t5-small-finetuned-text2log-compute-metrics-v5-400

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: 0.5820
Bleu: 30.1378
Gen Len: 18.568

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: 150
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu	Gen Len
No log	1.0	23	1.5732	8.4784	17.3373
No log	2.0	46	1.1400	22.8492	18.8284
No log	3.0	69	0.9426	27.2247	18.787
No log	4.0	92	0.8110	26.0131	18.7101
No log	5.0	115	0.7559	24.1316	18.7988
No log	6.0	138	0.7102	25.4664	18.7692
No log	7.0	161	0.6807	26.2651	18.6923
No log	8.0	184	0.6575	27.5298	18.6213
No log	9.0	207	0.6369	28.8178	18.5976
No log	10.0	230	0.6250	27.2363	18.6864
No log	11.0	253	0.6143	28.7917	18.6391
No log	12.0	276	0.5973	29.6901	18.7041
No log	13.0	299	0.5864	29.6632	18.6568
No log	14.0	322	0.5827	29.874	18.6391
No log	15.0	345	0.5700	29.6188	18.6864
No log	16.0	368	0.5620	29.9895	18.7278
No log	17.0	391	0.5569	29.3883	18.7278
No log	18.0	414	0.5486	29.783	18.6864
No log	19.0	437	0.5520	29.4199	18.6568
No log	20.0	460	0.5496	28.8502	18.6923
No log	21.0	483	0.5495	29.2369	18.7337
0.8287	22.0	506	0.5411	29.1707	18.6923
0.8287	23.0	529	0.5325	28.8466	18.6923
0.8287	24.0	552	0.5388	29.2703	18.6746
0.8287	25.0	575	0.5288	28.7683	18.7278
0.8287	26.0	598	0.5299	28.3489	18.7337
0.8287	27.0	621	0.5314	28.2042	18.7278
0.8287	28.0	644	0.5159	29.5603	18.7574
0.8287	29.0	667	0.5163	28.8959	18.6982
0.8287	30.0	690	0.5170	30.0671	18.7219
0.8287	31.0	713	0.5202	28.9559	18.6686
0.8287	32.0	736	0.5181	28.1951	18.7574
0.8287	33.0	759	0.5134	28.2097	18.7041
0.8287	34.0	782	0.5134	28.0953	18.7574
0.8287	35.0	805	0.5165	28.4171	18.7988
0.8287	36.0	828	0.5195	28.7953	18.7633
0.8287	37.0	851	0.5185	29.1606	18.7633
0.8287	38.0	874	0.5192	29.5226	18.6805
0.8287	39.0	897	0.5178	28.425	18.7692
0.8287	40.0	920	0.5196	29.4795	18.7041
0.8287	41.0	943	0.5161	29.5127	18.7041
0.8287	42.0	966	0.5164	28.6225	18.7574
0.8287	43.0	989	0.5183	29.0629	18.6272
0.3866	44.0	1012	0.5174	28.6628	18.6213
0.3866	45.0	1035	0.5141	28.499	18.6213
0.3866	46.0	1058	0.5151	28.1309	18.6272
0.3866	47.0	1081	0.5167	29.8871	18.6391
0.3866	48.0	1104	0.5133	28.7513	18.6331
0.3866	49.0	1127	0.5188	28.3089	18.6213
0.3866	50.0	1150	0.5203	28.8714	18.6331
0.3866	51.0	1173	0.5263	28.7644	18.6331
0.3866	52.0	1196	0.5222	28.4817	18.6331
0.3866	53.0	1219	0.5307	28.8117	18.6272
0.3866	54.0	1242	0.5255	29.3844	18.6213
0.3866	55.0	1265	0.5264	29.7108	18.6213
0.3866	56.0	1288	0.5272	29.353	18.6331
0.3866	57.0	1311	0.5283	28.5792	18.6391
0.3866	58.0	1334	0.5301	29.9914	18.6272
0.3866	59.0	1357	0.5320	29.3162	18.6272
0.3866	60.0	1380	0.5380	29.2162	18.6272
0.3866	61.0	1403	0.5349	28.5292	18.6272
0.3866	62.0	1426	0.5313	28.7165	18.6627
0.3866	63.0	1449	0.5335	29.3637	18.6154
0.3866	64.0	1472	0.5350	29.3612	18.568
0.3866	65.0	1495	0.5330	29.1338	18.5621
0.283	66.0	1518	0.5322	29.0514	18.5562
0.283	67.0	1541	0.5362	29.2592	18.5562
0.283	68.0	1564	0.5379	29.6757	18.568
0.283	69.0	1587	0.5386	29.5012	18.5976
0.283	70.0	1610	0.5379	29.6616	18.5917
0.283	71.0	1633	0.5364	29.8762	18.6154
0.283	72.0	1656	0.5392	29.7143	18.6036
0.283	73.0	1679	0.5438	29.385	18.5976
0.283	74.0	1702	0.5386	28.3472	18.6095
0.283	75.0	1725	0.5372	29.1045	18.574
0.283	76.0	1748	0.5406	29.0839	18.6095
