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iva_mt_wslot-m2m100_418M-en-tr
This model is a fine-tuned version of facebook/m2m100_418M on the iva_mt_wslot dataset. It achieves the following results on the evaluation set:
- Loss: 0.0136
- Bleu: 63.3126
- Gen Len: 19.5834
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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
---|---|---|---|---|---|
0.0175 | 1.0 | 2068 | 0.0151 | 59.2285 | 19.5597 |
0.0121 | 2.0 | 4136 | 0.0138 | 60.2539 | 19.3643 |
0.0087 | 3.0 | 6204 | 0.0134 | 61.6109 | 19.3507 |
0.0065 | 4.0 | 8272 | 0.0134 | 61.9941 | 19.6187 |
0.0049 | 5.0 | 10340 | 0.0134 | 63.4822 | 19.6174 |
0.0039 | 6.0 | 12408 | 0.0136 | 62.9517 | 19.6493 |
0.0031 | 7.0 | 14476 | 0.0136 | 63.3126 | 19.5834 |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
Citation
If you use this model, please cite the following:
@article{Sowanski2023SlotLI,
title={Slot Lost in Translation? Not Anymore: A Machine Translation Model for Virtual Assistants with Type-Independent Slot Transfer},
author={Marcin Sowanski and Artur Janicki},
journal={2023 30th International Conference on Systems, Signals and Image Processing (IWSSIP)},
year={2023},
pages={1-5}
}