PSTransformer 2.2.0

This is Ai Transformer

This is Transformer

• Sentence translation transformers are advanced models in natural language processing (NLP) that encode entire sentences into high-dimensional vectors, preserving their contextual meaning. These models, such as BERT, RoBERTa, and XLM-RoBERTa, are fine-tuned to generate embeddings that can be used for various tasks like semantic search, clustering, and sentence similarity³. By training on parallel sentences in multiple languages, these transformers can align the vector spaces of different languages, enabling accurate and meaningful translations¹². This approach significantly enhances the performance of NLP applications, making them more effective in multilingual contexts³.

EXample:

Ram Eats mango    --> English 



 राम आम खाता  है    --> Hindi

• How to Install this

pip install PSTransformer==<version>



            OR



pip install PSTransformer

• 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"

    }

    config = get_config()

    train_model(config)

Licence

MIT Licence

Dependencies

• torch

• tqdm this is progress bar library

• datasets this is dataset libarary by huggingface

• tokenizers this is tokenizers libary by huggingface

• tensorboard TensorBoard is a visualization toolkit for machine learning experimentation. TensorBoard allows tracking and visualizing metrics such as loss and accuracy, visualizing the model graph, viewing histograms, displaying images and much more. In this tutorial we are going to cover TensorBoard installation, basic usage with PyTorch, and how to visualize data you logged in TensorBoard UI.

Uninstall

Uninstall package and dependent package with pip command .

pip uninstall PSTransformer torch tqdm datasets tokenizers tensorboard

Contibuting

See contribution guidelines .

CHANGELOG

1.0.0

• First Implemention version

  • Add a function

    • BilingualDataset()

    • build_transformer()

2.0.0

• solve some Error, I will Get the package error

2.1.0

• First Stable relased version

2.2.0

• Improve document

  • Update the README document

  • Add the Licence

  • some Required Things