
# text_generation_bangla_model
BanglaCLM dataset: 

- OSCAR: 12.84GB

- Wikipedia dump: 6.24GB

- ProthomAlo: 3.92GB

- Kalerkantho: 3.24GB


## Model description

- context size : 128


## Training and evaluation data
The BanglaCLM data set is divided into a training set (90%)and a validation set (10%).


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:

- Batch size: 32

- Initial learning rate: 5e-5

- Number of warmup steps: 10000

- Weight decay rate: 0.01

- Tokenization algorithm: BPE

- Vocabulary size of tokenizer: 50256

- Total trainable params: 124,439,808

- Epochs: 40

- Number of training steps: 40772228

- training_precision: float32


### Training results

perplexity score: 2.86.


### Framework versions

- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.10.0
- Tokenizers 0.13.2

### Citation
If you find this model helpful, please cite.
```
@INPROCEEDINGS{10303383,
  author={Salim, Md. Shahidul and Murad, Hasan and Das, Dola and Ahmed, Faisal},
  booktitle={2023 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD)}, 
  title={BanglaGPT: A Generative Pretrained Transformer-Based Model for Bangla Language}, 
  year={2023},
  volume={},
  number={},
  pages={56-59},
  doi={10.1109/ICICT4SD59951.2023.10303383}}

```

{}
transformers pytorch tf gpt2 text-generation autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tf #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


# text_generation_bangla_model
BanglaCLM dataset: 

- OSCAR: 12.84GB

- Wikipedia dump: 6.24GB

- ProthomAlo: 3.92GB

- Kalerkantho: 3.24GB


## Model description

- context size : 128


## Training and evaluation data
The BanglaCLM data set is divided into a training set (90%)and a validation set (10%).


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:

- Batch size: 32

- Initial learning rate: 5e-5

- Number of warmup steps: 10000

- Weight decay rate: 0.01

- Tokenization algorithm: BPE

- Vocabulary size of tokenizer: 50256

- Total trainable params: 124,439,808

- Epochs: 40

- Number of training steps: 40772228

- training_precision: float32


### Training results

perplexity score: 2.86.


### Framework versions

- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.10.0
- Tokenizers 0.13.2

If you find this model helpful, please cite.


# text_generation_bangla_model
BanglaCLM dataset: 

- OSCAR: 12.84GB

- Wikipedia dump: 6.24GB

- ProthomAlo: 3.92GB

- Kalerkantho: 3.24GB ## Model description

- context size : 128 ## Training and evaluation data
The BanglaCLM data set is divided into a training set (90%)and a validation set (10%). ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:

- Batch size: 32

- Initial learning rate: 5e-5

- Number of warmup steps: 10000

- Weight decay rate: 0.01

- Tokenization algorithm: BPE

- Vocabulary size of tokenizer: 50256

- Total trainable params: 124,439,808

- Epochs: 40

- Number of training steps: 40772228

- training_precision: float32 ### Training results

perplexity score: 2.86. ### Framework versions

- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.10.0
- Tokenizers 0.13.2

If you find this model helpful, please cite.
TAGS
#transformers #pytorch #tf #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # text_generation_bangla_model
BanglaCLM dataset: 

- OSCAR: 12.84GB

- Wikipedia dump: 6.24GB

- ProthomAlo: 3.92GB

- Kalerkantho: 3.24GB ## Model description

- context size : 128 ## Training and evaluation data
The BanglaCLM data set is divided into a training set (90%)and a validation set (10%). ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:

- Batch size: 32

- Initial learning rate: 5e-5

- Number of warmup steps: 10000

- Weight decay rate: 0.01

- Tokenization algorithm: BPE

- Vocabulary size of tokenizer: 50256

- Total trainable params: 124,439,808

- Epochs: 40

- Number of training steps: 40772228

- training_precision: float32 ### Training results

perplexity score: 2.86. ### Framework versions

- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.10.0
- Tokenizers 0.13.2

If you find this model helpful, please cite.
passage: TAGS
#transformers #pytorch #tf #gpt2 #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# text_generation_bangla_model
BanglaCLM dataset: 

- OSCAR: 12.84GB

- Wikipedia dump: 6.24GB

- ProthomAlo: 3.92GB

- Kalerkantho: 3.24GB## Model description

- context size : 128## Training and evaluation data
The BanglaCLM data set is divided into a training set (90%)and a validation set (10%).## Training procedure### Training hyperparameters

The following hyperparameters were used during training:

- Batch size: 32

- Initial learning rate: 5e-5

- Number of warmup steps: 10000

- Weight decay rate: 0.01

- Tokenization algorithm: BPE

- Vocabulary size of tokenizer: 50256

- Total trainable params: 124,439,808

- Epochs: 40

- Number of training steps: 40772228

- training_precision: float32### Training results

perplexity score: 2.86.### Framework versions

- Transformers 4.26.1
- TensorFlow 2.11.0
- Datasets 2.10.0
- Tokenizers 0.13.2

If you find this model helpful, please cite.