
<!-- 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
{"language": ["bn", "en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "text-generation-inference"], "datasets": ["kde4"], "metrics": ["bleu"], "pipeline_tag": "text2text-generation", "model-index": [{"name": "bengali-bn-to-en", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "kde4", "type": "kde4", "config": "bn-en", "split": "train", "args": "bn-en"}, "metrics": [{"type": "bleu", "value": 50.9475, "name": "Bleu"}]}]}]}
transformers pytorch tensorboard safetensors marian text2text-generation generated_from_trainer text-generation-inference bn en dataset:kde4 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #marian #text2text-generation #generated_from_trainer #text-generation-inference #bn #en #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

### How to use


You can use this model directly with a pipeline:


bengali-en-to-bn
================


This model is a fine-tuned version of 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



### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3



### How to use


You can use this model directly with a pipeline:


bengali-en-to-bn
================


This model is a fine-tuned version of 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 ### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #safetensors #marian #text2text-generation #generated_from_trainer #text-generation-inference #bn #en #dataset-kde4 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### How to use


You can use this model directly with a pipeline:


bengali-en-to-bn
================


This model is a fine-tuned version of 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 ### Framework versions


* Transformers 4.29.1
* Pytorch 2.0.0+cu118
* Datasets 2.12.0
* Tokenizers 0.13.3