t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol-finetuned-nl-to-fol-version2

This model is a fine-tuned version of anki08/t5-small-finetuned-text2log-finetuned-nl-to-fol-finetuned-nl-to-fol on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0069
• Bleu: 28.1311
• Gen Len: 18.7412

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: 100
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu	Gen Len
No log	1.0	22	0.0692	27.4908	18.7353
No log	2.0	44	0.0631	27.554	18.7294
No log	3.0	66	0.0533	27.6007	18.7294
No log	4.0	88	0.0484	27.6446	18.7294
No log	5.0	110	0.0439	27.6401	18.7294
No log	6.0	132	0.0404	27.5117	18.7294
No log	7.0	154	0.0389	27.6358	18.7294
No log	8.0	176	0.0362	27.6358	18.7294
No log	9.0	198	0.0339	27.5731	18.7294
No log	10.0	220	0.0319	27.2326	18.6882
No log	11.0	242	0.0298	27.2326	18.6882
No log	12.0	264	0.0293	27.5498	18.7294
No log	13.0	286	0.0276	27.6566	18.7294
No log	14.0	308	0.0268	27.6566	18.7294
No log	15.0	330	0.0251	27.6107	18.7294
No log	16.0	352	0.0239	27.7096	18.7294
No log	17.0	374	0.0228	27.6716	18.7294
No log	18.0	396	0.0231	27.8083	18.7294
No log	19.0	418	0.0218	27.4838	18.6882
No log	20.0	440	0.0212	27.4712	18.6882
No log	21.0	462	0.0197	27.8787	18.7353
No log	22.0	484	0.0207	27.6899	18.6941
0.1026	23.0	506	0.0186	27.6376	18.6941
0.1026	24.0	528	0.0202	27.6672	18.6941
0.1026	25.0	550	0.0174	28.0172	18.7412
0.1026	26.0	572	0.0170	27.8714	18.7412
0.1026	27.0	594	0.0164	27.7423	18.7412
0.1026	28.0	616	0.0164	27.8278	18.7412
0.1026	29.0	638	0.0163	27.8278	18.7412
0.1026	30.0	660	0.0158	27.907	18.7412
0.1026	31.0	682	0.0165	27.7752	18.7412
0.1026	32.0	704	0.0147	27.8284	18.7412
0.1026	33.0	726	0.0150	27.8862	18.7412
0.1026	34.0	748	0.0148	27.8402	18.7412
0.1026	35.0	770	0.0141	27.8353	18.7412
0.1026	36.0	792	0.0142	27.858	18.7412
0.1026	37.0	814	0.0143	27.858	18.7412
0.1026	38.0	836	0.0158	27.8353	18.7412
0.1026	39.0	858	0.0125	27.8913	18.7412
0.1026	40.0	880	0.0121	27.9167	18.7412
0.1026	41.0	902	0.0122	27.9569	18.7412
0.1026	42.0	924	0.0126	27.9569	18.7412
0.1026	43.0	946	0.0120	28.001	18.7412
0.1026	44.0	968	0.0125	28.0079	18.7412
0.1026	45.0	990	0.0115	28.0079	18.7412
0.072	46.0	1012	0.0113	27.9851	18.7412
0.072	47.0	1034	0.0113	28.0184	18.7412
0.072	48.0	1056	0.0110	28.0184	18.7412
0.072	49.0	1078	0.0108	28.0184	18.7412
0.072	50.0	1100	0.0107	28.0184	18.7412
0.072	51.0	1122	0.0101	28.0184	18.7412
0.072	52.0	1144	0.0102	28.0184	18.7412
0.072	53.0	1166	0.0099	28.0184	18.7412
0.072	54.0	1188	0.0100	28.0184	18.7412
0.072	55.0	1210	0.0102	28.0184	18.7412
0.072	56.0	1232	0.0095	28.0184	18.7412
0.072	57.0	1254	0.0098	28.0184	18.7412
0.072	58.0	1276	0.0092	28.0184	18.7412
0.072	59.0	1298	0.0090	28.0184	18.7412
0.072	60.0	1320	0.0095	28.0184	18.7412
0.072	61.0	1342	0.0092	27.9674	18.7412
0.072	62.0	1364	0.0091	27.9419	18.7412
0.072	63.0	1386	0.0100	27.9419	18.7412
0.072	64.0	1408	0.0084	28.0752	18.7412
0.072	65.0	1430	0.0086	28.0192	18.7412
0.072	66.0	1452	0.0084	28.0192	18.7412
0.072	67.0	1474	0.0085	28.0192	18.7412
0.072	68.0	1496	0.0087	28.0192	18.7412
0.0575	69.0	1518	0.0084	28.0192	18.7412
0.0575	70.0	1540	0.0080	28.0192	18.7412
0.0575	71.0	1562	0.0082	28.0192	18.7412
0.0575	72.0	1584	0.0080	28.0192	18.7412
0.0575	73.0	1606	0.0075	28.0192	18.7412
0.0575	74.0	1628	0.0079	28.0192	18.7412
0.0575	75.0	1650	0.0078	28.0752	18.7412
0.0575	76.0	1672	0.0076	28.1311	18.7412
0.0575	77.0	1694	0.0073	28.1311	18.7412
0.0575	78.0	1716	0.0074	28.1311	18.7412
0.0575	79.0	1738	0.0072	28.1311	18.7412
0.0575	80.0	1760	0.0078	28.1311	18.7412
0.0575	81.0	1782	0.0077	28.1311	18.7412
0.0575	82.0	1804	0.0071	28.1311	18.7412
0.0575	83.0	1826	0.0072	28.1311	18.7412
0.0575	84.0	1848	0.0075	28.1311	18.7412
0.0575	85.0	1870	0.0071	28.1311	18.7412
0.0575	86.0	1892	0.0070	28.1311	18.7412
0.0575	87.0	1914	0.0069	28.1311	18.7412
0.0575	88.0	1936	0.0069	28.1311	18.7412
0.0575	89.0	1958	0.0069	28.1311	18.7412
0.0575	90.0	1980	0.0069	28.1311	18.7412
0.0509	91.0	2002	0.0069	28.1311	18.7412
0.0509	92.0	2024	0.0070	28.1311	18.7412
0.0509	93.0	2046	0.0069	28.1311	18.7412
0.0509	94.0	2068	0.0070	28.1311	18.7412
0.0509	95.0	2090	0.0069	28.1311	18.7412
0.0509	96.0	2112	0.0069	28.1311	18.7412
0.0509	97.0	2134	0.0069	28.1311	18.7412
0.0509	98.0	2156	0.0069	28.1311	18.7412
0.0509	99.0	2178	0.0069	28.1311	18.7412
0.0509	100.0	2200	0.0069	28.1311	18.7412

Framework versions

• Transformers 4.21.1
• Pytorch 1.12.1+cu113
• Datasets 2.4.0
• Tokenizers 0.12.1