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EN, ES and NL to AMR parsing

This model is a fine-tuned version of facebook/mbart-large-cc25 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6363
  • Smatch Precision: 75.39
  • Smatch Recall: 77.67
  • Smatch Fscore: 76.51
  • Smatch Unparsable: 0
  • Percent Not Recoverable: 0.2129

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: 2
  • eval_batch_size: 2
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 25

Training results

Training Loss Epoch Step Validation Loss Smatch Precision Smatch Recall Smatch Fscore Smatch Unparsable Percent Not Recoverable
0.3131 1.0 10431 1.5867 25.55 66.9 36.97 0 0.0194
0.0897 2.0 20862 1.0779 36.21 72.1 48.2 0 0.0968
0.1392 3.0 31294 0.7726 42.78 75.64 54.65 0 0.1936
0.085 4.0 41725 0.7040 46.38 76.85 57.85 0 0.0774
0.0008 5.0 52156 0.6874 47.47 76.12 58.47 0 0.1161
0.003 6.0 62588 0.6477 53.05 77.36 62.94 0 0.1742
0.0306 7.0 73019 0.6230 52.01 78.19 62.47 0 0.0968
0.0176 8.0 83451 0.6139 52.78 78.53 63.13 0 0.2129
0.0004 9.0 93882 0.6737 58.01 77.55 66.37 0 0.1355
0.0018 10.0 104313 0.6187 58.99 77.99 67.17 0 0.1161
0.0188 11.0 114745 0.6119 62.35 78.01 69.31 0 0.1161
0.0055 12.0 125176 0.6455 62.08 79.07 69.55 0 0.0774
0.0555 13.0 135607 0.6502 62.35 78.17 69.37 0 0.1355
0.0041 14.0 146039 0.6509 65.88 78.31 71.56 0 0.1742
0.0064 15.0 156470 0.6771 66.98 78.33 72.21 0 0.1355
0.0031 16.0 166902 0.6361 68.12 78.66 73.01 0 0.0774
0.0131 17.0 177333 0.6390 69.49 78.66 73.79 0 0.0968
0.0067 18.0 187764 0.6933 69.67 78.4 73.77 0 0.1549
0.0267 19.0 198196 0.6558 70.64 78.71 74.46 0 0.0774
0.0146 20.0 208627 0.6574 71.23 78.93 74.88 0 0.1161
0.0025 21.0 219058 0.6781 71.88 78.28 74.94 0 0.1936
0.0044 22.0 229490 0.6491 73.08 78.57 75.72 0 0.1161
0.0234 23.0 239921 0.6458 74.02 78.33 76.12 0 0.1549
0.0001 24.0 250353 0.6485 74.58 77.98 76.24 0 0.2129
0.0448 25.0 260775 0.6363 75.39 77.67 76.51 0 0.2129

Framework versions

  • Transformers 4.34.0.dev0
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.2
  • Tokenizers 0.13.3
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