|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
- text-generation-inference |
|
datasets: |
|
- kde4 |
|
metrics: |
|
- bleu |
|
model-index: |
|
- name: bengali-bn-to-en |
|
results: |
|
- task: |
|
name: Sequence-to-sequence Language Modeling |
|
type: text2text-generation |
|
dataset: |
|
name: kde4 |
|
type: kde4 |
|
config: bn-en |
|
split: train |
|
args: bn-en |
|
metrics: |
|
- name: Bleu |
|
type: bleu |
|
value: 50.9475 |
|
language: |
|
- bn |
|
- en |
|
pipeline_tag: text2text-generation |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
### How to use |
|
|
|
You can use this model directly with a pipeline: |
|
|
|
|
|
```python |
|
from transformers import AutoTokenizer, pipeline |
|
tokenizer = AutoTokenizer.from_pretrained("shihab17/bn-to-en-translation") |
|
model = AutoModelForSeq2SeqLM.from_pretrained("shihab17/bn-to-en-translation") |
|
|
|
sentence = 'ম্যাচ শেষে পুরস্কার বিতরণের মঞ্চে তামিমের মুখে মোস্তাফিজের প্রশংসা শোনা গেল' |
|
|
|
translator = pipeline("translation_en_to_bn", model=model, tokenizer=tokenizer) |
|
output = translator(sentence) |
|
print(output) |
|
``` |
|
|
|
|
|
# bengali-en-to-bn |
|
|
|
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-bn-en](https://huggingface.co/Helsinki-NLP/opus-mt-bn-en) on the kde4 dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.6885 |
|
- Bleu: 50.9475 |
|
- Gen Len: 6.7043 |
|
|
|
## 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 |
|
- num_epochs: 10 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |
|
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:| |
|
| 1.8866 | 1.0 | 2047 | 1.6397 | 39.6617 | 8.0651 | |
|
| 1.5769 | 2.0 | 4094 | 1.6160 | 33.0247 | 8.9865 | |
|
| 1.3622 | 3.0 | 6141 | 1.6189 | 53.483 | 6.6037 | |
|
| 1.2317 | 4.0 | 8188 | 1.6280 | 51.6882 | 6.762 | |
|
| 1.1248 | 5.0 | 10235 | 1.6450 | 53.1619 | 6.5515 | |
|
| 1.0297 | 6.0 | 12282 | 1.6587 | 52.3224 | 6.5905 | |
|
| 0.9632 | 7.0 | 14329 | 1.6733 | 52.3362 | 6.5441 | |
|
| 0.8831 | 8.0 | 16376 | 1.6802 | 49.3544 | 6.8272 | |
|
| 0.8291 | 9.0 | 18423 | 1.6868 | 49.9486 | 6.792 | |
|
| 0.8175 | 10.0 | 20470 | 1.6885 | 50.9475 | 6.7043 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.29.1 |
|
- Pytorch 2.0.0+cu118 |
|
- Datasets 2.12.0 |
|
- Tokenizers 0.13.3 |
