.7807	18.7054
No log	9.0	126	0.3672	28.3994	18.7054
No log	10.0	140	0.3403	28.6664	18.6429
No log	11.0	154	0.3181	29.5906	18.7321
No log	12.0	168	0.3000	30.4838	18.7857
No log	13.0	182	0.2870	29.3458	18.6964
No log	14.0	196	0.2731	28.5458	18.6429
No log	15.0	210	0.2582	31.0445	18.6429
No log	16.0	224	0.2491	31.14	18.6964
No log	17.0	238	0.2367	30.9688	18.7054
No log	18.0	252	0.2262	31.2437	18.7054
No log	19.0	266	0.2206	30.6938	18.6518
No log	20.0	280	0.2137	31.1833	18.7054
No log	21.0	294	0.2043	31.1204	18.7054
No log	22.0	308	0.1978	31.9392	18.7054
No log	23.0	322	0.1915	31.9986	18.7054
No log	24.0	336	0.1850	32.4128	18.7054
No log	25.0	350	0.1789	32.2531	18.7054
No log	26.0	364	0.1716	31.5966	18.7054
No log	27.0	378	0.1670	31.4045	18.7054
No log	28.0	392	0.1631	31.6554	18.7054
No log	29.0	406	0.1575	31.6052	18.7054
No log	30.0	420	0.1530	31.6284	18.7054
No log	31.0	434	0.1500	31.6599	18.7054
No log	32.0	448	0.1449	32.2263	18.7054
No log	33.0	462	0.1409	31.7732	18.7054
No log	34.0	476	0.1385	31.6335	18.7054
No log	35.0	490	0.1323	31.5864	18.7054
0.5638	36.0	504	0.1300	31.8469	18.7054
0.5638	37.0	518	0.1294	31.571	18.6964
0.5638	38.0	532	0.1255	31.6656	18.6964
0.5638	39.0	546	0.1239	31.6699	18.6964
0.5638	40.0	560	0.1223	31.6207	18.6964
0.5638	41.0	574	0.1179	31.7496	18.6964
0.5638	42.0	588	0.1155	31.8694	18.6964
0.5638	43.0	602	0.1139	31.8661	18.6696
0.5638	44.0	616	0.1119	31.9764	18.6964
0.5638	45.0	630	0.1097	31.9264	18.6964
0.5638	46.0	644	0.1077	31.8732	18.6964
0.5638	47.0	658	0.1065	32.1597	18.6964
0.5638	48.0	672	0.1046	31.984	18.6964
0.5638	49.0	686	0.1059	32.0725	18.6964
0.5638	50.0	700	0.1009	31.9764	18.6964
0.5638	51.0	714	0.1010	31.9335	18.6964
0.5638	52.0	728	0.1007	31.9461	18.6964
0.5638	53.0	742	0.0983	32.3444	18.6964
0.5638	54.0	756	0.0948	32.2457	18.6964
0.5638	55.0	770	0.0925	32.37	18.6964
0.5638	56.0	784	0.0915	32.6063	18.6964
0.5638	57.0	798	0.0891	32.5387	18.6964
0.5638	58.0	812	0.0886	32.5844	18.6964
0.5638	59.0	826	0.0873	32.8077	18.6964
0.5638	60.0	840	0.0850	32.8999	18.6964
0.5638	61.0	854	0.0844	32.9431	18.6964
0.5638	62.0	868	0.0835	32.4755	18.6339
0.5638	63.0	882	0.0829	32.8512	18.6964
0.5638	64.0	896	0.0834	32.9652	18.6964
0.5638	65.0	910	0.0803	32.7494	18.6964
0.5638	66.0	924	0.0803	33.1091	18.6964
0.5638	67.0	938	0.0799	32.7754	18.6964
0.5638	68.0	952	0.0773	32.691	18.6964
0.5638	69.0	966	0.0766	32.6675	18.6964
0.5638	70.0	980	0.0757	32.8043	18.6964
0.5638	71.0	994	0.0747	33.0999	18.6964
0.1955	72.0	1008	0.0758	32.831	18.6964
0.1955	73.0	1022	0.0716	33.0565	18.6964
0.1955	74.0	1036	0.0720	32.9855	18.6964
