---
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: Helsinki-NLPopus-mt-tc-big-en-moroccain_dialect
  results: []
pipeline_tag: translation
---

<!-- 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. -->

<!-- in this model i use transfer learning for translate english to Moroccain dialect (darija). -->

<!-- about dataset used for training model : I used about 18,000 pairs of English and Moroccain Dialect. -->

<!-- my model is trained three times, the last being one epoch. -->

# Helsinki-NLPopus-mt-tc-big-en-moroccain_dialect

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6930
- Bleu: 50.0607
- Gen Len: 14.7048

## Model description

MarianConfig {
  "_name_or_path": "/content/drive/MyDrive/Colab Notebooks/big_helsinki_eng_dar",
  "activation_dropout": 0.0,
  "activation_function": "relu",
  "architectures": [
    "MarianMTModel"
  ],
  "attention_dropout": 0.0,
  "bad_words_ids": [
    [
      61246
    ]
  ],
  "bos_token_id": 0,
  "classifier_dropout": 0.0,
  "d_model": 1024,
  "decoder_attention_heads": 16,
  "decoder_ffn_dim": 4096,
  "decoder_layerdrop": 0.0,
  "decoder_layers": 6,
  "decoder_start_token_id": 61246,
  "decoder_vocab_size": 61247,
  "dropout": 0.1,
  "encoder_attention_heads": 16,
  "encoder_ffn_dim": 4096,
  "encoder_layerdrop": 0.0,
  "encoder_layers": 6,
  "eos_token_id": 25897,
  "forced_eos_token_id": 25897,
  "init_std": 0.02,
  "is_encoder_decoder": true,
  "max_length": 512,
  "max_position_embeddings": 1024,
  "model_type": "marian",
  "normalize_embedding": false,
  "num_beams": 4,
  "num_hidden_layers": 6,
  "pad_token_id": 61246,
  "scale_embedding": true,
  "share_encoder_decoder_embeddings": true,
  "static_position_embeddings": true,
  "torch_dtype": "float32",
  "transformers_version": "4.28.0",
  "use_cache": true,
  "vocab_size": 61247
}

## Intended uses & limitations

More information needed

## Training and evaluation data

DatasetDict({
    train: Dataset({
        features: ['input_ids', 'attention_mask', 'labels'],
        num_rows: 15443
    })
    test: Dataset({
        features: ['input_ids', 'attention_mask', 'labels'],
        num_rows: 813
    })
})

## Training procedure

Using transfer learning due to limited data in the Moroccan dialect.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 4000
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu    | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 0.617         | 1.0   | 1931 | 0.6930          | 50.0607 | 14.7048 |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3