---
license: mit
tags:
- generated_from_trainer
datasets:
- iva_mt_wslot
metrics:
- bleu
model-index:
- name: iva_mt_wslot-m2m100_418M-en-hi
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: iva_mt_wslot
      type: iva_mt_wslot
      config: en-hi
      split: validation
      args: en-hi
    metrics:
    - name: Bleu
      type: bleu
      value: 66.566
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# iva_mt_wslot-m2m100_418M-en-hi

This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/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}
}
```