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mbart-large-cc25-ft-amr30-es_nl
BramVanroy/mbart-large-cc25-ft-amr30-es_nl
mbart-large-cc25-ft-amr30-es_nl is a machine learning model from BramVanroy. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
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: 0.6542 - Smatch Precision: 73.41 - Smatch Recall: 76.04 - Smatch Fs…
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At a glance
- Library
- transformers
- Model type
- mbart
- Access
- Public
- Created
- Oct 24, 2023
- Updated
- Oct 24, 2023
- SHA
- f6bf5f5e
Base models
Model card
From the Hugging Face model README
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: 0.6542
- Smatch Precision: 73.41
- Smatch Recall: 76.04
- Smatch Fscore: 74.7
- Smatch Unparsable: 0
- Percent Not Recoverable: 0.2613
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 |
|---|---|---|---|---|---|---|---|---|
| 0.2675 | 1.0 | 6954 | 1.3790 | 23.26 | 65.74 | 34.36 | 0 | 0.0 |
| 0.1137 | 2.0 | 13908 | 1.0480 | 32.79 | 71.81 | 45.02 | 0 | 0.0 |
| 0.1606 | 3.0 | 20862 | 0.8573 | 38.99 | 72.53 | 50.72 | 0 | 0.0581 |
| 0.0923 | 4.0 | 27817 | 0.7614 | 40.4 | 75.22 | 52.56 | 0 | 0.0290 |
| 0.0292 | 5.0 | 34771 | 0.7935 | 46.44 | 75.63 | 57.54 | 0 | 0.0290 |
| 0.0106 | 6.0 | 41725 | 0.7326 | 49.54 | 75.8 | 59.92 | 0 | 0.0 |
| 0.0054 | 7.0 | 48679 | 0.6385 | 51.35 | 76.11 | 61.33 | 0 | 0.0290 |
| 0.048 | 8.0 | 55634 | 0.6489 | 53.03 | 76.79 | 62.74 | 0 | 0.0581 |
| 0.0334 | 9.0 | 62588 | 0.6128 | 59.05 | 77.3 | 66.95 | 0 | 0.0581 |
| 0.0393 | 10.0 | 69542 | 0.6242 | 57.91 | 77.02 | 66.11 | 0 | 0.0871 |
| 0.0251 | 11.0 | 76496 | 0.6417 | 58.46 | 77.31 | 66.58 | 0 | 0.1742 |
| 0.0035 | 12.0 | 83451 | 0.6271 | 62.28 | 76.99 | 68.86 | 0 | 0.0581 |
| 0.0228 | 13.0 | 90405 | 0.6685 | 62.47 | 76.97 | 68.97 | 0 | 0.1452 |
| 0.0119 | 14.0 | 97359 | 0.6414 | 63.12 | 77.23 | 69.47 | 0 | 0.1161 |
| 0.0066 | 15.0 | 104313 | 0.6515 | 65.63 | 77.02 | 70.87 | 0 | 0.0871 |
| 0.0025 | 16.0 | 111268 | 0.6467 | 67.05 | 77.35 | 71.83 | 0 | 0.0871 |
| 0.0024 | 17.0 | 118222 | 0.6657 | 65.47 | 77.13 | 70.82 | 0 | 0.0581 |
| 0.0223 | 18.0 | 125176 | 0.6754 | 67.56 | 77.21 | 72.06 | 0 | 0.1452 |
| 0.034 | 19.0 | 132130 | 0.6569 | 68.47 | 76.97 | 72.47 | 0 | 0.1161 |
| 0.007 | 20.0 | 139085 | 0.6734 | 69.86 | 77.17 | 73.34 | 0 | 0.2033 |
| 0.0224 | 21.0 | 146039 | 0.6544 | 70.95 | 76.72 | 73.72 | 0 | 0.1742 |
| 0.005 | 22.0 | 152993 | 0.6619 | 72.18 | 76.83 | 74.43 | 0 | 0.1742 |
| 0.0055 | 23.0 | 159947 | 0.6683 | 72.21 | 76.42 | 74.26 | 0 | 0.2323 |
| 0.0 | 24.0 | 166902 | 0.6585 | 72.8 | 76.3 | 74.51 | 0 | 0.2033 |
| 0.0693 | 25.0 | 173850 | 0.6542 | 73.41 | 76.04 | 74.7 | 0 | 0.2613 |
Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.2
- Tokenizers 0.13.3
- Library
- transformers
- Type
- mbart
- Languages
- nl, es
- Created
- Oct 24, 2023
- Updated
- Oct 24, 2023