0.1955	75.0	1050	0.0708	33.2507	18.6964
0.1955	76.0	1064	0.0693	33.3378	18.6964
0.1955	77.0	1078	0.0708	33.4991	18.6964
0.1955	78.0	1092	0.0680	33.6325	18.6964
0.1955	79.0	1106	0.0670	34.0041	18.6964
0.1955	80.0	1120	0.0665	33.8912	18.6964
0.1955	81.0	1134	0.0667	33.8682	18.6964
0.1955	82.0	1148	0.0669	33.9798	18.6964
0.1955	83.0	1162	0.0661	33.8137	18.6964
0.1955	84.0	1176	0.0640	34.0485	18.6964
0.1955	85.0	1190	0.0639	34.0046	18.6964
0.1955	86.0	1204	0.0628	33.9692	18.6964
0.1955	87.0	1218	0.0620	34.0327	18.6964
0.1955	88.0	1232	0.0633	33.6707	18.6964
0.1955	89.0	1246	0.0624	33.6019	18.6964
0.1955	90.0	1260	0.0602	33.7535	18.6964
0.1955	91.0	1274	0.0623	33.96	18.6964
0.1955	92.0	1288	0.0596	33.9758	18.6964
0.1955	93.0	1302	0.0575	34.0948	18.6964
0.1955	94.0	1316	0.0580	34.0769	18.6964
0.1955	95.0	1330	0.0564	34.0158	18.6964
0.1955	96.0	1344	0.0556	34.0563	18.6964
0.1955	97.0	1358	0.0554	33.9136	18.6964
0.1955	98.0	1372	0.0561	34.1803	18.6964
0.1955	99.0	1386	0.0528	34.1803	18.6964
0.1955	100.0	1400	0.0541	34.1803	18.6964
0.1955	101.0	1414	0.0586	34.2268	18.6964
0.1955	102.0	1428	0.0527	34.2268	18.6964
0.1955	103.0	1442	0.0521	34.2876	18.6964
0.1955	104.0	1456	0.0512	34.1363	18.6964
0.1955	105.0	1470	0.0505	34.1803	18.6964
0.1955	106.0	1484	0.0507	34.3964	18.6964
0.1955	107.0	1498	0.0531	34.3264	18.6964
0.1348	108.0	1512	0.0503	34.3964	18.6964
0.1348	109.0	1526	0.0479	34.2748	18.6964
0.1348	110.0	1540	0.0486	34.4116	18.6964
0.1348	111.0	1554	0.0469	34.4479	18.6964
0.1348	112.0	1568	0.0466	34.4479	18.6964
0.1348	113.0	1582	0.0459	34.6456	18.6964
0.1348	114.0	1596	0.0470	34.5772	18.6964
0.1348	115.0	1610	0.0474	34.7063	18.6964
0.1348	116.0	1624	0.0470	34.8006	18.6964
0.1348	117.0	1638	0.0446	34.8006	18.6964
0.1348	118.0	1652	0.0450	34.6814	18.6964
0.1348	119.0	1666	0.0448	34.6371	18.6964
0.1348	120.0	1680	0.0436	34.8505	18.6964
0.1348	121.0	1694	0.0436	34.8948	18.6964
0.1348	122.0	1708	0.0420	35.0312	18.6964
0.1348	123.0	1722	0.0414	35.0994	18.6964
0.1348	124.0	1736	0.0419	34.8656	18.6964
0.1348	125.0	1750	0.0410	35.3786	18.6964
0.1348	126.0	1764	0.0411	35.3786	18.6964
0.1348	127.0	1778	0.0420	35.3341	18.6964
0.1348	128.0	1792	0.0397	35.3341	18.6964
0.1348	129.0	1806	0.0391	35.3341	18.6964
0.1348	130.0	1820	0.0384	35.3341	18.6964
0.1348	131.0	1834	0.0383	35.4629	18.6964
0.1348	132.0	1848	0.0378	35.5074	18.6964
0.1348	133.0	1862	0.0371	35.5755	18.6964
0.1348	134.0	1876	0.0369	35.7042	18.6964
0.1348	135.0	1890	0.0367	35.7042	18.6964
0.1348	136.0	1904	0.0372	35.5847	18.6964
0.1348	137.0	1918	0.0364	35.8328	18.6964
0.1348	138.0	1932	0.0361	35.7881	18.6964
