Uzair54/thesis_only_english

text-to-sparql-t5-small-qald9

This model is a fine-tuned version of yazdipour/text-to-sparql-t5-small-qald9 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0001
• Gen Len: 19.0
• P: 0.6665
• R: 0.1769
• F1: 0.4085
• Bleu-score: 12.0496
• Bleu-precisions: [98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261]
• Bleu-bp: 0.1231

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: 0.0003
• 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: 20
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Gen Len	P	R	F1	Bleu-score	Bleu-precisions	Bleu-bp
No log	1.0	28	0.2187	19.0	0.5442	0.1725	0.3510	9.1204	[84.10326086956522, 63.125, 53.49264705882353, 45.75892857142857]	0.1519
No log	2.0	56	0.0265	19.0	0.6848	0.1878	0.4229	9.2726	[97.80907668231612, 94.84346224677716, 93.73601789709173, 92.02279202279202]	0.0980
No log	3.0	84	0.0092	19.0	0.6648	0.1744	0.4063	11.7575	[97.9502196193265, 97.10391822827938, 96.5376782077393, 95.69620253164557]	0.1214
No log	4.0	112	0.0055	19.0	0.6571	0.1701	0.4004	12.1496	[97.40259740259741, 95.97989949748744, 95.20958083832335, 94.07407407407408]	0.1270
No log	5.0	140	0.0023	19.0	0.6654	0.1752	0.4070	11.8546	[98.09941520467837, 97.44897959183673, 96.95121951219512, 96.21212121212122]	0.1220
No log	6.0	168	0.0010	19.0	0.6665	0.1769	0.4085	12.0496	[98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261]	0.1231
No log	7.0	196	0.0008	19.0	0.6665	0.1769	0.4085	12.0496	[98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261]	0.1231
No log	8.0	224	0.0003	19.0	0.6665	0.1769	0.4085	12.0496	[98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261]	0.1231
No log	9.0	252	0.0005	19.0	0.6665	0.1769	0.4085	12.0496	[98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261]	0.1231
No log	10.0	280	0.0002	19.0	0.6665	0.1769	0.4085	12.0496	[98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261]	0.1231
No log	11.0	308	0.0002	19.0	0.6665	0.1769	0.4085	12.0496	[98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261]	0.1231
No log	12.0	336	0.0001	19.0	0.6665	0.1769	0.4085	12.0496	[98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261]	0.1231
No log	13.0	364	0.0002	19.0	0.6665	0.1769	0.4085	12.0496	[98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261]	0.1231
No log	14.0	392	0.0001	19.0	0.6665	0.1769	0.4085	12.0496	[98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261]	0.1231
No log	15.0	420	0.0001	19.0	0.6665	0.1769	0.4085	12.0496	[98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261]	0.1231
No log	16.0	448	0.0001	19.0	0.6665	0.1769	0.4085	12.0496	[98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261]	0.1231
No log	17.0	476	0.0001	19.0	0.6665	0.1769	0.4085	12.0496	[98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261]	0.1231
0.088	18.0	504	0.0001	19.0	0.6665	0.1769	0.4085	12.0496	[98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261]	0.1231
0.088	19.0	532	0.0001	19.0	0.6665	0.1769	0.4085	12.0496	[98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261]	0.1231
0.088	20.0	560	0.0001	19.0	0.6665	0.1769	0.4085	12.0496	[98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261]	0.1231

Framework versions

• Transformers 4.38.1
• Pytorch 2.1.2
• Datasets 2.1.0
• Tokenizers 0.15.2