EN, ES and NL to AMR parsing (stratified)
This version was trained on a subselection of the data. The AMR 3.0 corpus was translated to all the relevant languages. We then divided the dataset so that in total we only see a third of each language's dataset (so that in total we only see the full AMR 3.0 corpus in size once). In other words, all languages were undersampled for research purposes.
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: 0.5902
- Smatch Precision: 74.83
- Smatch Recall: 77.62
- Smatch Fscore: 76.2
- Smatch Unparsable: 0
- Percent Not Recoverable: 0.2904
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 |
|---|---|---|---|---|---|---|---|---|
| 0.3941 | 1.0 | 3477 | 1.8519 | 18.33 | 65.69 | 28.66 | 0 | 0.0 |
| 0.3983 | 2.0 | 6954 | 0.9133 | 29.25 | 72.49 | 41.68 | 0 | 0.1742 |
| 0.2932 | 3.0 | 10431 | 0.7729 | 34.75 | 74.02 | 47.29 | 0 | 0.0 |
| 0.2121 | 4.0 | 13908 | 0.7737 | 34.16 | 74.66 | 46.87 | 2 | 0.0 |
| 0.0401 | 5.0 | 17385 | 0.7656 | 36.6 | 75.39 | 49.27 | 0 | 0.0 |
| 0.1274 | 6.0 | 20862 | 0.7373 | 44.18 | 75.99 | 55.88 | 0 | 0.0 |
| 0.0668 | 7.0 | 24339 | 0.6024 | 50.13 | 77.11 | 60.76 | 0 | 0.0 |
| 0.0681 | 8.0 | 27816 | 0.6398 | 50.92 | 77.53 | 61.47 | 0 | 0.0 |
| 0.0381 | 9.0 | 31293 | 0.5849 | 57.36 | 77.99 | 66.1 | 0 | 0.1161 |
| 0.0586 | 10.0 | 34770 | 0.5628 | 59.08 | 77.76 | 67.15 | 0 | 0.0 |
| 0.0074 | 11.0 | 38247 | 0.5632 | 60.25 | 79.02 | 68.37 | 0 | 0.1742 |
| 0.0055 | 12.0 | 41724 | 0.5795 | 59.25 | 78.6 | 67.57 | 0 | 0.2904 |
| 0.0014 | 13.0 | 45201 | 0.5725 | 64.79 | 78.78 | 71.11 | 0 | 0.1161 |
| 0.0063 | 14.0 | 48678 | 0.5494 | 67.65 | 78.58 | 72.71 | 0 | 0.0 |
| 0.012 | 15.0 | 52155 | 0.5821 | 66.07 | 78.66 | 71.82 | 0 | 0.0581 |
| 0.0216 | 16.0 | 55632 | 0.5914 | 66.43 | 78.79 | 72.08 | 0 | 0.0581 |
| 0.0155 | 17.0 | 59109 | 0.5684 | 70.69 | 78.61 | 74.44 | 0 | 0.1161 |
| 0.0019 | 18.0 | 62586 | 0.5796 | 70.35 | 78.68 | 74.28 | 0 | 0.1161 |
| 0.0224 | 19.0 | 66063 | 0.5885 | 69.56 | 78.73 | 73.86 | 0 | 0.1742 |
| 0.0112 | 20.0 | 69540 | 0.5917 | 72.31 | 78.4 | 75.23 | 0 | 0.1161 |
| 0.0014 | 21.0 | 73017 | 0.6102 | 72.56 | 78.24 | 75.3 | 0 | 0.2323 |
| 0.0077 | 22.0 | 76494 | 0.5989 | 73.48 | 77.96 | 75.66 | 0 | 0.1742 |
| 0.0072 | 23.0 | 79971 | 0.5907 | 74.32 | 78.04 | 76.13 | 0 | 0.0581 |
| 0.0066 | 24.0 | 83448 | 0.5899 | 74.62 | 77.87 | 76.21 | 0 | 0.2323 |
| 0.0048 | 25.0 | 86925 | 0.5902 | 74.83 | 77.62 | 76.2 | 0 | 0.2904 |
Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.2
- Tokenizers 0.13.3
