Metadata-Version: 2.1
Name: simpletransformers
Version: 0.1.1
Summary: A wrapper for the Transformers library by Hugging Face.
Home-page: https://test.pypi.org/legacy/
Author: Thilina Rajapakse
Author-email: chaturangarajapakshe@gmail.com
License: UNKNOWN
Description: # Pytorch-Transformers-Classification
        
        
        This library is based on the [Pytorch-Transformers](https://github.com/huggingface/pytorch-transformers) library by HuggingFace. Using this library, you can quickly train and evaluate Transformer models.
        
        Please refer to this [Medium article](https://medium.com/p/https-medium-com-chaturangarajapakshe-text-classification-with-transformer-models-d370944b50ca?source=email-6b1e2355088e--writer.postDistributed&sk=f21ffeb66c03a9804572d7063f57c04e) for further information on how this project works.
        
        Please note that the documentation is still being written.
        
        Table of contents
        =================
        
        <!--ts-->
           * [Setup](#Setup)
              * [With Conda](#with-conda)
           * [Usage](#usage)
              * [Minimal Start](#minimal-start)
              * [Current Pretrained Models](#current-pretrained-models)
           * [Acknowledgements](#acknowledgements)
        <!--te-->
        
        ## Setup
        
        ### With Conda
        
        1. Install Anaconda or Miniconda Package Manager from [here](https://www.anaconda.com/distribution/)
        2. Create a new virtual environment and install packages.  
        `conda create -n transformers python pandas tqdm jupyter`  
        `conda activate transformers`  
        If using cuda:  
          `conda install pytorch cudatoolkit=10.0 -c pytorch`  
        else:  
          `conda install pytorch cpuonly -c pytorch`  
        `conda install -c anaconda scipy`  
        `conda install -c anaconda scikit-learn`  
        `pip install transformers`  
        `pip install tensorboardx`  
        
        3. Clone repo.
        `pip install simpletransformers`  
        
        ## Usage
        
        ### Minimal Start
        
        ```
        from simpletransformers.model import TransformerModel
        import pandas as pd
        
        
        # Train and Evaluation data needs to be in a Pandas Dataframe of two columns. The first column is the text with type str, and the second column in the label with type int.
        train_data = [['Example sentence belonging to class 1', 1], ['Example sentence belonging to class 0', 0]]
        train_df = pd.DataFrame(train_data)
        
        eval_data = [['Example eval sentence belonging to class 1', 1], ['Example eval sentence belonging to class 0', 0]]
        eval_df = pd.DataFrame(eval_data)
        
        # Create a TransformerModel
        model = TransformerModel('roberta', 'roberta-base')
        
        # Train the model
        model.train_model(train_df)
        
        # Evaluate the model
        result, model_outputs, wrong_predictions = model.eval_model(eval_df)
        ```
        
        ### Current Pretrained Models
        
        The table below shows the currently available model types and their models. You can use any of these by setting the `model_type` and `model_name` in the `args` dictionary. For more information about pretrained models, see [HuggingFace docs](https://huggingface.co/pytorch-transformers/pretrained_models.html).
        
