Metadata-Version: 2.1
Name: PSTransformer
Version: 2.1.0
Summary: This is Ai Transformer
Author: ProgramerSalar
Author-email: <manishkumar60708090@gmail.com>
Keywords: python,transformer,chat-GPT Transformer
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Description-Content-Type: text/markdown
Requires-Dist: torch
Requires-Dist: tqdm
Requires-Dist: pathlib
Requires-Dist: datasets
Requires-Dist: tokenizers
Requires-Dist: tensorboard


# This is Transformer 









- if you are import the model of the Transformer then used to this import 

```

# import the transformer model 

>>> from PSTansformer.model import build_transformer



# how to used this transformer model 

>>> build_transformer(

        vocab_src_len=vocabulary_source_length,   # vocabulary source length of sentence like tokeinzer source length of 

        vocab_tgt_len=vocabulary_target_length,    # same for the target language 

        src_seq_len=config["seq_len"],     # source language  length of you sentence like 350 

        tgt_seq_len=config['seq_len'],     # target language length of you sentence same as source length

        d_model=config['d_model']        # dimension model your language like 512

)

```









- if you import the Tensor dataset function, which is convert the tensor data from raw data 

```

# import the Tensor dataset Function

>>> from PSTansformer.dataset import BilingualDataset



# how to used this Tensor dataset which is convert to the Tensor of the row data 

>>> BilingualDataset(

        ds=train_dataset_raw,   # raw dataset like='Ram eats mango'

        tokenizer_src=tokenizer_source,  # source language tokenizer 

        tokenizer_tgt=tokinzer_target,   # target language tokenizer

        src_lang=config['lang_src'],      # source language like engish

        tgt_lang=config['lang_tgt'],     # target language like Hindi

        seq_len=config['seq_len'])      # sequence length like 350

```



- how to used the train model 

```

def get_config():

    return {

        "batch_size": 8,

        "num_epochs": 20,

        "lr": 10**-4,

        "seq_len": 350,

        "d_model": 512,

        "datasource": 'opus_books',

        "lang_src": "en",

        "lang_tgt": "it",

        "model_folder": "weights",

        "model_basename": "tmodel_",

        "preload": "latest",

        "tokenizer_file": "tokenizer_{0}.json",

        "experiment_name": "runs/tmodel"

    }

```



```

if __name__ == '__main__':

    warnings.filterwarnings("ignore")

    config = get_config()

    train_model(config)

```