0.1348	139.0	1946	0.0358	35.7881	18.6964
0.1348	140.0	1960	0.0358	35.6669	18.6964
0.1348	141.0	1974	0.0365	35.7132	18.6964
0.1348	142.0	1988	0.0354	35.7648	18.6964
0.104	143.0	2002	0.0350	35.7648	18.6964
0.104	144.0	2016	0.0348	35.5989	18.6964
0.104	145.0	2030	0.0351	35.5543	18.6964
0.104	146.0	2044	0.0356	35.5543	18.6964
0.104	147.0	2058	0.0346	35.5989	18.6964
0.104	148.0	2072	0.0336	35.5989	18.6964
0.104	149.0	2086	0.0335	35.7648	18.6964
0.104	150.0	2100	0.0340	35.6669	18.6964
0.104	151.0	2114	0.0332	35.8328	18.6964
0.104	152.0	2128	0.0330	35.6669	18.6964
0.104	153.0	2142	0.0333	35.6669	18.6964
0.104	154.0	2156	0.0326	35.6669	18.6964
0.104	155.0	2170	0.0321	35.6669	18.6964
0.104	156.0	2184	0.0312	35.7274	18.6964
0.104	157.0	2198	0.0311	35.7274	18.6964
0.104	158.0	2212	0.0309	35.7737	18.6964
0.104	159.0	2226	0.0312	35.7274	18.6964
0.104	160.0	2240	0.0305	35.7274	18.6964
0.104	161.0	2254	0.0301	35.7274	18.6964
0.104	162.0	2268	0.0304	35.7274	18.6964
0.104	163.0	2282	0.0309	35.7274	18.6964
0.104	164.0	2296	0.0301	35.7274	18.6964
0.104	165.0	2310	0.0296	35.7274	18.6964
0.104	166.0	2324	0.0292	35.7274	18.6964
0.104	167.0	2338	0.0290	35.7274	18.6964
0.104	168.0	2352	0.0295	35.7737	18.6964
0.104	169.0	2366	0.0298	35.7737	18.6964
0.104	170.0	2380	0.0290	35.7737	18.6964
0.104	171.0	2394	0.0287	35.7737	18.6964
0.104	172.0	2408	0.0289	35.7737	18.6964
0.104	173.0	2422	0.0283	35.7737	18.6964
0.104	174.0	2436	0.0280	35.7737	18.6964
0.104	175.0	2450	0.0280	35.7737	18.6964
0.104	176.0	2464	0.0278	35.7737	18.6964
0.104	177.0	2478	0.0275	35.8416	18.6964
0.104	178.0	2492	0.0274	35.8416	18.6964
0.0892	179.0	2506	0.0272	35.8416	18.6964
0.0892	180.0	2520	0.0272	35.8416	18.6964
0.0892	181.0	2534	0.0275	35.8416	18.6964
0.0892	182.0	2548	0.0274	35.8416	18.6964
0.0892	183.0	2562	0.0273	35.8416	18.6964
0.0892	184.0	2576	0.0274	35.8416	18.6964
0.0892	185.0	2590	0.0275	35.8416	18.6964
0.0892	186.0	2604	0.0275	35.8416	18.6964
0.0892	187.0	2618	0.0277	35.8416	18.6964
0.0892	188.0	2632	0.0276	35.9612	18.6964
0.0892	189.0	2646	0.0271	35.9612	18.6964
0.0892	190.0	2660	0.0269	35.9612	18.6964
0.0892	191.0	2674	0.0267	35.9612	18.6964
0.0892	192.0	2688	0.0266	35.9612	18.6964
0.0892	193.0	2702	0.0265	35.9612	18.6964
0.0892	194.0	2716	0.0265	35.9612	18.6964
0.0892	195.0	2730	0.0266	36.0754	18.6964
0.0892	196.0	2744	0.0266	36.0754	18.6964
0.0892	197.0	2758	0.0267	36.0754	18.6964
0.0892	198.0	2772	0.0267	36.0754	18.6964
0.0892	199.0	2786	0.0267	36.0754	18.6964
0.0892	200.0	2800	0.0267	36.0754	18.6964

Framework versions

• Transformers 4.21.1
• Pytorch 1.12.0+cu113
• Datasets 2.4.0
• Tokenizers 0.12.1