0.283	77.0	1771	0.5408	29.735	18.5799
0.283	78.0	1794	0.5406	29.5432	18.6036
0.283	79.0	1817	0.5413	29.1501	18.5976
0.283	80.0	1840	0.5434	29.5822	18.6095
0.283	81.0	1863	0.5491	29.1933	18.5799
0.283	82.0	1886	0.5473	28.9065	18.5385
0.283	83.0	1909	0.5507	29.4129	18.5385
0.283	84.0	1932	0.5534	29.2249	18.5385
0.283	85.0	1955	0.5561	29.6955	18.5799
0.283	86.0	1978	0.5575	29.1081	18.5385
0.2296	87.0	2001	0.5531	29.7633	18.5385
0.2296	88.0	2024	0.5548	30.045	18.5385
0.2296	89.0	2047	0.5567	29.9209	18.5385
0.2296	90.0	2070	0.5577	29.1879	18.5858
0.2296	91.0	2093	0.5602	29.1587	18.5799
0.2296	92.0	2116	0.5595	29.5205	18.5799
0.2296	93.0	2139	0.5605	29.3439	18.5325
0.2296	94.0	2162	0.5583	29.4742	18.5325
0.2296	95.0	2185	0.5576	29.132	18.5325
0.2296	96.0	2208	0.5566	29.0861	18.5325
0.2296	97.0	2231	0.5584	29.6618	18.5385
0.2296	98.0	2254	0.5593	29.1068	18.5325
0.2296	99.0	2277	0.5603	29.7081	18.5385
0.2296	100.0	2300	0.5599	29.6368	18.5325
0.2296	101.0	2323	0.5598	29.6263	18.5325
0.2296	102.0	2346	0.5637	29.6321	18.5385
0.2296	103.0	2369	0.5678	29.6306	18.5266
0.2296	104.0	2392	0.5685	29.3279	18.5325
0.2296	105.0	2415	0.5680	29.1363	18.5621
0.2296	106.0	2438	0.5726	29.2666	18.5385
0.2296	107.0	2461	0.5738	29.2981	18.5385
0.2296	108.0	2484	0.5740	29.5752	18.5385
0.1942	109.0	2507	0.5749	29.5596	18.5385
0.1942	110.0	2530	0.5732	29.6728	18.574
0.1942	111.0	2553	0.5738	29.6052	18.5325
0.1942	112.0	2576	0.5731	29.5143	18.574
0.1942	113.0	2599	0.5744	29.8059	18.574
0.1942	114.0	2622	0.5751	29.6796	18.574
0.1942	115.0	2645	0.5763	29.9279	18.568
0.1942	116.0	2668	0.5746	29.892	18.568
0.1942	117.0	2691	0.5741	29.8104	18.574
0.1942	118.0	2714	0.5759	29.6379	18.574
0.1942	119.0	2737	0.5777	29.7949	18.574
0.1942	120.0	2760	0.5776	29.6297	18.574
0.1942	121.0	2783	0.5789	29.5298	18.574
0.1942	122.0	2806	0.5794	29.6102	18.574
0.1942	123.0	2829	0.5799	29.7981	18.574
0.1942	124.0	2852	0.5811	30.0894	18.574
0.1942	125.0	2875	0.5826	29.9849	18.574
0.1942	126.0	2898	0.5829	29.8349	18.574
0.1942	127.0	2921	0.5817	29.6295	18.574
0.1942	128.0	2944	0.5809	29.5264	18.568
0.1942	129.0	2967	0.5813	29.5858	18.568
0.1942	130.0	2990	0.5843	29.6556	18.568
0.1777	131.0	3013	0.5836	30.0165	18.568
0.1777	132.0	3036	0.5835	29.8399	18.568
0.1777	133.0	3059	0.5824	29.8065	18.568
0.1777	134.0	3082	0.5821	29.8948	18.574
0.1777	135.0	3105	0.5808	29.9342	18.574
0.1777	136.0	3128	0.5810	29.7556	18.574
0.1777	137.0	3151	0.5813	30.0425	18.568
0.1777	138.0	3174	0.5822	30.0719	18.568
0.1777	139.0	3197	0.5823	30.0719	18.568
0.1777	140.0	3220	0.5828	30.1124	18.568
0.1777	141.0	3243	0.5826	30.1451	18.568
0.1777	142.0	3266	0.5828	30.1451	18.568
0.1777	143.0	3289	0.5829	30.1451	18.568
0.1777	144.0	3312	0.5829	30.1451	18.568
0.1777	145.0	3335	0.5825	30.1378	18.568
0.1777	146.0	3358	0.5824	30.1378	18.568
0.1777	147.0	3381	0.5822	30.1378	18.568
0.1777	148.0	3404	0.5820	30.1378	18.568
0.1777	149.0	3427	0.5821	30.1378	18.568
0.1777	150.0	3450	0.5820	30.1378	18.568

Framework versions

Transformers 4.21.2
Pytorch 1.12.1+cu113
Datasets 2.4.0
Tokenizers 0.12.1

anki08/t5-small-finetuned-text2log-compute-metrics-v5-400

t5-small-finetuned-text2log-compute-metrics-v5-400

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions