transformers safetensors marian text2text-generation generated_from_trainer base_model:Helsinki-NLP/opus-mt-lg-en base_model:finetune:Helsinki-NLP/opus-mt-lg-en license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
base_model: Helsinki-NLP/opus-mt-lg-en
license: apache-2.0
metrics:
- bleu
tags:
- generated_from_trainer
model-index:
- name: opus-mt-lg-en-informal
  results: []
---

<!-- 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. -->

# opus-mt-lg-en-informal

This model is a fine-tuned version of [Helsinki-NLP/opus-mt-lg-en](https://huggingface.co/Helsinki-NLP/opus-mt-lg-en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1343
- Bleu: 0.0
- Bleu Precision: [0.019908987485779295, 0.0006461339651087659, 0.0, 0.0]
- Bleu Brevity Penalty: 1.0
- Bleu Length Ratio: 1.2563
- Bleu Translation Length: 5274
- Bleu Reference Length: 4198

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu | Bleu Precision                                           | Bleu Brevity Penalty | Bleu Length Ratio | Bleu Translation Length | Bleu Reference Length |
|:-------------:|:-----:|:----:|:---------------:|:----:|:--------------------------------------------------------:|:--------------------:|:-----------------:|:-----------------------:|:---------------------:|
| 4.568         | 1.0   | 119  | 0.8525          | 0.0  | [0.011322534989778267, 0.00017458100558659218, 0.0, 0.0] | 1.0                  | 1.5148            | 6359                    | 4198                  |
| 0.6495        | 2.0   | 238  | 0.1701          | 0.0  | [0.012054948135688253, 0.0003405994550408719, 0.0, 0.0]  | 0.8379               | 0.8497            | 3567                    | 4198                  |
| 0.1889        | 3.0   | 357  | 0.1443          | 0.0  | [0.0408483896307934, 0.0010443864229765013, 0.0, 0.0]    | 0.5226               | 0.6065            | 2546                    | 4198                  |
| 0.1513        | 4.0   | 476  | 0.1384          | 0.0  | [0.03887070376432079, 0.0005515719801434088, 0.0, 0.0]   | 0.4879               | 0.5822            | 2444                    | 4198                  |
| 0.1424        | 5.0   | 595  | 0.1357          | 0.0  | [0.027095148078134845, 0.0012106537530266344, 0.0, 0.0]  | 1.0                  | 1.1341            | 4761                    | 4198                  |
| 0.1331        | 6.0   | 714  | 0.1346          | 0.0  | [0.016541609822646658, 0.0005732849226065354, 0.0, 0.0]  | 1.0                  | 1.3969            | 5864                    | 4198                  |
| 0.1265        | 7.0   | 833  | 0.1340          | 0.0  | [0.03237891356703238, 0.0016097875080489374, 0.0, 0.0]   | 0.8839               | 0.8902            | 3737                    | 4198                  |
| 0.1296        | 8.0   | 952  | 0.1339          | 0.0  | [0.026692456479690523, 0.0013218770654329147, 0.0, 0.0]  | 1.0                  | 1.2315            | 5170                    | 4198                  |
| 0.123         | 9.0   | 1071 | 0.1340          | 0.0  | [0.025897226753670472, 0.001404165691551603, 0.0, 0.0]   | 1.0                  | 1.1682            | 4904                    | 4198                  |
| 0.1227        | 10.0  | 1190 | 0.1339          | 0.0  | [0.014839915868193504, 0.0008830579033682352, 0.0, 0.0]  | 1.0                  | 2.0386            | 8558                    | 4198                  |
| 0.117         | 11.0  | 1309 | 0.1343          | 0.0  | [0.019908987485779295, 0.0006461339651087659, 0.0, 0.0]  | 1.0                  | 1.2563            | 5274                    | 4198                  |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1
