File size: 3,064 Bytes
c9be0ed 1b4aa59 c9be0ed 1b4aa59 c9be0ed e058c29 4a9cdee e058c29 45cec0e e058c29 cd88491 e058c29 4a9cdee e058c29 cd88491 c9be0ed 1b4aa59 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 |
---
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 |