iva_mt_wslot-m2m100_418M-en-hi
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: 0.0128
- Bleu: 66.566
- Gen Len: 21.9557
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 |
|---|---|---|---|---|---|
| 0.0173 | 1.0 | 1676 | 0.0143 | 62.0504 | 21.7847 |
| 0.0116 | 2.0 | 3352 | 0.0127 | 64.7753 | 22.0289 |
| 0.0085 | 3.0 | 5028 | 0.0123 | 65.6406 | 21.8877 |
| 0.0064 | 4.0 | 6704 | 0.0123 | 66.4173 | 21.9532 |
| 0.0048 | 5.0 | 8380 | 0.0125 | 66.2846 | 21.8706 |
| 0.0037 | 6.0 | 10056 | 0.0127 | 66.4123 | 21.8911 |
| 0.003 | 7.0 | 11732 | 0.0128 | 66.566 | 21.9557 |
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}
}