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---
tags:
- generated_from_trainer
base_model: jq/nllb-1.3B-many-to-many-step-2k
datasets:
- generator
model-index:
- name: nllb-1.3B-many-to-many-pronouncorrection-charaug
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. -->
# nllb-1.3B-many-to-many-pronouncorrection-charaug
This model is a fine-tuned version of [jq/nllb-1.3B-many-to-many-step-2k](https://huggingface.co/jq/nllb-1.3B-many-to-many-step-2k) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2075
- Bleu Ach Eng: 28.371
- Bleu Lgg Eng: 30.45
- Bleu Lug Eng: 41.978
- Bleu Nyn Eng: 32.296
- Bleu Teo Eng: 30.422
- Bleu Eng Ach: 20.972
- Bleu Eng Lgg: 22.362
- Bleu Eng Lug: 30.359
- Bleu Eng Nyn: 15.305
- Bleu Eng Teo: 21.391
- Bleu Mean: 27.391
## 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: 0.0003
- train_batch_size: 25
- eval_batch_size: 25
- seed: 42
- gradient_accumulation_steps: 120
- total_train_batch_size: 3000
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu Ach Eng | Bleu Lgg Eng | Bleu Lug Eng | Bleu Nyn Eng | Bleu Teo Eng | Bleu Eng Ach | Bleu Eng Lgg | Bleu Eng Lug | Bleu Eng Nyn | Bleu Eng Teo | Bleu Mean |
|:-------------:|:------:|:----:|:---------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:------------:|:---------:|
| No log | 0.0667 | 100 | 1.1541 | 29.033 | 31.47 | 41.596 | 34.169 | 32.442 | 19.677 | 19.657 | 27.889 | 14.554 | 19.143 | 26.963 |
| No log | 1.0301 | 200 | 1.1570 | 27.473 | 31.853 | 41.934 | 32.575 | 31.606 | 20.25 | 20.634 | 28.592 | 13.672 | 19.997 | 26.859 |
| No log | 1.0968 | 300 | 1.1288 | 29.086 | 33.257 | 43.387 | 33.678 | 33.579 | 20.377 | 20.91 | 28.906 | 14.992 | 21.013 | 27.919 |
| No log | 2.0603 | 400 | 1.1620 | 28.122 | 31.46 | 42.491 | 33.304 | 32.331 | 20.282 | 21.604 | 29.577 | 14.961 | 20.94 | 27.507 |
| 0.7273 | 3.0237 | 500 | 1.1661 | 28.311 | 32.122 | 42.825 | 32.333 | 32.415 | 19.799 | 22.287 | 29.558 | 15.708 | 21.948 | 27.731 |
| 0.7273 | 3.0904 | 600 | 1.1652 | 28.593 | 30.62 | 41.964 | 33.383 | 32.08 | 21.142 | 21.8 | 30.215 | 14.717 | 21.744 | 27.626 |
| 0.7273 | 4.0538 | 700 | 1.2075 | 28.371 | 30.45 | 41.978 | 32.296 | 30.422 | 20.972 | 22.362 | 30.359 | 15.305 | 21.391 | 27.391 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.0
- Datasets 2.19.0
- Tokenizers 0.19.1
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