t5-small-awesome-text-to-sql-2024-11-10_13-40

This model is a fine-tuned version of cssupport/t5-small-awesome-text-to-sql on the arrow dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1505
  • Gen Len: 19.0
  • Bertscorer-p: 0.5983
  • Bertscorer-r: 0.1002
  • Bertscorer-f1: 0.3375
  • Sacrebleu-score: 6.1735
  • Sacrebleu-precisions: [92.82196987876635, 86.09309987961223, 81.16865589315682, 77.5936294965929]
  • Bleu-bp: 0.0733

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: 10
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Gen Len Bertscorer-p Bertscorer-r Bertscorer-f1 Sacrebleu-score Sacrebleu-precisions Bleu-bp
0.2655 1.0 4772 0.2099 19.0 0.5770 0.0864 0.3203 5.7173 [91.0934769807022, 81.88030009989161, 75.59001146341751, 71.32247244849066] 0.0718
0.1951 2.0 9544 0.1772 19.0 0.5695 0.0718 0.3090 5.7315 [91.38097911302968, 82.52214039836731, 76.55664627495614, 73.06145893164847] 0.0711
0.1609 3.0 14316 0.1628 19.0 0.5960 0.1033 0.3382 6.0737 [92.32304047118862, 84.75338215740487, 79.32502315982035, 75.25860249102807] 0.0735
0.1412 4.0 19088 0.1551 19.0 0.5925 0.0959 0.3326 6.0701 [92.56176903043524, 85.09918369073299, 79.79597353297214, 76.12497023888257] 0.0730
0.1191 5.0 23860 0.1512 19.0 0.5905 0.0928 0.3300 6.0937 [92.29263048778147, 84.9906547977318, 79.83711978971085, 76.22241882452364] 0.0733
0.1063 6.0 28632 0.1486 19.0 0.5959 0.0986 0.3356 6.1128 [92.67271190348113, 85.5578689269597, 80.37916696032137, 76.71086200742904] 0.0731
0.094 7.0 33404 0.1489 19.0 0.5984 0.1024 0.3388 6.1770 [92.60841659561831, 85.6159908960634, 80.52775143703391, 76.7429609924408] 0.0738
0.0875 8.0 38176 0.1496 19.0 0.5960 0.0976 0.3351 6.1421 [92.6290822842547, 85.75971432797346, 80.81931219105543, 77.24221764177369] 0.0732
0.0841 9.0 42948 0.1498 19.0 0.6019 0.1059 0.3424 6.2261 [92.84100049795074, 86.14431816984929, 81.20480235905357, 77.4564647967041] 0.0739
0.0777 10.0 47720 0.1505 19.0 0.5983 0.1002 0.3375 6.1735 [92.82196987876635, 86.09309987961223, 81.16865589315682, 77.5936294965929] 0.0733

Framework versions

  • Transformers 4.45.1
  • Pytorch 2.4.0
  • Datasets 3.0.1
  • Tokenizers 0.20.0
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