        | Architecture        | Model Type           | Model Name  | Details  |
        | :------------- |:----------| :-------------| :-----------------------------|
        | BERT      | bert | bert-base-uncased | 12-layer, 768-hidden, 12-heads, 110M parameters.<br>Trained on lower-cased English text. |
        | BERT      | bert | bert-large-uncased | 24-layer, 1024-hidden, 16-heads, 340M parameters.<br>Trained on lower-cased English text. |
        | BERT      | bert | bert-base-cased | 12-layer, 768-hidden, 12-heads, 110M parameters.<br>Trained on cased English text. |
        | BERT      | bert | bert-large-cased | 24-layer, 1024-hidden, 16-heads, 340M parameters.<br>Trained on cased English text. |
        | BERT      | bert | bert-base-multilingual-uncased | (Original, not recommended) 12-layer, 768-hidden, 12-heads, 110M parameters. <br>Trained on lower-cased text in the top 102 languages with the largest Wikipedias |
        | BERT      | bert | bert-base-multilingual-cased | (New, recommended) 12-layer, 768-hidden, 12-heads, 110M parameters.<br>Trained on cased text in the top 104 languages with the largest Wikipedias |
        | BERT      | bert | bert-base-chinese | 12-layer, 768-hidden, 12-heads, 110M parameters. <br>Trained on cased Chinese Simplified and Traditional text. |
        | BERT      | bert | bert-base-german-cased | 12-layer, 768-hidden, 12-heads, 110M parameters. <br>Trained on cased German text by Deepset.ai |
        | BERT      | bert | bert-large-uncased-whole-word-masking | 24-layer, 1024-hidden, 16-heads, 340M parameters. <br>Trained on lower-cased English text using Whole-Word-Masking |
        | BERT      | bert | bert-large-cased-whole-word-masking | 24-layer, 1024-hidden, 16-heads, 340M parameters. <br>Trained on cased English text using Whole-Word-Masking |
        | BERT      | bert | bert-large-uncased-whole-word-masking-finetuned-squad | 24-layer, 1024-hidden, 16-heads, 340M parameters. <br>The bert-large-uncased-whole-word-masking model fine-tuned on SQuAD |
        | BERT      | bert | bert-large-cased-whole-word-masking-finetuned-squad | 24-layer, 1024-hidden, 16-heads, 340M parameters <br>The bert-large-cased-whole-word-masking model fine-tuned on SQuAD |
        | BERT      | bert | bert-base-cased-finetuned-mrpc | 12-layer, 768-hidden, 12-heads, 110M parameters. <br>The bert-base-cased model fine-tuned on MRPC |
        | XLNet      | xlnet | xlnet-base-cased | 12-layer, 768-hidden, 12-heads, 110M parameters. <br>XLNet English model |
        | XLNet      | xlnet | xlnet-large-cased | 24-layer, 1024-hidden, 16-heads, 340M parameters. <br>XLNet Large English model |
        | XLM      | xlm | xlm-mlm-en-2048 | 12-layer, 2048-hidden, 16-heads <br>XLM English model |
        | XLM      | xlm | xlm-mlm-ende-1024 | 6-layer, 1024-hidden, 8-heads <br>XLM English-German Multi-language model |
        | XLM      | xlm | xlm-mlm-enfr-1024 | 6-layer, 1024-hidden, 8-heads <br>XLM English-French Multi-language model |
        | XLM      | xlm | xlm-mlm-enro-1024 | 6-layer, 1024-hidden, 8-heads <br>XLM English-Romanian Multi-language model |
        | XLM      | xlm | xlm-mlm-xnli15-1024 | 12-layer, 1024-hidden, 8-heads <br>XLM Model pre-trained with MLM on the 15 XNLI languages |
        | XLM      | xlm | xlm-mlm-tlm-xnli15-1024 | 12-layer, 1024-hidden, 8-heads <br>XLM Model pre-trained with MLM + TLM on the 15 XNLI languages |
        | XLM      | xlm | xlm-clm-enfr-1024 | 12-layer, 1024-hidden, 8-heads <br>XLM English model trained with CLM (Causal Language Modeling) |
        | XLM      | xlm | xlm-clm-ende-1024 | 6-layer, 1024-hidden, 8-heads <br>XLM English-German Multi-language model trained with CLM (Causal Language Modeling) |
        | RoBERTa      | roberta | roberta-base | 125M parameters <br>RoBERTa using the BERT-base architecture |
        | RoBERTa      | roberta | roberta-large | 24-layer, 1024-hidden, 16-heads, 355M parameters <br>RoBERTa using the BERT-large architecture |
        | RoBERTa      | roberta | roberta-large-mnli | 24-layer, 1024-hidden, 16-heads, 355M parameters <br>roberta-large fine-tuned on MNLI. |
        
        ## Acknowledgements
        
        None of this would have been possible without the hard work by the HuggingFace team in developing the [Pytorch-Transformers](https://github.com/huggingface/pytorch-transformers) library.
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.6
Description-Content-Type: text/markdown
